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Abstract This research explores the feasibility of using a nanocomposite from multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) for thermal engineering applications. The hybrid nanocomposites were suspended in water at various volumetric concentrations. Their heat transfer and pressure drop characteristics were analyzed using computational fluid dynamics and artificial neural network models. The study examined flow regimes with Reynolds numbers between 5000 and 17,000, inlet fluid temperatures ranging from 293.15 to 333.15 K, and concentrations from 0.01 to 0.2% by volume. The numerical results were validated against empirical correlations for heat transfer coefficient and pressure drop, showing an acceptable average error. The findings revealed that the heat transfer coefficient and pressure drop increased significantly with higher inlet temperatures and concentrations, achieving approximately 45.22% and 452.90%, respectively. These results suggested that MWCNTs-GNPs nanocomposites hold promise for enhancing the performance of thermal systems, offering a potential pathway for developing and optimizing advanced thermal engineering solutions.

Abstract Electrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In the current research, the EC of two Australian rivers (Albert River and Barratta Creek) was forecasted for up to 10 days using a novel deep learning algorithm (Convolutional Neural Network combined with Long Short-Term Memory Model, CNN-LSTM). The Boruta-XGBoost feature selection method was used to determine the significant inputs (time series lagged data) to the model. To compare the performance of Boruta-XGB-CNN-LSTM models, three machine learning approaches—multi-layer perceptron neural network (MLP), K-nearest neighbour (KNN), and extreme gradient boosting (XGBoost) were used. Different statistical metrics, such as correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error, were used to assess the models' performance. From 10 years of data in both rivers, 7 years (2012–2018) were used as a training set, and 3 years (2019–2021) were used for testing the models. Application of the Boruta-XGB-CNN-LSTM model in forecasting one day ahead of EC showed that in both stations, Boruta-XGB-CNN-LSTM can forecast the EC parameter better than other machine learning models for the test dataset (R = 0.9429, RMSE = 45.6896, MAPE = 5.9749 for Albert River, and R = 0.9215, RMSE = 43.8315, MAPE = 7.6029 for Barratta Creek). Considering the better performance of the Boruta-XGB-CNN-LSTM model in both rivers, this model was used to forecast 3–10 days ahead of EC. The results showed that the Boruta-XGB-CNN-LSTM model is very capable of forecasting the EC for the next 10 days. The results showed that by increasing the forecasting horizon from 3 to 10 days, the performance of the Boruta-XGB-CNN-LSTM model slightly decreased. The results of this study show that the Boruta-XGB-CNN-LSTM model can be used as a good soft computing method for accurately predicting how the EC will change in rivers.

Abstract Prediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variables like rainfall, strength of flow, and sediment supply. Artificial intelligence (AI) approaches have become prevalent in water resource engineering to solve multifaceted problems like sediment load modelling. The present work proposes a robust model incorporating support vector machine with a novel sparrow search algorithm (SVM-SSA) to compute SSL in Tilga, Jenapur, Jaraikela and Gomlai stations in Brahmani river basin, Odisha State, India. Five different scenarios are considered for model development. Performance assessment of developed model is analyzed on basis of mean absolute error (MAE), root mean squared error (RMSE), determination coefficient (R2), and Nash–Sutcliffe efficiency (ENS). The outcomes of SVM-SSA model are compared with three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper optimization algorithm), SVM-BA (Bat algorithm), and benchmark SVM model. The findings revealed that SVM-SSA model successfully estimates SSL with high accuracy for scenario V with sediment (3-month lag) and discharge (current time-step and 3-month lag) as input than other alternatives with RMSE = 15.5287, MAE = 15.3926, and ENS = 0.96481. The conventional SVM model performed the worst in SSL prediction. Findings of this investigation tend to claim suitability of employed approach to model SSL in rivers precisely and reliably. The prediction model guarantees the precision of the forecasted outcomes while significantly decreasing the computing time expenditure, and the precision satisfies the demands of realistic engineering applications.

This review was conducted to highlight the most influential factors and specify the trends reducing uncertainty and increasing the accuracy of soil and water assessment tool (SWAT)-based hydrological models. Although the resolution of input data on the results of SWAT-based hydrological models has been extensively determined. There is still a gap in providing comprehensive review framework to be emerged for identifying the impact of the data resolution and accuracy. The factors taken into consideration in this study were the impact of digital elevation model (DEM) resolution, soil data resolution, land use and land cover (LULC) resolution, and the impact of weather data resolution. Identifying the best DEM resolution depends on the watershed response and hydrological processes. However, for sediment yield estimation, more attention should be paid to the accuracy of soil data. Furthermore, the impact of LULC resolution on the accuracy of streamflow is still not sufficiently understood, whereas fine resolution is required for an accurate simulation of the sediment yield. Sub-daily precipitation data is essential for an accurate estimation of streamflow. Despite the fact that climate forecast system reanalysis (CFSR) and tropical rainfall measuring mission (TRMM) are the most widely used climate products, climate hazards group infrared precipitation with station data (CHIRPS) produces an adequate estimation for streamflow when there is insufficient gauged data. However, other aspects have not been deeply taken into consideration, including the interactive and complementary impacts of these factors. Thus, more attention and focus should be given to these issues. This review and evaluation can be a significant guide for selecting the suitable input data to implement efficient SWAT-based watershed models.

Abstract Among several hydrological processes, river flow is an essential parameter that is vital for different water resources engineering activities. Although several methodologies have been adopted over the literature for modeling river flow, the limitation still exists in modeling the river flow time series curve. In this research, a functional quantile autoregressive of order one model was developed to characterize the entire conditional distribution of the river flow time series curve. Based on the functional principal component analysis, the regression parameter function was estimated using a multivariate quantile regression framework. For this purpose, hourly scale river flow collected from three rivers in Australia (Mary River, Lockyer Valley, and Albert River) were used to evaluate the finite‐sample performance of the proposed methodology. A series of Monte‐Carlo experiments and historical data sets were examined at three stations. Further, uncertainty analysis was adopted for the methodology evaluation. Compared with the existing methods, the proposed model provides more robust forecasts for outlying observations, non‐Gaussian and heavy‐tailed error distribution, and heteroskedasticity. Also, the proposed model has the merit of predicting the intervals of future realizations of river flow time series at the central and non‐central locations. The results confirmed the potential for predicting the river flow time series curve with a high level of accuracy in comparison with the benchmark existing functional time series methods.

Abstract Sugarcane is the main sugar crop, and sugar is an important agricultural product in Egypt. There are many problems with the technology used in the current planting method of sugarcane, which has a great impact on the planting quality of sugarcane, which have a series of problems, such as low cutting efficiency and poor quality. Therefore, the aim of the current study was to design, construct, and field testing of a semiautomatic sugarcane bud chipper assisted with pivot knives for cutting sugarcane buds and germinating them in plastic trays inside a greenhouse until they reached an average length of 35 cm, and then planting them in the field. In the field tests five cutting speeds (35, 40, 45, 50, and 56 rpm. (Revolution Per minute), three cutting knives (1.5, 2.0, and 2.5 mm) were used for cutting sugarcane stalks with four different diameters (1.32, 1.82, 2.43, and 2.68 cm). The obtained results showed that the values of the damage index and invisible losses were within acceptable limits (ranging between − 1.0 and 0.0) for all the variables under the test. Still, the lowest damage index and invisible losses were recorded with the buds that were cut with a knife of 1.5 mm thickness and cutting speeds less than 50 rpm. The skipping rate increases with the increase in cutting speed and stalk diameter, ranging between 0.0 to 13%. The maximum machine productivity was 110 Buds per minute at a cutting speed of 35 rpm and stalk diameter of 1.32 cm. The paper's findings have important application values for promoting the designing and development of sugarcane bud chipper and sugarcane planting technology in the future.

Global climate change and landform alteration are correlated with a high impact on rainfall, land surface temperature (LST), vegetation conditions, and soil moisture. This study examines rainfall, temperature, vegetation condition, moisture, and drought index to identify the decadal change from 2002 to 2021 using Landsat and global Standardized Precipitation Evapotranspiration Index-base (SPEIbase) data in Google Earth Engine (GEE). In between 20 years, 50.54 mm of mean rainfall decreased in the study area while 6.31 °C of LST increased in the entire region. The most affected states are Bihar, Jharkhand, and West Bengal. Similarly, in northeast India, Assam, Tripura, and Meghalaya have huge effects on temperature variation. The green space (0.158), and moisture (−0.082) fluctuated mainly in Mizoram, Arunachal Pradesh, Manipur, and Tripura states. The SPEI has increased (−0.232) in Bihar, Manipur, Tripura, Assam, and Meghalaya. The analysis observed that decreased rainfall and high-temperature variation may impact crop production, moisture loss, cyclonic affected in West Bengal coastal areas, floods in the Brahmaputra River basin and Bihar, droughts, and urban heat island-related effects. Some policies like reduction of forest fire (northeast), flood (Assam), urban planning (West Bengal, Bihar, and Assam), industrial pollution, and jhum cultivation decrease can improve the climate change in those states, India.

1D and 2D carbon nanomaterials such as multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) were investigated numerically. The thermophysical properties of water and nanofluids using MWCNTs in different outer diameters (ODs) and GNPs in different surface areas (SSA) were measured at an inlet temperature of 303.15 K and 0.1wt.%. The 3D geometry was solved under a fully developed turbulent flow of 6000 ≤ Re ≤ 16,000 using the model of k-ω SST via (ANSYS FLUENT 2022R2) software. Four numerical networks, Polyhedra, Polyhexacore, Hexacore, and Tetrahedral, were optimized. Moreover, seven parameters were discussed, namely wall surface temperature (Tw), heat transfer coefficient (htc), average Nusselt number (Nuavg), friction factor (f), pressure drop (ΔP), and total thermal performance index (PIth). Polyhexacore was the main grid over Polyhedra, Hexacore, and Tetrahedral with the average error (Dittus-Boelter: 2.754%, Gnielinski: 2.343%, Blasius: 1.441%, and Petukhov: 0.640%). Heat transfer increased by 18.38% with GNPs-300, 22.05% with GNPs-500, 23.25% with GNPs-750, 13.63% with CNT < 8 nm, and 11.42% with CNT 20–30 nm, relative to H2O at Re = 16,000. Pressure drop increased by about 42.01% with GNPs-300, 45.16% with GNPs-500, 44.84% with GNPs-750, 36.72% with CNT < 8 nm, and 34.39% with CNT 20-30 nm.

In current work, the unsteady stagnation point’s flow on a special third-grade fuzzy hybrid [Formula: see text] nanofluid (HNF) through a permeable convective shrinking/stretching sheet has been scrutinized. In addition, the adverse consequences of heat source, viscous dissipation, nonlinear thermal radiation, and fuzzy nanoparticle volume fraction are likewise taken into consideration. Non-linear coupled partial differential equations (PDEs) get transformed into ordinary differential equations (ODEs) using an effective similarity transformation. After that, the ODEs are numerically solved using the bvp4c algorithm. Regarding validation, the present results align with earlier published research. The effects of heat distribution, flow rate, Nusselt number, and skin friction coefficient on hybrid nanofluid dynamics are explored using graphical and tabular forms. The nanoparticle volume fraction is considered a triangular fuzzy number (TFN) [0, 5%, 10%]. With the use of TFNs, ODEs are transformed into fuzzy differential equations (FDEs). The TFNs are controlled using a widely used [Formula: see text] technique and [Formula: see text] which requires minimal computational effort to examine their dynamical performance. Also, the comparison of [Formula: see text] [Formula: see text] and [Formula: see text] through the fuzzy membership functions (MFs). The fuzzy MFs show that the hybrid nanofluid ([Formula: see text]) in terms of rate of heat transfer is better than both [Formula: see text] and [Formula: see text] nanofluids.

In this study, two deep learning approaches, bidirectional long short-term memory (BiLSTM) and long short-term memory (LSTM), were used along with adaptive boosting and general regression neural network to forecast multi-step-ahead pan evaporation in two arid climate stations in Iran (Ahvaz and Yazd). Lagged time series of meteorological data and pan evaporation data were input to the machine learning models. Two feature selection methods, i.e., the Boruta extra tree and XGBoost, were used to select significant inputs to reduce the number of inputs and model complexity. Different statistical metrics were used to investigate the model performance. The results demonstrated that Boruta-extra-tree-based models were more accurate than XGBoost-based models. Compared with the machine learning techniques, the combination of Boruta extra tree and BiLSTM enabled more accurate one-day-ahead forecasting of pan evaporation for both sites (Root Mean Square Error (RMSE) = 1.6857, for the Ahvaz station, and RMSE = 1.3996 for the Yazd station). The proposed model was used to forecast up to 30 days ahead of pan evaporation in both stations. The results showed that the Boruta-BiLSTM model could accurately forecast the pan evaporation for up to 30 days in both stations.

Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020–2059) and the distant future (2060–2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.

Climate change and flooding are related issues on the Earth's surface, while numerous lowland areas, especially delta regions, are mostly affected by flood hazards. Hence, flood susceptibility mapping and the simulation of future effect areas are essential for hazard management and awareness. The river floodplain areas of the Ganga River in the Bihar state are the most affected due to high annual floods. Floods cause huge economic losses and environmental degradation, such as deforestation, riverbank erosion, and water quality loss. Thus, flood vulnerability measurement is a serious concern in this area, which involves building proper awareness and mitigation strategies to achieve sustainable development goals. Remote Sensing (RS) is widely applied to such hydrological issues. Hence, statistical approaches, such as the Analytical Hierarchy Process (AHP), Frequency Ratio (FR), and Fuzzy-AHP (FAHP) algorithms, were applied for flood susceptibility analysis in the selected flood plain of the Ganga River in the Bihar state. The most suitable flood susceptibility areas of three different approaches were 9604.21 km2 (AHP), 9712.48 km2 (FAHP) and 9598.28 km2 (FR), while river and channel areas were selected as not suitable area. The flooded maps indicated the flooded lands using Google Earth Engine (GEE) of different years are 2977.69 km2 (2020), 10481.63 km2 (2021), and 1103.89 km2 (2022), respectively. The results of the current study indicate that this area is essentially in need of serious attention for future hazard management and adaptation strategies for the reduction of flooded areas, in addition to the reduction of socio-economic variability in monsoon regions. Otherwise, floods destroyed cropland, increased food scarcity, and caused huge socio-economic losses.

A polymeric blend combines two or more polymers to form a new material with different physical properties. In this work, a base material consisting of a polymeric mixture was manufactured to improve its properties and then strengthened with nanoparticles (ZrO2-ZnO) to develop a hybrid nanocomposite material, which has better properties than its constituent materials. Reinforcement material, i.e., (ZrO2-ZnO) nanoparticles, were prepared using relaxation method. A polymeric resin mixture (lamination and methyl vinyl silicone) was prepared by adding methyl vinyl silicone to the lamination resin in different ratios (4 %, 8 %, 12 %, and 16 %). The mixture properties were studied through tensile, bending, shock, and hardness tests, and the optimal results were achieved for the 12 % ratio. The resulting composite nanoparticles and their properties were studied using EDX, X-Ray, SEM, and PSA techniques. Finally, the nano-hybrid composite material was manufactured by choosing the optimal blend (i.e., 12 %). It had the highest polymeric base material properties, and nanoparticles were added at different dosages (3 %, 6 %, 9 %, and 12 %). The resulting hybrid composite material properties were studied through different tests (tensile, flexural, impact, and hardness). The results showed that the binary composite nanoparticles improved the properties of the mixture for both sizes (30 nm and 89 nm) at all mixing ratios, compared to the control specimens (i.e., without any addition). The optimal results were obtained when 30 nm particles were added and for all tests compared to samples reinforced with 89 nm particles. The optimal ratio of (ZrO2-ZnO) was 9 % wt 30 nm size, representing the best sample in terms of the resulting properties. It is recommended to use the sample with the 9 % addition of (ZrO2 - ZnO) wt with a granular size of (30 nm) in essential applications, including prosthetics (foot).

Abstract According to recent reports, planar structure-based organometallic perovskite solar cells (OPSCs) have achieved remarkable power conversion efficiency (PCE), making them very competitive with the more traditional silicon photovoltaics. A complete understanding of OPSCs and their individual parts is still necessary for further enhancement in PCE. In this work, indium sulfide (In2S3)-based planar heterojunction OPSCs were proposed and simulated with the SCAPS (a Solar Cell Capacitance Simulator)-1D programme. Initially, OPSC performance was calibrated with the experimentally fabricated architecture (FTO/In2S3/MAPbI3/Spiro-OMeTAD/Au) to evaluate the optimum parameters of each layer. The numerical calculations showed a significant dependence of PCE on the thickness and defect density of the MAPbI3 absorber material. The results showed that as the perovskite layer thickness increased, the PCE improved gradually but subsequently reached a maximum at thicknesses greater than 500 nm. Moreover, parameters involving the series resistance as well as the shunt resistance were recognized to affect the performance of the OPSC. Most importantly, a champion PCE of over 20% was yielded under the optimistic simulation conditions. Overall, the OPSC performed better between 20 and 30 °C, and its efficiency rapidly decreases above that temperature.

Abstract Climatic condition is triggering human health emergencies and earth’s surface changes. Anthropogenic activities, such as built-up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and triggering the earth’s health. Nitrogen Dioxide (NO2), Carbon Monoxide (CO), and Aerosol Optical Depth (AOD) are truthfully important for air quality measurement because those air pollutants are more harmful to the environment and human’s health. Earth observational Sentinel-5P is applied for monitoring the air pollutant and chemical conditions in the atmosphere from 2018 to 2021. The cloud computing-based Google Earth Engine (GEE) platform is applied for monitoring those air pollutants and chemical components in the atmosphere. The NO2 variation indicates high during the time because of the anthropogenic activities. Carbon Monoxide (CO) is also located high between two 1-month different maps. The 2020 and 2021 results indicate AQI change is high where 2018 and 2019 indicates low AQI throughout the year. The Kolkata have seven AQI monitoring station where high nitrogen dioxide recorded 102 (2018), 48 (2019), 26 (2020) and 98 (2021), where Delhi AQI stations recorded 99 (2018), 49 (2019), 37 (2020), and 107 (2021). Delhi, Kolkata, Mumbai, Pune, and Chennai recorded huge fluctuations of air pollutants during the study periods, where ~ 50–60% NO2 was recorded as high in the recent time. The AOD was noticed high in Uttar Pradesh in 2020. These results indicate that air pollutant investigation is much necessary for future planning and management otherwise; our planet earth is mostly affected by the anthropogenic and climatic conditions where maybe life does not exist.

Abstract This research aims to attain the optimal method of removing the high salinity concentrations without its effect on the balance or accuracy of stable isotopes measurement of deuterium and oxygen-18 (δ18O, δ2H). Four treatment methods (i.e., distillation, vacuum distillation, electro dialysis and ion exchange) were applied for nine samples, which were obtained from different water sources (sea, groundwater, river).l Worth to notice that the samples have Electrical Conductivity (EC) ranged (1000–60,000 µs/cm). Liquid–Water Isotope Analyzer used to measure the isotope concentration of δ18O, δ2H. The research findings of the four applied methods revealed their effectiveness with various percentages (normal distillation: 92.37%; vacuum distillation: 88.31%; electro dialysis: 94.85%; ion exchange: 99.62%). In addition, the investigation was conducted a clear correspondence measurement of (δ18O, δ2H) isotopes before and after treatment. The four methods results indicated that samples with EC ranged (1000–5000 µs/cm) have no effect on stable isotope readings. Whereas, samples with EC higher than 10,000, have substantial influence on the stable isotope readings. Finally, vacuum distillation method attained the best results among the treatment methods for EC ranged (10,000–60,000 µs/cm) without affecting the isotopic content of (δ18O, δ2H). There is a clear correspondence of the stable isotopic measurements before and after treatment, for all the selected samples.

This study attempts to characterize and interpret the groundwater quality (GWQ) using a GIS environment and multivariate statistical approach (MSA) for the Jakham River Basin (JRB) in Southern Rajasthan. In this paper, analysis of various statistical indicators such as the Water Quality Index (WQI) and multivariate statistical methods, i.e., principal component analysis and correspondence analysis (PCA and CA), were implemented on the pre and post-monsoon water quality datasets. All these methods help identify the most critical factor in controlling GWQ for potable water. In pre-monsoon (PRM) and post-monsoon (POM) seasons, the computed value of WQI has ranged between 28.28 to 116.74 and from 29.49 to 111.98, respectively. As per the GIS-based WQI findings, 63.42 percent of the groundwater samples during the PRM season and 42.02 percent during the POM were classed as 'good' and could be consumed for drinking. The Principal component analysis (PCA) is a suitable tool for simplification of the evaluation process in water quality analysis. The PCA correlation matrix defines the relation among the water quality parameters, which helps to detect the natural or anthropogenic influence on sub-surface water. The finding of PCA's factor analysis shows the impact of geological and human intervention, as increased levels of EC, TDS, Na+, Cl-, HCO3-, F-, and SO42- on potable water. In this study, hierarchical cluster analysis (HCA) was used to categories the WQ parameters for PRM and POR seasons using the Ward technique. The research outcomes of this study can be used as baseline data for GWQ development activities and protect human health from water-borne diseases in the southern region of Rajasthan.

Drought is a hazardous natural disaster that can negatively affect the environment, water resources, agriculture, and the economy. Precise drought forecasting and trend assessment are essential for water management to reduce the detrimental effects of drought. However, some existing drought modeling techniques have limitations that hinder precise forecasting, necessitating the exploration of suitable approaches. This study examines two forecasting models, Long Short-Term Memory (LSTM) and a hybrid model integrating regularized extreme learning machine and Snake algorithm, to forecast hydrological droughts for one to six months in advance. Using the Multivariate Standardized Streamflow Index (MSSI) computed from 58 years of streamflow data for two drier Malaysian stations, the models forecast droughts and were compared to classical models such as gradient boosting regression and K-nearest model for validation purposes. The RELM-SO model outperformed other models for forecasting one month ahead at station S1, with lower root mean square error (RMSE = 0.1453), mean absolute error (MAE = 0.1164), and a higher Nash-Sutcliffe efficiency index (NSE = 0.9012) and Willmott index (WI = 0.9966). Similarly, at station S2, the hybrid model had lower (RMSE = 0.1211 and MAE = 0.0909), and higher (NSE = 0.8941 and WI = 0.9960), indicating improved accuracy compared to comparable models. Due to significant autocorrelation in the drought data, traditional statistical metrics may be inadequate for selecting the optimal model. Therefore, this study introduced a novel parameter to evaluate the model's effectiveness in accurately capturing the turning points in the data. Accordingly, the hybrid model significantly improved forecast accuracy from 19.32 % to 21.52 % when compared with LSTM. Besides, the reliability analysis showed that the hybrid model was the most accurate for providing long-term forecasts. Additionally, innovative trend analysis, an effective method, was used to analyze hydrological drought trends. The study revealed that October, November, and December experienced higher occurrences of drought than other months. This research advances accurate drought forecasting and trend assessment, providing valuable insights for water management and decision-making in drought-prone regions.

Evapotranspiration is one of agricultural water management's most significant and impactful hydrologic processes. A new multi-decomposition deep learning-based technique is proposed in this study to forecast weekly reference evapotranspiration (ETo) in western coastal regions of Australia (Redcliffe and Gold Coast). The time-varying filter-based empirical mode decomposition (TVF-EMD) technique was used to first break down the original meteorological variables/signals into intrinsic mode decomposition functions (IMFs), which included maximum and minimum temperature, relative humidity, wind speed, and solar radiation. Using a partial autocorrelation function (PACF), the significant lagged values were then calculated from the decomposed sub-sequences (i.e., IMFs). A novel Extra Tree- Boruta feature selection algorithm was used to extract important features from the decomposed IMFs. Four machine learning approaches, including bidirectional recurrent neural network (Bi-RNN), multi-layer perceptron neural network (MLP), random forest (RF), and extreme gradient boosting (XGBoost), were used to forecast weekly evapotranspiration using the TVF-EMD-based decomposed meteorological data. Different statistical metrics were applied to evaluate the model performances. The results showed that the decomposition of the input data by TVF-EMD significantly improved the accuracy compared with the non-decomposed inputs (single models without decomposition). The findings indicate that the TVF-BiRNN model, as presented, achieved the highest level of accuracy in simulating weekly ET0 at both the Redcliffe and Gold Coast stations (Redcliffe: R=0.9281, RMSE=3.8793 mm/week, MAPE = 9.2010%; Gold Coast: R=0.8717, RMSE=4.1169 mm/week, MAPE = 11.5408%). The novel hybrid modeling technique can potentially improve agricultural water management through its ability to generate more accurate ETo estimates weekly. The proposed methodology exhibits potential applicability to various other environmental and hydrological modeling issues.

Sediment with varying sizes is transported through runoff, finding ways into sewerage pipes and deposited at low-velocity sections. As wastewater is high in organic content, biofilm formed on the deposited sediment, cementing the deposition and resulting in difficulties in the removal from pipes. High concentration of oil and grease in domestic wastewater promotes strengthening the cementing process of the deposited sediment. The presence of silt in the sediment distribution promotes physical cohesion, whereas biofilm through Extracellular Polymeric Substances (EPS) provides microbial cohesion. Thus, this study attempts to investigate the incipient sediment motion of sediment mixture with a different fraction of fine sand-silt, presence of biofilm through variation of EPS concentration, 0–0.1% and percentage of oil and grease (O&G) at 0–10%. The analysis showed that physical cohesion is more critical than microbial cohesion, with the presence of O&G posing the least significant on the changes of incipient sediment motion.

Recent technological advances have made it possible to produce particles with nanometer dimensions that are uniformly and steadily suspended in traditional solar liquids and have enhanced the impact of thermo-physical parameters. In this research, a three-dimensional flat plate solar collector was built using a thin flat plate and a single working fluid pipe. The physical model was solved computationally under conditions of conjugated laminar forced convection in the range 500 ≤ Re ≤ 1900 and a heat flux of 1000 W/m2. Distilled water (DW) and different types of hybrid nanofluids (namely, 0.1%-Al2O3@Cu/DW, 0.1%-MWCNTs@Fe3O4/DW, 0.3%-MWCNTs@Fe3O4/DW, 0.5%-Ag@MgO/DW, 1%-Ag@MgO/DW, 1%-S1 and 1%-S2, where MWCNTs are multi-wall carbon nanotubes, S1 means 2CuO–1Cu and S2 means 1CuO–2Cu nanocomposites) were evaluated via a set of parameters. The numerical results revealed that, by increasing the working fluid velocity (the Reynolds number), the average heat transfer coefficient, pressure loss, heat gain and solar collector efficiency were increased. Meanwhile, outlet fluid temperature and flat plate surface temperature were decreased. At Re = 1900, 1%-S2 and 1%-S1 presented higher thermal performance enhancement by 44.28% and 36.72% relative to DW. Moreover, low thermal performance enhancement of 7.59% and 7.44% were reported by 0.1%-Al2O3@Cu/DW and 0.3%-MWCNTs@Fe3O4/DW, respectively.

The flat-plate solar collector (FPSC) three-dimensional (3D) model was used to numerically evaluate the energy and economic estimates. A laminar flow with 500 ≤ Re ≤ 1900, an inlet temperature of 293 K, and a solar flux of 1000 W/m2 were assumed the operating conditions. Two mono nanofluids, CuO-DW and Cu-DW, were tested with different shapes (Spherical, Cylindrical, Platelets, and Blades) and different volume fractions. Additionally, hybrid nanocomposites from CuO@Cu/DW with different shapes (Spherical, Cylindrical, Platelets and Blades), different mixing ratios (60% + 40%, 50% + 50% and 40% + 60%) and different volume fractions (1 volume%, 2 volume%, 3 volume% and 4 volume%) were compared with mono nanofluids. At 1 volume% and Re = 1900, CuO-Platelets demonstrated the highest pressure drop (33.312 Pa). CuO-Platelets achieved the higher thermal enhancement with (8.761%) at 1 vol.% and Re = 1900. CuO-Platelets reduced the size of the solar collector by 25.60%. Meanwhile, CuO@Cu-Spherical (40:60) needed a larger collector size with 16.69% at 4 vol.% and Re = 1900. CuO-Platelets with 967.61, CuO – Cylindrical with 976.76, Cu Platelets with 983.84, and Cu-Cylindrical with 992.92 presented the lowest total cost. Meanwhile, the total cost of CuO – Cu – Platelets with 60:40, 50:50, and 40:60 was 994.82, 996.18, and 997.70, respectively.

The current study aims to reduce the hot corrosion issues in steam turbines for Al-Mussaib thermal power stations. To gain the aim of the study, many experimental tests were conducted by taking a sample from an existing broken steam turbine blade to identify the alloy composition and preparing samples with exact composition by powder metallurgy method, then using the electro-deposition method to coat the prepared samples by three different coating composite materials consists of TiO2 in different ratios (5, 10 and 15) g/l and 5 g/l SiO2 added to Watt's solution. To verify the efficiency of coating, several tests were conducted (surface roughness, hardness, wear, and oxidation test). The obtained results indicated that increasing the Ni-5%SiO2-TiO2 (5, 10 and 15) g/l caused an increase in the coating thickness, which is compatible with increasing the surface roughness. Also, the sample hardness increased after coating, which returned to increasing TiO2 amount (5, 10 and 15) g/l. However, wear resistance for the samples after coating by selected coating composite and 10 g/l TiO2 amount records the highest reduction in the wear of the sample.

Abstract Aerobic rice cultivation progresses water productivity, and it can save almost 50% of irrigation water compared to lowland rice with the appropriate development of genotypes and management practices. Two field trials were conducted during 2020, and 2021 seasons to determine the validation of different rice varieties under aerobic cultivation based on their plant defense system, physio-morphological traits, stress indices, grain yield, and water productivity. The experiments were designed in a split-plot design with four replications. Two planting methods, transplanting and aerobic cultivation, were denoted as the main plots, and ten rice genotypes were distributed in the subplots. The results revealed that the planting method varied significantly in all measured parameters. The transplanting method with well watering had the highest value of all measured parameters except leaf rolling, membrane stability index, antioxidant, proline, and the number of unfilled grains. EHR1, Giza179 and GZ9399 as well as A22 genotypes a chief more antioxidant defense system that operated under aerobic conditions. Giza179, EHR1, GZ9399, and Giza178 showed high cell membrane stability and subsequently high validation under such conditions, and also showed efficiency in decreasing water consumption and improving water use efficiency. In conclusion, this study proves that Giza179, EHR1, GZ9399, Giza178, and A22 are valid genotypes for aerobic conditions.

The temporal and geographical variety of population dynamics in coral reef fish profoundly affects environmental resources.Parrotfishes are a group of about 90 fish species regarded as a family (Scaridae) or a subfamily (Scarinae) of the wrasses. The Indo-Pacific has the highest species richness in this category, with around 95 species. They are found in coral reefs, rocky coasts, and seagrass beds and can play a significant role in bioerosion. Most parrotfish species are herbivores, feeding mainly on epilithic algae. The development of parrotfishes is complex and accompanied by a series of changes in sex and color (polychromatism). In this review, the biological and ecological studies, the search for seasonal food, and finally, the breeding of parrotfish were shed light and clarified. Also modern tracking methods have been highlighted to monitor the migration of parrot fish. Finally, highlighting the sight of the annual seasonal stampede of parrotfish migration to Farasan Island.

Developments in the transportation field are emerging because of the growing worldwide demand and upgrading requirements. This study measured the transportation development, shortage distance, and decadal land transformation of Kuala Lumpur and Madrid using various remote sensing and GIS approaches. The kernel density estimation (KDE) tool was applied for road and railway density analysis, and hotspot information increased the knowledge about assessable areas. Landsat datasets were used (1991–2021) for land transformation and related analyses. The built-up land increased by 1327.27 and 404.09 km2 in Kuala Lumpur and Madrid, respectively. In the last thirty years, the temperature increased 6.45 °C in Kuala Lumpur and 4.15 °C in Madrid owing to urban expansion and road construction. Chamberi, Retiro, Moratalaz, Salama, Wangsa Maju, Titiwangsa, Bukit Bintang, and Seputeh have very high road densities. KDE measurements showed that the road densities in Kuala Lumpur (4498.34) and Madrid (9099.15) were high in the central parts of the city, and the railway densities were 348.872 and 2197.87, respectively. The observed P values were 0.99 and 0.96 for traffic signals and 0.98 and 0.99 for bus stops, respectively. The information provided by this study can support local planners, administrators, scientists, and researchers in understanding the global transportation issues that require implementation strategies for ensuring sustainable livelihoods.

Forecasting accurately suspended sediment load (SSL) in the basin is one of the most critical issues for river engineering, environment, and water resources management which effectively reduces flood damages. In this study, a new multi-criteria hybrid expert system comprised of empirical wavelet decomposition (EWT) integrated with Encoder-Decoder Bidirectional long short-term memory (EDBi-LSTM), supported by five feature selection (FS) methods was developed for the first time to forecast daily SSL at two study sites (Bamini and Ashti) of Godavari river basin, India. The employed FS schemes are including Boruta-Random forest (BRF), simulated annealing (SA), Relief algorithm, Ridge regression (RR), and Mutual information (MI) where the BRF coupled with EWT and EDBi-LSTM (i.e., EWT-EDBi-LSTM-Boruta) is identified as the main forecasting paradigm. Here the original SSL signals in the monsoon season (2001–2015) as the only input information were considered to forecast SSL events at a daily time scale in both study zones. The SSL signals were decomposed using the EWT technique considering the significant antecedent time-lagged inputs based on partial auto-correlation function (PACF). In the next stage, five FS strategies were addressed to specify the significant sub-sequences to reduce computational cost and enhance forecasting accuracy. Besides, the extreme gradient boosting (XGB) approach was implemented to compare the potential of the hybrid EDBi-LSTM and standalone counterpart models for both study sites. According to several goodness-of-fit indices and validation tools, the outcomes at the Bamini and Ashti sites demonstrated that the EWT-EDBi-LSTM-Boruta as the main model, achieved the best accuracy, followed by EWT-XGB-Boruta, EWT-EDBi-LSTM-SA, and EWT-XGB-SA, respectively. Comparing all the hybrid models showed that the BRF, SA, and RR strategies performed better in integration with machine learning (ML) models.

Total phosphorus (T-P) refers to the concentration of phosphorus in water and is one of the important parameters for eutrophication in lakes and rivers. In current research, neuroscience dependent (i.e., singular and double-platform synthetic) approaches were employed to predict the river T-P concentration. Singular techniques were developed utilizing machine learning based (support vector machines (SVMs), stochastic gradient boosting (SGB)), and deep learning based multilayer perceptron (DMLP) models. Besides, double-platform synthetic techniques were developed by integrating a prior data-processing (Discrete-wavelet) algorithm with the singular techniques. Six input scenarios conditional on different water quantity and quality parameters acquired from two stations (Hwangji and Toilchun), South Korea, were employed for appraising singular and double-platform synthetic techniques. The various promoted models were evaluated using four statistical standards viz., mean absolute error (MAE), root mean square error (RMSE), scatter index (SI), and Pearson correlation coefficient (CCp), and four visual references viz., scatter diagram, box-and-whisker plot, violin plot, and Taylor diagram. It can be indicated from the outcomes that the double-platform synthetic techniques did not always lead to more accurate predictions than the singular techniques. Further, results also supplied that the SGB with the 6th input (MAE = 0.012 mg/L, RMSE = 0.014 mg/L, and CCp = 0.650 for Hwangji; MAE = 0.011 mg/L, RMSE = 0.017 mg/L, and CCp = 0.963 for Toilchun) scenario model demonstrated the best accuracy for predicting river T-P concentration by the singular techniques for both stations, whereas the discrete-wavelet SVMs with the 4th input (MAE = 0.007 mg/L, RMSE = 0.011 mg/L, and CCp = 0.765 for Hwangji) and discrete-wavelet SGB with the 5th input (MAE = 0.012 mg/L, RMSE = 0.017 mg/L, and CCp = 0.953 for Toilchun) scenario models provided the best predictive accuracy among double-platform synthetic techniques, respectively.

Abstract Fiber reinforced polymer (FPR) bars have been widely used as a substitutional material of steel reinforcement in reinforced concrete elements in corrosion areas. Shear resistance of FRP reinforced concrete element can be affected by concrete properties and transverse FRP stirrups. Hence, studying the shear strength (V s ) mechanism is one of the highly essential for pre-design procedure for reinforced concrete elements. This research examines the ability of three machine learning (ML) models called M5-Tree (M5), extreme learning machine (ELM), and random forest (RF) in predicting V s of 112 shear tests of FRP reinforced concrete beam with transverse reinforcement. For generating the prediction matrix of the developed ML models, statistical correlation analysis was conducted to generate the suitable inputs models for V s prediction. Statistical evaluation and graphical approaches were used to evaluate the efficiency of the proposed models. The results revealed that all the proposed models performed in general well for all the input combinations. However, ELM-M1 and M5-Tree-M5 models exhibited less accuracy performance in comparison with the other developed models. The study showed that the best prediction performance was revealed by M5 tree model using nine input parameters, with coefficient of determination (R2) and root mean square error (RMSE) equal to 0.9313 and 35.5083 KN, respectively. The comparison results also indicated that ELM and RF were performed significant results with a less slight performance than M5 model. The study outcome contributes to basic knowledge of investigating the impact of stirrups on V s of FRP reinforced concrete beam with the potential of applying different computer aid models.

In the present study, two innovative techniques namely, Deep Learning (DL) and Gradient boosting Machine (GBM) models are developed based on a maximum air temperature ‘univariate modeling scheme’ for modeling the monthly pan evaporation (Epan) process. Monthly air temperature and pan evaporation are used to build the predictive models. These models are used for evaluating the evaporation prediction for the Kiashahr meteorological station located in the north of Iran and Ranichauri station positioned in Uttarakhand State of India. Findings indicated that the deep learning model was found best at Kiashahr station for testing datasets MAE (0.5691, mm/month), RMSE (0.7111, mm/month), NSE (0.7496), and IOA (0.9413). It can be concluded that in the semi-arid climate of Iran both of the used methods had the good capability in modeling of monthly Epan. However, DL predicted monthly Epan better than GBM. Moreover, the highest accuracy of the deep learning model was also observed for the Ranichauri station in terms of MAE = 0.3693 mm/month, RMSE = 0.4357 mm/month, NSE = 0.8344, & IOA = 0.9507 in testing stage. Overall, results expose the superior performance of DL-based models for both study stations and can also be utilized for various other environmental modeling.

Abstract Water distribution systems (WDSs) are used to transmit and distribute water resources in cities. Water distribution networks (WDNs) are partitioned into district metered areas (DMAs) by water network partitioning (WNP), which can be used for leak control, pollution monitoring, and pressure optimization in WDS management. In order to overcome the limitations of optimal search range and the decrease of recovery ability caused by two-step WNP and fixed DMAs in previous studies, this study developed a new method combining a graph neural network to realize integrated WNP and dynamic DMAs to optimize WDS management and respond to emergencies. The proposed method was tested in a practical case study; the results showed that good hydraulic performance of the WDN was maintained and that dynamic DMAs demonstrated excellent stability in emergency situations, which proves the effectiveness of the method in WNP.

Abstract Partitioning a water distribution network into several district metered areas is beneficial for its management. Partitioning a network according to its node features and connections remains a challenge. A recent study has realized water network partitioning based on node features or pipe connections individually. This study proposes an unsupervised clustering method for nodes based on a graph neural network, which uses graph attention technology to update node features based on the connections and a neural network to cluster nodes. The similarity between nodes located in each area and the balance of the total water demand between areas are optimized, and the importance of the boundary pipes is calculated to determine the installation position of flowmeters and valves. Three water distribution networks with different structures and sizes are used to verify the proposed model. The results show that the average location differences (LocDiffs) within the areas of the three networks completed by partitioning are 0.12, 0.07, and 0.06, and the total demand differences (DemDiffs) between areas are 0.13, 0.27, and 0.29, respectively. The LocDiff and DemDiff of the proposed method decreased by 6% and 55%, respectively, when compared to the traditional clustering method. Additionally, the proposed method for calculating the importance of boundaries provides an objective basis for boundary closure. When the same number of boundaries are closed, the comprehensive impact of the proposed method on the pipe network decreases by 17.1%. The proposed method can be used in practical applications because it ensures a highly reliable and interpretive water distribution network partitioning method.

Flat plate solar collectors (FPSCs) are the most often used as solar collectors due to their easiness of installation and usage. The current research investigates the energy efficiency of FPSC using different mass concentration with varied base fluids containing Graphene nanofluids (T-Gr). Mass concentration of 0.1%-wt., 0.075%-wt., 0.050%-wt. and 0.025%-wt. were mixed with ethylene glycol (EG) and distilled water (DW) in different rations. The operating conditions were volumetric flowrate (1.5, 1 and 0.5) LPM 50 °C-input fluid temperature and 800 W/m2-global solar irradiation. Scanning electron microscope (SEM) and energy dispersive X-ray (EDX) were used to synthesize the thermally treated nanomaterial. The theoretical investigation indicated that using T-Gr nanosuspensions increased the FPSC efficiency in comparison with the host fluid for all examined mass concentrations and volumetric flowrates. In quantitative terms, the maximum thermal effectiveness improvement for the EG, (DW:70 + EG:30) and DW:EG (DW:50 + EG:50) and using flowrates of (1.5, 1 and 0.5) LPM were 12.54%, 12.46% and 12.48%. In addition, the research results pointed that the essential parameters (i.e., loss energy (FRUL)) and gain energy (FR(τα)) of the T-Gr nanofluids were increased significantly.

As part of city planning and urban studies, it is customary to use the phrase urban density to describe people and other human activity and development throughout a certain area of land. Urban regions in affluent nations are not only more densely populated (DPUA), but they also have a greater population density per unit of land area than rural areas do as well. In densely populated urban areas, public transportation is more plentiful, reducing travel times making them more efficient. Greater affluent residents in metropolitan areas can also benefit from modern high-rise structures, which offer more breathing room. An energy-efficient design and features provide great living standards and comfort while using little energy and releasing minimal carbon dioxide in this sort of housing. In the absence of or rare usage of conventional active and passive heating and cooling systems, Smart sensors and other IoT devices make up the Internet of Things (IoT). IoT presents cities with new potential for using data from sensors in their infrastructure to improve traffic flow and reduce pollution. The more crowded and congested a city is, the more dissatisfied its residents are. There are numerous negatives to urban life, including high living costs, congestion, pollution, crime, and traffic, yet the benefits exceed the risks. Clean up the air by reducing the amount of energy used and using alternate modes of transportation. Provide services like garbage disposal and housing through private–public partnerships. Urban planning should include planting trees and the maintenance of municipal green areas. There are many advantages to living in densely populated places, such as better healthcare, better amenities, and faster emergency response times. The innovative DPUA-IoT approach proposed in this study is only one example of these advantages. Rapid population expansion is harmful to achieving economic and social progress and sustainably managing the natural resource base. However, there is still a large disparity between the interests of the commercial and public sectors when it comes to reducing fertility.

In recent studies, Thermo-Electric Coolers (TEC) have been utilized for dehumidification purposes, which is mainly based on the extraction of moisture from humid atmospheric air. The reviewed literature showed that the rate of water collection from the TEC-based system can be affected by various parameters such as the module’s input voltage, the heat sink orientation, and tilt angles. In this research, the analysis of variance (ANOVA) was used to examine the significance of these factors and their interaction within the system on the TEC-based dehumidification system. Four levels were investigated for both, the Peltier’s input voltage and the rotation angle, and three levels for the tilt angle. This study indicated the significance of the studied factors and their interactions within the dehumidification system along with performing an overall numerical optimization. The experiments were conducted under the same working conditions in an enclosed environment to minimize errors. According to the overall numerical optimization, which was validated experimentally, the optimum system performance was predicted to be obtained at approximately 6.8V Peltier input volt, 65° rotation angle, and 90° tilt angles, with predicted optimum productivities of 0.32278 L/kWh and 13.03 mL/hr. For the same set of parameters, the variation between the experiment and the numerical optimization was less than 4%. The experiments show that when optimizing water collection rates for thermoelectric cooling heat sinks​ under high humidity conditions, the orientation of the heat sink should be considered.

Abstract Covalent and non-covalent nanofluids were tested inside a circular tube fitted with twisted tape inserts with 45° and 90° helix angles. Reynolds number was 7000 ≤ Re ≤ 17,000, and thermophysical properties were assessed at 308 K. The physical model was solved numerically via a two-equation eddy-viscosity model (SST k-omega turbulence). GNPs-SDBS@DW and GNPs-COOH@DW nanofluids with concentrations (0.025 wt.%, 0.05 wt.% and 0.1 wt.%) were considered in this study. The twisted pipes' walls were heated under a constant temperature of 330 K. The current study considered six parameters: outlet temperature, heat transfer coefficient, average Nusselt number, friction factor, pressure loss, and performance evaluation criterion. In both cases (45° and 90° helix angles), GNPs-SDBS@DW nanofluids presented higher thermohydraulic performance than GNPs-COOH@DW and increased by increasing the mass fractions such as 1.17 for 0.025 wt.%, 1.19 for 0.05 wt.% and 1.26 for 0.1 wt.%. Meanwhile, in both cases (45° and 90° helix angles), the value of thermohydraulic performance using GNPs-COOH@DW was 1.02 for 0.025 wt.%, 1.05 for 0.05 wt.% and 1.02 for 0.1 wt.%.

Abstract Bimetals are widely used as a thermal tripping mechanism inside the miniature circuit breakers (MCBs) products when an overload current passes through the circuit for a certain period. Experimental, numerical, and, recently artificial intelligence methods are widely used in designing electric components. However, developing the bimetal for MCB products somewhat differs from developing other conductor items since they are strongly related to the electrical, mechanical, and thermal performance of the MCB. The conventional experimental and numerical approaches are time-consuming processes that cannot be easily utilized in optimizing the product's performance within the development lead time. In this study, a simple, fast, robust, and accurate novel methodology has been introduced to predict the temperature rise of the bimetal and other related performance characteristics. The numerical model has been built on the time-based finite difference method to frame the theoretical thermal model of the bimetal. Then, the numerical model has been consolidated by the machine learning (ML) model to take advantage of the experiments to provide an accurate, fast and reliable model finally. The novel model agrees well with the experimental tests, where the maximum error does not exceed 8%. The model has been used to redesign the bimetal of a 32 A MCB product and significantly reduce the maximum temperature by 24 °C. The novel model is promising since it considerably reduces the required design time, provides accurate predictions, and helps to optimize the performance of the circuit breaker products.

Abstract Coastal vulnerability assessment is the key to coastal management and sustainable development. Sea level rise (SLR) and anthropogenic activities have triggered more extreme climatic events and made the coastal region vulnerable in recent decades. Many parts of the world also noticed increased sediment deposition, tidal effects, and changes in the shoreline. Farasan Island, located in the south-eastern part of Saudi Arabia, experienced changes in sediment deposition from the Red Sea in recent years. This study used Digital Shoreline Analysis System (DSAS) to delineate the shoreline changes of Farasan Island during 1975–2020. Multi-temporal Landsat data and DSAS were used for shoreline calculation based on endpoint rate (EPR) and linear regression. Results revealed an increase in vegetation area on the island by 17.18 km2 during 1975–1989 and then a decrease by 69.85 km2 during 1990–2020. The built-up land increased by 5.69 km2 over the study period to accommodate the population growth. The annual temperature showed an increase at a rate of 0.196 °C/year. The sea-level rise caused a shift in the island's shoreline and caused a reduction of land by 80.86 km2 during 1975–2020. The highly influenced areas by the environmental changes were the north, central, northwest, southwest, and northeast parts of the island. Urban expansion and sea-level rise gradually influence the island ecosystem, which needs proper attention, management, policies, and awareness planning to protect the environment of Farasan Island. Also, the study’s findings could help develop new strategies and plan climate change adaptation.

Extreme climate is one of the important variables which determine the capability of tropical peatland to act as either carbon sink and/or carbon source. The purpose of this study is to reveal the spatio-temporal trend in the long-term time series of extreme rainfall and temperature in Sarawak peatland cause by climate change. Gridded-based Princeton datasets were used for trend analysis spanning 68-year (1948–2016) based on Modified Mann-Kendall (m-MK) test which has the capability of distinguishing unidirectional trend with multi-scale variability. The m-MK test was also used to confirm the increasing or decreasing trend produce by Mann-Kendall (MK), and to discriminate the exaggeration in trend caused by serial auto-correlation due to the high effect of large scale climate events regulating the climate in the region. By using R-based program, RClimDex for extreme climate indices output, extreme climate under Northeast (NE) and Southwest (SW) monsoon showed lower grid point with significant changes under m-MK test compared to MK test at 95% significance level. Here, the exaggeration of trend by MK test has been reduced by using m-MK test which can accommodate the scaling effect in the time series due to inherent natural climate variability. Diurnal temperature range (DTR) was expected to decrease for both monsoons in the central-coastal region as minimum temperature (TN) increased more than maximum temperature (TX). Significant increase in extreme rainfall (R10, R20, Rnn) was spatially observed more during SW monsoon compared to NE monsoon, although with high spatial variability. Significant increase of TN indices of TNn and TN90p might cause increased rainfall intensity in the south and central-coastal region, while high TX indices of TXn might cause increased rainfall intensity in the north. Due to the imminent threat of climate change, this study gives scientists an essential view on the behavior of different extreme climate variables and its potential impact on the peatland area which is susceptible to flood and risk of fire during the NE and SW monsoon, respectively.

This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.

Abstract The rising salinity trend in the country’s coastal groundwater has reached an alarming rate due to unplanned use of groundwater in agriculture and seawater seeping into the underground due to sea-level rise caused by global warming. Therefore, assessing salinity is crucial for the status of safe groundwater in coastal aquifers. In this research, a rigorous hybrid neurocomputing approach comprised of an Adaptive Neuro-Fuzzy Inference System (ANFIS) hybridized with a new meta-heuristic optimization algorithm, namely Aquila optimization (AO) and the Boruta-Random forest feature selection (FS) was developed for estimating the salinity of multi-aquifers in coastal regions of Bangladesh. In this regard, 539 data samples, including ten water quality indices, were collected to provide the predictive model. Moreover, the individual ANFIS, Slime Mould Algorithm (SMA), and Ant Colony Optimization for Continuous Domains (ACOR) coupled with ANFIS (i.e., ANFIS-SMA and ANFIS-ACOR) and LASSO regression (Lasso-Reg) schemes were examined to compare with the primary model. Several goodness-of-fit indices, such as correlation coefficient (R), the root mean squared error (RMSE), and Kling-Gupta efficiency (KGE) were used to validate the robustness of the predictive models. Here, the Boruta-Random Forest (B-RF), as a new robust tree-based FS, was adopted to identify the most significant candidate inputs and effective input combinations to reduce the computational cost and time of the modeling. The outcomes of four selected input combinations ascertained that the ANFIS-OA regarding the best accuracy in terms of (R = 0.9450, RMSE = 1.1253 ppm, and KGE = 0.9146) outperformed the ANFIS-SMA (R = 0.9406, RMSE = 1.1534 ppm, and KGE = 0.8793), ANFIS-ACOR (R = 0.9402, RMSE = 1.1388 ppm, and KGE = 0.8653), Lasso-Reg (R = 0.9358), and ANFIS (R = 0.9306) models. Besides, the first candidate input combination (C1) by three inputs, including Cl− (mg/l), Mg2+ (mg/l), Na+ (mg/l), yielded the best accuracy among all alternatives, implying the role importance of (B-RF) feature selection. Finally, the spatial salinity distribution assessment in the study area ascertained the high predictability potential of the ANFIS-OA hybrid with B-RF feature selection compared to other paradigms. The most important novelty of this research is using a robust framework comprised of the non-linear data filtering technique and a new hybrid neuro-computing approach, which can be considered as a reliable tool to assess water salinity in coastal aquifers.

Since genetic factors alone cannot explain most cases of Autism, the environmental factors are worth investigating as they play an essential role in the development of some cases of Autism. This research is a review paper that aims to clarify the role of the macro elements (MEs), Trace elements (TEs) and ultra-trace elements (UTEs) on human health if they are greater or less than the normal range. Aluminium (Al), cadmium Cd), lead (Pb), chromium (Cr), zinc (Zn), copper (Cu), nickel (Ni), arsenic (As), mercury (Hg), manganese (Mn), and iron (Fe) have been reviewed. Exposure to toxicants has a chemical effect that may ultimately lead to autism spectrum disorder (ASD). The Cr, As and Al are found in high concentrations in the blood of an autistic child when compared to normal child reference values. The toxic metals, particularly aluminium, are primarily responsible for difficulties in socialization and language skills disabilities. Zinc and copper are important elements in regulating the gene expression of metallothioneins (MTs), and zinc deficiency may be a risk factor for ASD pathogenesis. Autistics frequently have zinc deficiency combined with copper excess; as part of the treatment protocol, it is critical to monitor zinc and copper levels in autistic people, particularly those with zinc deficiency. Zinc deficiency is linked to epileptic seizures, which are common in autistic patients. Higher serum manganese and copper significantly characterize people who have ASD. Autistic children have significantly decreased lead and cadmium in urine, whereas they have significantly higher urine Cr. A higher level of As and Hg was found in the ASD individual’s blood.

For turbidity removal, most drinking water treatment plants are using coagulants due to the presence of suspended and colloidal materials at the coagulation and flocculation units. Aluminium and sulphates salts are the widely used coagulants, such as aluminium sulphate (Alum) and ferric chloride. However, several researches have linked Alzheimer's disease to the use of aluminium sulphate. Hence, scholars have conducted several researches on the possibility to reduce the amount of aluminium sulphate by using natural material/plants base as coagulant aids. In this study, Mallow's Leaves Extracts (MLE) and Carob's Pods Extracts (CPE) were used as an alternative coagulant aid. Couples of coagulation tests were implemented to find the optimal dosage of aluminium sulphates used as coagulants. The results displayed that the maximum turbidity removal efficiency by adding 100% of each coagulant, i.e. alum, MLE and CPE, were 61.16, 51.175 and 37.12%, respectively. In addition, the minimum residual turbidity and maximum turbidity removal efficiency were 4.56 NTU and 97.72% by adding 22.5 alum and 7.5 MLE presenting 30 mg/L dosing. Further, the minimum residual turbidity and maximum turbidity removal efficiency were 15.4 NTU and 92.3% by adding 22.5 alum and 7.5 CPE presenting 30 mg/L dosing. HIGHLIGHTS Mallow's Leaves Extracts (MLE) and Carob's Pods Extracts (CPE) were used as an alternative coagulant aid.; Couples of coagulation tests were implemented to find the optimal dosage of aluminium sulphates used as coagulants.; The results revealed that MLE and CPE have considerable flocculation properties.; The minimum residual turbidity was 15.4 NTU by adding 22.5 alum and 7.5 CPE presenting 30 mg/L dosing.;

Abstract Inadequate agricultural planning compounded by inaccurate predictions results in an inflated local market rate and prompts higher importation of wheat. To tackle this problem, this research has designed two-phase universal machine learning (ML) model to predict wheat yield (Wpred), utilizing 27 agricultural counties’ data within the Agro-ecological zone. The universal model, online sequential extreme learning machines coupled with ant colony optimization (ACO-OSELM) is developed, by incorporating the significant annual yield data lagged at (t − 1) as the model’s predictor to generate future yield at 6 test stations. In the first phase, ACO is adopted to search for suitable, statistically relevant data stations for model training, and the corresponding test station by virtue of a feature selection strategy. An annual wheat yield time-series input dataset is constructed utilizing data from each selected training station (1981–2013) and applied against 6 test stations (with each case modelled with 26 station data as the input) to evaluate the hybrid ACO-OSELM model. The partial autocorrelation function is implemented to deduce statistically significant lagged data, and OSELM is applied to generate Wpred. The two-phase hybrid ACO-OSELM model is tested within the 6 agricultural districts (represented as stations) of Punjab province, Pakistan and the results are benchmarked with extreme learning machine (ELM) and random forest (RF) integrated with ACO (i.e., hybrid ACO-ELM and hybrid ACO-RF models, respectively). The performance of the ACO-OSELM model was proven to be good in comparison to ACO-ELM and ACO-RF models. The hybrid ACO-OSELM model revealed its potential to be implemented as a decision-making system for crop yield prediction in areas where a significant association with the historical agricultural crop is well-established.

Abstract Precise prediction of water quality parameters plays a significant role in making an early alert of water pollution and making better decisions for the management of water resources. As one of the influential indicative parameters, electrical conductivity (EC) has a crucial role in calculating the proportion of mineralization. In this study, the integration of an adaptive hybrid of differential evolution and particle swarm optimization (A-DEPSO) with adaptive neuro fuzzy inference system (ANFIS) model is adopted for EC prediction. The A-DEPSO method uses unique mutation and crossover processes to correspondingly boost global and local search mechanisms. It also uses a refreshing operator to prevent the solution from being caught inside the local optimal solutions. This study uses A-DEPSO optimizer for ANFIS training phase to eliminate defects and predict accurately the EC water quality parameter every month at the Maroon River in the southwest of Iran. Accordingly, the recorded dataset originated from the Tange-Takab station from 1980 to 2016 was operated to develop the ANFIS-A-DEPSO model. Besides, the wavelet analysis was jointed to the proposed algorithm in which the original time series of EC was disintegrated into the sub-time series through two mother wavelets to boost the prediction certainty. In the following, the comparison between statistical metrics of the standalone ANFIS, least-square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), generalized regression neural network (GRNN), wavelet-LSSVM (WLSSVM), wavelet-MARS (W-MARS), wavelet-ANFIS (W-ANFIS) and wavelet-GRNN (W-GRNN) models was implemented. As a result, it was apparent that not only was the W-ANFIS-A-DEPSO model able to rise remarkably the EC prediction certainty, but W-ANFIS-A-DEPSO (R = 0.988, RMSE = 53.841, and PI = 0.485) also had the edge over other models with Dmey mother in terms of EC prediction. Moreover, the W-ANFIS-A-DEPSO can improve the RMSE compared to the standalone ANFIS-DEPSO model, accounting for 80%. Hence, this model can create a closer approximation of EC value through W-ANFIS-A-DEPSO model, which is likely to act as a promising procedure to simulate the prediction of EC data.

Engineering Mathematics requires that problem-solving should be implemented through ongoing assessments; hence the prediction of student performance using continuous assessments remains an important task for engineering educators, mainly to monitor and improve their teaching practice. This paper develops probabilistic models to predict weighted scores (WS, or the overall mark leading to a final grade) for face-to-face (on-campus) and web-based (online) Advanced Engineering Mathematics students at an Australian regional university over a 6-year period (2013–2018). We fitted parametric and non-parametric D-vine copula models utilizing multiple quizzes, assignments and examination score results to construct and validate the predicted WS in independently test datasets. The results are interpreted in terms of the probability of whether a student’s continuous performance (i.e., individually or jointly with other counterpart assessments) is likely to lead to a passing grade conditional upon joint performance in students’ quizzes and assignment scores. The results indicate that the newly developed D-vine model, benchmarked against a linear regression model, can generate accurate grade predictions, and particularly handle the problem of low or high scores (tail dependence) compared with a conventional model for both face-to-face, and web-based students. Accordingly, the findings advocate the practical utility of joint copula models that capture the dependence structure in engineering mathematics students’ marks achieved. This therefore, provide insights through learning analytic methods to support an engineering educator’s teaching decisions. The implications are on better supporting engineering mathematics students’ success and retention, developing evidence-based strategies consistent with engineering graduate requirements through improved teaching and learning, and identifying/addressing the risk of failure through early intervention. The proposed methods can guide an engineering educator’s practice by investigating joint influences of engineering problem-solving assessments on their student’s grades.

In the current study, three machine learning (ML) models, i.e. Gaussian process regression (GPR), generalized regression neural network (GRNN), and multigene genetic programming (MGGP), were developed for predicting the discharge coefficient (Cd) of a radial gate under two different flow conditions, i.e. free and submerged. The modeling development of the flow and geometry input variables for the Cd was determined based on statistical correlations. We also performed a sensitivity analysis of the input variables for the Cd. The modeling results indicated that the developed ML models attained acceptable predictable performance; however, the prediction accuracy of the models was better under the free flow condition. In quantitative terms, the minimum root mean square error (RMSE) value was 0.010 using the GPR model and 0.019 using the MGGP model for the submerged and free flow conditions, respectively. The sensitivity analysis evidenced that the ratio of the gate opening height to the depth of water in the upstream (W:Yo) was the influential variable for the Cd under the free flow condition, whereas the ratio of the depth of water in the upstream to the depth of water in downstream (Yo:YT) was the influential variable for the Cd under the submerged flow condition.

Feature selection (FS) is an irreplaceable phase that makes data mining more efficient. It effectively enhances the implementation and decreases the computational problem of learning models. The comprehensive and greedy algorithms are not suitable for the present growing number of features when detecting the optimal subset. Thus, swarm intelligence algorithms (SI) are becoming more common in dealing with FS problems. The grasshopper optimizer algorithm (GOA) represents a new SI; it showed good performance in different fields. Another promising nature-inspired algorithm is a salp swarm algorithm, denoted as SSA, an SI used to tackle optimization issues. In this paper, two phases are applied to propose a new method using crossover-salp swarm with grasshopper optimization algorithm (cSG). In this method, the crossover operators are used to maintain the population of the SSA then the improved SSA is used as a local search to boost the exploration phase of the GOA. Subsequently, this improvement prevents the cSG from premature convergence, high computation time, and being trapped in local minimum. To confirm the effectiveness of proposed cSG method, it is evaluated in different optimizations problems. Eventually, the obtained results are compared to a number of well-known algorithms over global optimization, feature selection datasets, and six real-engineering problems. Experimental results point out that the cSG is superior in solving different optimization problems due to the integration of crossover operators and SSA which enhances its performance and flexibility.

The study highlights the potential characteristics of droughts under future climate change scenarios. For this purpose, the changes in Standardized Precipitation Evapotranspiration Index (SPEI) under the A1B, A2, and B1 climate change scenarios in Iran were assessed. The daily weather data of 30 synoptic stations from 1992 to 2010 were analyzed. The HadCM3 statistical model in the LARS-WG was used to predict the future weather conditions between 2011 and 2112, for three 34-year periods; 2011-2045, 2046-2079, and 2080-2112. In regard to the findings, the upward trend of the potential evapotranspiration in parallel with the downward trend of the precipitation in the next 102 years in three scenarios to the base timescale was transparent. The frequency of the SPEI in the base month indicated that 17.02% of the studied months faced the drought. Considering the scenarios of climate change for three 34-year periods (i.e., 2011-2045, 2046-2079, and 2080-2112) the average percentages of potential drought occurrences for all the stations in the next three periods will be 8.89, 16.58, and 27.27 respectively under the B1 scenario. While the predicted values under the A1B scenario are 7.63, 12.66, and 35.08%respectively. The relevant findings under the A2 scenario are 6.73, 10.16, 40.8%. As a consequence, water shortage would be more serious in the third period of study under all three scenarios. The percentage of drought occurrence in the future years under the A2, B1, and A1B will be 19.23%, 17.74%, and 18.84%, respectively which confirms the worst condition under the A2 scenario. For all stations, the number of months with moderate drought was substantially more than severe and extreme droughts. Considering the A2 scenario as a high emission scenario, the analysis of SPEI frequency illustrated that the proportion of dry periods in regions with humid and cool climate is more than hot and warm climates; however, the duration of dry periods in warmer climates is longer than colder climates. Moreover, the temporal distribution of precipitation and potential evapotranspiration indicated that in a large number of stations, there is a significant difference between them in the middle months of the year, which justifies the importance of prudent water management in warm months.