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One of the procedures that helps remove pharmaceuticals (Phs) from wastewater in Constructed Wetlands (CWs) is phytoremediation. Using Ruellia simplex, the main emphasis of this study is on the function of plants and substrates in the removal mechanism independently from aqueous solution. Even in an aqueous solution containing high concentrations of antibiotics, the plant could thrive, and lighting further promoted growth. For the removal of amoxicillin (AMX) and ciprofloxacin (CIP) at a concentration of 25 mg/L, six reactors are used: hydroponic planted reactors, sand rectors, chicken manure -- wood shavings (CM-WS) rectors, rhizobacterial-inoculated unplanted reactors, unplanted control reactors, and unplanted dark control reactors to study the roles of phytoremediation, adsorption, biodegradation, photodegradation and hydrolysis process in reduction of AMX and CIP. Additionally, the plant's weight is determined for each sample collected and compared to the initial weight of the experiment to monitor plant growth. The possible fates of AMX and CIP were explored using hydroponic tests, whereby 43%, 26% and 19%, respectively, of the total removal of AMX had been accounted for by plant uptake, adsorption by CM-WS and biodegradation after 21 days of exposure, also these process contributed well in CIP removal, where the proportions were 39%, 32% and 18%, respectively of the total CIP reduction at the same intervals. [ABSTRACT FROM AUTHOR]

Wind energy is a valuable renewable resource that plays a significant role in electricity generation process. Predicting wind speed (W.S.) is critical for effectively managing wind energy and producing power. This study proposes an improved multi-layer perceptron (MLP) model for W.S. prediction that incorporates a novel optimization algorithm namely water strider algorithm (WSA). The WSA optimizes the MLP parameters to increase the model's accuracy. The MLP-WSA model's predictive capabilities were compared with various algorithms, such as MLP-sine cosine (SCA), MLP-salp swarm (MLP-SSA), Multi-Layer Perceptron-particle swarm optimization (MLP-PSO), and MLP models. Furthermore, we proposed an inclusive multiple model (IMM) that utilizes the outputs of the MLP-WSA to predict W.S. The study utilized fuzzy reasoning to modify MLP models to remove redundant weights and reduce computation time. Finally, to predict W.S, we considered five stations in Malaysia. By utilizing the WSA and Gamma tests, we identified that the IMM model provided the most optimal input for our model. To test the I.P. station, the RMSE of the IMM model was lower than other models. Additionally, the NSE of the IMM model was found to be higher at the B.L. station, indicating superior performance. Furthermore, the IMM model's mean absolute error was notably lower than other models in C.H. station. Overall, the results demonstrate that the combination of the WSA and Gamma tests allowed us to achieve more accurate and efficient predictions with less computation time using fuzzy reasoning. [ABSTRACT FROM AUTHOR]

Savonius turbine is an alternative wind turbine design that has the ability for self-starting from static condition at different blade angle. There are concerns in the structural behavior of the materials used for the Savonius turbine blades under aerodynamic loads. This study aims to optimize the structural and material properties of Savonius wind turbine blades by conducting a comparative analysis of maximum stress and deformation across different materials. This research involves creating detailed numerical models of the turbine blades using Finite Element Analysis to simulate the mechanical behavior under operational loads. Wood, Aluminum, and Steel are investigated to examine its effect of structural performance in the static conditions. In addition to solid isotropic materials, anisotropic materials such as Carbon Fiber and Glass Fiber composites are investigated. Ansys Composite Prepost is used to access the mechanical properties of composites. Initial analysis shows that the deformation is highly dependent on the Young's modulus of each material. Due to the dependency of composite on the fiber properties, a multi-objective optimization is defined to optimize these parameters on a five-layer composite structure to minimize stress, deformation, and weight. It was found that Carbon-Fiber composites exhibit better performance compared to other materials with improvement up to 22% in maximum deformation. These findings highlight the potential for substantial improvements in blade efficiency and durability through careful material selection and optimization. These findings can be utilized for a preliminary design of Savonius wind turbines and other renewable energy sector, where optimizing wind turbine performance is crucial for sustainable energy production to find optimum composite configuration without compromising its aerodynamic performance. [ABSTRACT FROM AUTHOR]

Predicting and mapping appropriate habitats for endangered and threatened species is crucial for monitoring and restoring their dwindling populations in their natural surroundings. Additionally, it aids in selecting suitable conservation sites and effectively managing their habitats. An ideal approach for habitat suitability modeling involves utilizing MaxEnt machine learning techniques. The MaxEnt model was employed to forecast habitat suitability for key species, including Ursus arctos, Capra aegagrus, Ovis ammon, Lutra lutra, Martes foina, Lynx lynx, and Panthera pardus. Additionally, Linkage Pathways were employed to model ecological corridors connecting core habitats, enhancing our understanding of landscape connectivity for these species. The results showed that it is imperative to safeguard vital northern and southern areas between the prohibited hunting zones and the protected area. These areas provide the best routes for species to move between two habitats. However, settlements and rural areas pose a significant threat that can lead to the reduction or destruction of these communication areas. Therefore, protecting these regions should be a top priority. [ABSTRACT FROM AUTHOR]

It is crucial for developing countries such as Malaysia to be able to accurately predict future municipal solid waste generations in order to achieve high-quality waste management. The previous machine algorithm applied in the proposed study area Malaysia was an artificial neural network using NARX inputs to accommodate the need of forecasting municipal solid waste generations in Malaysia. However, this approach is not highly accurate in today's higher progressive state. Therefore, one of the aims of this research was to investigate the use of machine learning algorithms and its benefits. The machine learning algorithms investigated are specifically Gaussian process regression (GPR), ensemble of trees and neural networks. Each of these algorithms has its many strengths that could be altered according to the needs of users. For instance, various versions of neural networks are widely used for predicting municipal solid waste which includes the current approach adapted in the proposed study area. The findings indicated that the bagged tree model currently developed is not suitable for plotting a linear prediction although it managed to obtain a high performance of coefficient of determination (R2) = 0.92. Regarding GPR and neural network, the accuracy of the models was very high when every variable is included as a scenario which gives a perfect R2 = 1.00. The findings also showed that GPR and neural networks had the least error with root mean square error (RMSE) of 0.00009748 and 0.00099684, and mean absolute error (MAE) of 0.000071824 and 0.000672810, respectively. This study managed to fill in the gap of using GPR for predicting municipal solid waste generation. The outcome of this study could be of direct interest to public and private solid waste management companies in order to effectively manage solid waste through predicting the municipal solid waste generation accurately. [ABSTRACT FROM AUTHOR]

Correct inflow prediction is a critical non-engineering measure for ensuring flood control and increasing water supply efficiency. In addition, accurate inflow prediction can offer reservoir planning and management guidance since inflow is the major input into reservoirs. This study aims at generalizing a machine learning model for forecasting reservoir inflow. Daily, weekly, and monthly inflow and rainfall time-series data have been collected as two hydrological parameters to forecast reservoir inflow using a machine learning method, namely, support vector regression (SVR). Four different SVR kernels have been applied in this study. The kernels are radial basis function (RBF), linear, normalized polynomial, and sigmoid. Two scenarios for input selection have been implemented. Dokan dam in Kurdistan region of Iraq and Warragamba Dam in Australia were selected as the case studies for this research. For the purpose of generalization, the proposed models have been applied to two countries with a different climate condition. The findings showed that daily timescale outperformed weekly and monthly, while RBF outperformed the other SVR kernels with root-mean-square error (RMSE) = 145.7 and coefficient of determination (R2) = 0.85 for forecasting daily inflow at Dokan dam. However, RBF kernel could not perform well for forecasting daily inflow in Warragamba dam. The results showed that the proposed machine learning model performed well at Kurdistan region of Iraq only, while the result for Australia was not accurate. Therefore, the proposed models could not be generalized. [ABSTRACT FROM AUTHOR]

A spillway is a significant structure that releases the exceeded water during extreme flood events. It is considered as a safety valve during dams operating. Spillway location at dam body is one of the most important requirements of dam design. Different locations may influence the flow patterns which lead to change in the hydraulic performance of the spillway. In this paper, a numerical model was presented utilizing the Ansys fluent software using the realizable k-ɛ turbulence model to investigate the flow patterns of Mandali Dam's spillway at different locations. The interaction between air and water phases was simulated by using volume of fluid (VOF) model. Moreover, cavitation damage was studied for different flow discharges values. the validation of the numerical model was executed in compression with the measurement of the physical model. A discharge of 1800 m3/s was utilized for validation, while five different discharges; 1800, 1300, 1040, 440, and 67 m3/s were employed to investigate the hydraulic properties. The results show an agreement between numerical model and the physical model. According to the hydraulic properties, the spillway location at the center of the dam is better than its location at the edge of the dam. The discharge coefficient value in scenario one was found to be closer to the physical model value compared to the second scenario. At discharge 1800 and 1300, the discharge coefficient was 2.02 and 1.72, while in scenario two it was 1.46 and 1.21 in comparison with physical model which was 2.06 and 1.99. For cavitation investigations, the numerical model shows that the spillway is safe with no cavitation effects for the whole applied discharge values. [ABSTRACT FROM AUTHOR]

Water is the most basic need for humans and a source of livelihood for humans. Lack of human awareness to maintain water quality, causing water to become polluted, by both industrial and household waste, impacts on human health and material loss. Thus, it is important to create technology that can monitor water pollution automatically and quickly. This research aims to create a system which utilizes the Internet of Things (IoT) technology that can facilitate quality of water by measuring parameters such as pH and turbidity. The methodology of the system progresses by the usage of a controller which is ATmega328P-AU, pH sensor to measure acidity, turbidity sensor to measure turbidity level, LPWAN LoRa works like a communication of data transmission as well as cloud service, namely Antares, to store data that are sent via Android. Based on the outcomes, the proposed system has achieved a reliable accuracy with percentage error of 99.73% in pH sensor and 99.41% in the turbidity sensor. Also, 2.6 s is the average required time to deliver the results to the cloud service. [ABSTRACT FROM AUTHOR]

Recently, there has been a growing interest in employing optimization techniques to ascertain the most efficient operation of reservoirs. This involves their application to various facets of the reservoir operating system, particularly in determining optimal rule curves. This study delves into the exploration of different algorithms, including Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Firefly Algorithm (FA), Invasive Weed Optimization (IWO), Teaching Learning-Based Optimization (TLBO), and Harmony Search (HS). Each algorithm was integrated into a reservoir simulation model, focusing on finding optimal rule curves for the Mujib reservoir in Jordan from 2004 to 2019. The primary objective was to evaluate the long-term impact of water shortages and excess releases on the Mujib reservoir. Furthermore, the study aimed to determine the effects of water demand management by reducing it by 10%, 20%, and 30%. The results revealed that the used algorithms effectively mitigated water shortages and excess releases compared to the current operational strategy. Notably, the Teaching Learning-Based Optimization (TLBO) algorithm yielded the most favorable outcomes, reducing the frequency and average of water shortages to 55.09% and 56.26%, respectively. Additionally, it curtailed the frequency and average of excess releases to 63.16% and 73.31%, respectively. [ABSTRACT FROM AUTHOR]

For more than one billion people living in coastal regions, coastal aquifers provide a water resource. In coastal regions, monitoring water quality is an important issue for policymakers. Many studies mentioned that most of the conventional models were not accurate for predicting total dissolved solids (TDS) and electrical conductivity (EC) in coastal aquifers. Therefore, it is crucial to develop an accurate model for forecasting TDS and EC as two main parameters for water quality. Hence, in this study, a new hybrid deep learning model is presented based on Convolutional Neural Networks (CNNE), Long Short-Term Memory Neural Networks (LOST), and Gaussian Process Regression (GPRE) models. The objective of this study will contribute to the sustainable development goal (SDG) 6 of the united nation program which aims to guarantee universal access to clean water and proper sanitation. The new model can obtain point and interval predictions simultaneously. Additionally, features of data points can be extracted automatically. In the first step, the CNNE model automatically extracted features. Afterward, the outputs of CNNE were flattened. The LOST used flattened arrays for the point prediction. Finally, the outputs of the GPRE model receives the outputs of the LOST model to obtain the interval prediction. The model parameters were adjusted using the rat swarm optimization algorithm (ROSA). This study used PH, Ca + + , Mg2 + , Na + , K + , HCO3, SO4, and Cl− to predict EC and TDS in a coastal aquifer. For predicting EC, the CNNE-LOST-GPRE, LOST-GPRE, CNNE-GPRE, CNNE-LOST, LOST, and CNNE models achieved NSE values of 0.96, 0.95, 0.92, 0.91, 0.90, and 0.87, respectively. Sodium adsorption ratio, EC, magnesium hazard ratio, sodium percentage, and total hardness indices were used to evaluate the quality of GWL. These indices indicated poor groundwater quality in the aquifer. This study shows that the CNNE-LOST-GPRE is a reliable model for predicting complex phenomena. Therefore, the current developed hybrid model could be used by private and public water sectors for predicting TDS and EC for enhancing water quality in coastal aquifers. [ABSTRACT FROM AUTHOR]

Rainfall is crucial for the development and management of water resources. Six hybrid soft computing models, including multilayer perceptron (MLP)–Henry gas solubility optimization (HGSO), MLP–bat algorithm (MLP–BA), MLP–particle swarm optimization (MLP–PSO), radial basis neural network function (RBFNN)–HGSO, RBFNN–PSO, and RBFGNN–BA, were used in this study to forecast monthly rainfall at two stations in Malaysia (Sara and Banding). Different statistical measures (mean absolute error (MAE) and Nash–Sutcliffe efficiency (NSE) and percentage of BIAS (PBIAS)) and a Taylor diagram were used to assess the models' performance. The results indicated that the MLP–HGSO performed better than the other models in forecasting rainfall at both stations. In addition, transition matrices were computed for each station and year based on the conditional probability of rainfall or absence of rainfall on a given month. The values of MAE for testing processes for the MLP–HGSO, MLP–PSO, MLP–BA, RBFNN–HGSO, RBFNN–BA, and RBFNN–PSO at the first station were 0.712, 0.755, 0.765, 0.717, 0.865, and 0.891, while the corresponding NSE and PBIAS values were 0.90–0.23, 0.83–0.29, 0.85–0.25, 0.87–0.27, 0.81–0.31, and 0.80–0.35, respectively. For the second station, the values of MAE were found 0.711, 0.743, 0.742, 0.719, 0.863 and 0.890 for the MLP–HGSO, MLP–PSO, MLP–BA, RBFNN–HGSO, RBFNN–BA, and RBFNN–PSO during testing processes and the corresponding NSE–PBIAS values were 0.92–0.22, 0.85–0.28, 0.89–0.26, 0.91–0.25, 0.83–0.31, 0.82–0.32, respectively. Based on the outputs of the MLP–HGSO, the highest rainfall was recorded in 2012 with a probability of 0.72, while the lowest rainfall was recorded in 2006 with a probability of 0.52 at the Sara Station. In addition, the results indicated that the MLP–HGSO performed better than the other models within the Banding Station. According to the findings, the hybrid MLP–HGSO was selected as an effective rainfall prediction model. [ABSTRACT FROM AUTHOR]

The objective of this review article is to analyze published data encompassing compressive strength, tensile strength, elastic modulus, and flexural strength, as well as the utilization of scanning electron microscopy (SEM), energy dispersive x-ray spectroscopy (EDS), and x-ray diffraction (XRD) for Ultra High-Performance Geopolymer Concrete (UHP-GC), with the focus of establishing the current research trends regarding its mechanical, microstructural, and chemical characteristics. After a critical evaluation of the published data from the literature findings, it became evident that UHP-GC can attain a remarkably high level of engineering performance. In UHP-GC, the optimum percentage of silica fume as a slag partial replacement to achieve high compression, tensile, and elastic modulus were traced to be 25, 30, and 35%, respectively. The optimum ratio of sodium silicate to sodium hydroxide and sodium hydroxide molarity for UHP-GC were identified to be 3.5 and 16, respectively. All in all, the review provides a thorough understanding of the review gap and distinct functions of different raw materials in decreasing porosity and enhancing the formation of geopolymeric gels that not only bond but also strengthen UHP-GC. UHP-GC stands as an energy-saving material in concrete technology, poised to forge a path towards a sustainable future for the building sector. [ABSTRACT FROM AUTHOR]

Due to excessive streamflow (SF), Peninsular Malaysia has historically experienced floods and droughts. Forecasting streamflow to mitigate municipal and environmental damage is therefore crucial. Streamflow prediction has been extensively demonstrated in the literature to estimate the continuous values of streamflow level. Prediction of continuous values of streamflow is not necessary in several applications and at the same time it is very challenging task because of uncertainty. A streamflow category prediction is more advantageous for addressing the uncertainty in numerical point forecasting, considering that its predictions are linked to a propensity to belong to the pre-defined classes. Here, we formulate streamflow prediction as a time series classification with discrete ranges of values, each representing a class to classify streamflow into five or ten, respectively, using machine learning approaches in various rivers in Malaysia. The findings reveal that several models, specifically LSTM, outperform others in predicting the following n-time steps of streamflow because LSTM is able to learn the mapping between streamflow time series of 2 or 3 days ahead more than support vector machine (SVM) and gradient boosting (GB). LSTM produces higher F1 score in various rivers (by 5% in Johor, 2% in Kelantan and Melaka and Selangor, 4% in Perlis) in 2 days ahead scenario. Furthermore, the ensemble stacking of the SVM and GB achieves high performance in terms of F1 score and quadratic weighted kappa. Ensemble stacking gives 3% higher F1 score in Perak river compared to SVM and gradient boosting. [ABSTRACT FROM AUTHOR]

This comprehensive study reviews the latest and most popular artificial intelligence (AI) techniques utilised for estimating pan evaporation (Ep), an essential parameter for water resource management and irrigation planning. Through an extensive evaluation of 76 papers published between 2006 and 2022, this study analyses the input data categories, time steps, properties, and capabilities of different AI models used for estimating Ep across various regions. The reviewed papers offer partial and comprehensive observations, providing valuable insights for researchers looking to model Ep in similar studies. Furthermore, this study proposes innovative theories and approaches to enhance the efficacy of Ep modelling in the relevant analysis domain. While hybrid AI techniques have gained popularity due to their perceived superiority over standalone deep learning and machine learning approaches, they often pose significant operational and computational challenges for Ep forecasting. As such, the study strongly recommends the use of transformer neural networks for Ep estimation, given their unique architecture and promising performance across various fields. Overall, this study presents a comprehensive and up-to-date overview of the latest AI-based techniques for estimating Ep and highlights the most promising approaches for future research. [ABSTRACT FROM AUTHOR]

Conventional machine learning models have been widely used for reservoir inflow and rainfall prediction. Nowadays, researchers focus on a new computing architecture in the area of AI, namely, deep learning for hydrological forecasting parameters. This review paper tends to broadcast more of the intriguing interest in reservoir inflow prediction utilizing deep learning and machine learning algorithms. The AI models utilized for different hydrology sectors, as well as the most prevalent machine learning techniques, will be explored in this thorough study, which divides AI techniques into two primary categories: deep learning and machine learning. In this study, we look at the long short-term memory deep learning method as well as three traditional machine learning algorithms: support vector machine, random forest, and boosted regression tree. Under each part, a summary of the findings is provided. For convenience of reference, some of the benefits and drawbacks discovered through literature reviews have been listed. Finally, future recommendations and overall conclusions based on research findings are given. This review focuses on papers from high-impact factor periodicals published over a 4 years period beginning in 2018 onwards. [ABSTRACT FROM AUTHOR]

The water is the main pivotal sources of irrigation in agricultural activities and affects human daily activities such as drinking. The water quality has a significant impact on various aspects and thus this review aims to addresses existing problems related to water quality prediction methods that have been found in the literature. We explore numerous quality parameters incorporated in the modelling process to measure the quality of water. Furthermore, we review the commonly adopted artificial intelligence-based models which have been utilized to forecast the water quality. 83 studies published from 2009 to 2023 were selected and reviewed based on their success in modelling and forecasting the water quality in multiple regions. We compared these articles in terms of parameters, modelling algorithms, time scale scenarios, and performance measurement indicators. This paper is beneficial to researchers that have interests to conduct future studies related to water quality forecasting. Additionally, we discuss a variety of modelling methods such as deep learning (DL) that have proven to boost the efficiency compared to traditional machine learning (ML) models. As a result, the hybrid-DL models were found to outperform other models such as standalone ML, standalone DL, and hybrid-ML. This study shows a significant limitation of the data-hungry DL models which require a big data size for modelling. Hence, at the end of this review study, we discuss the potential of some methods such as generative adversarial networks (GANs) and attention-based transformer to open the door for water quality prediction improvement. GAN has shown promising performance in other domains for synthetic data generation. The potential usage of GAN for water quality domain can overcome the limitations of lack of data and enhance the performance of the predictive models reviewed in this study. Similarly, transformer was found to be state of the art model for time series prediction and thus it can be good candidate to predict water quality. [ABSTRACT FROM AUTHOR]

This study aims to assess the practicality of utilizing artificial intelligence (AI) to replicate the adsorption capability of functionalized carbon nanotubes (CNTs) in the context of methylene blue (MB) removal. The process of generating the carbon nanotubes involved the pyrolysis of acetylene under conditions that were determined to be optimal. These conditions included a reaction temperature of 550 °C, a reaction time of 37.3 min, and a gas ratio (H2/C2H2) of 1.0. The experimental data pertaining to MB adsorption on CNTs was found to be extremely well-suited to the Pseudo-second-order model, as evidenced by an R2 value of 0.998, an X2 value of 5.75, a qe value of 163.93 (mg/g), and a K2 value of 6.34 × 10–4 (g/mg min).The MB adsorption system exhibited the best agreement with the Langmuir model, yielding an R2 of 0.989, RL value of 0.031, qm value of 250.0 mg/g. The results of AI modelling demonstrated a remarkable performance using a recurrent neural network, achieving with the highest correlation coefficient of R2 = 0.9471. Additionally, the feed-forward neural network yielded a correlation coefficient of R2 = 0.9658. The modeling results hold promise for accurately predicting the adsorption capacity of CNTs, which can potentially enhance their efficiency in removing methylene blue from wastewater. [ABSTRACT FROM AUTHOR]

The current research aims to optimize the water release to generate optimal hydropower generation for the future up to the year 2039. The study's novelty is the adaptive and nonadaptive rule curves for power production using optimization algorithms under the climate change model. In addition, the study used the RCP 8.5 scenario based on seven climate change models. A weighting method was used to select the best climate change models. The method can allocate more weights to more accurate models. The results revealed that the temperature increased by about 26% in the future, while precipitation would decreased by around 3%. The bat algorithm was also used, given it is a powerful method in solving optimization problems in water resources management. The results indicated that less power could be generated during the future period in comparison with the base period as there will be less inflow to the reservoir and released water for hydropower generation. However, by applying adaptive rule curves, the hydropower generation may be improved even under the climate change conditions. For example, the volumetric reliability index obtained when using adaptive rule curves (92%) was higher than when nonadaptive rule curves (90%) were applied. Also, the adoption of adaptive rule curves decreased the vulnerability index for the future period. Therefore, the bat algorithm with adaptive rule curves has a high potential for optimizing reservoir operations under the climate change conditions. [ABSTRACT FROM AUTHOR]

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Natural calamities like droughts have harmed not just humanity throughout history but also the economy, food, agricultural production, flora, animal habitat, etc. A drought monitoring system must incorporate a study of the geographical and temporal fluctuation of the drought characteristics to function effectively. This study investigated the space–time heterogeneity of drought features across Sabah and Sarawak, East Malaysia. The Standardized Precipitation Index (SPIs) at timescales of 1-month, 3-months, and 6-months was selected to determine the spatial distribution of drought characteristics. Rainfall hydrographs for the area for 30 years between 1988 and 2017 have been used in this study. A total of six five-year sub-periods were studied, with an emphasis on the lowest and highest drought occurrence. The sub-periods were a division of the 30 years over an arbitrary continual division for convenience. The results showed that the sub-periods 1993–1997 and 2008–2012 had the highest and lowest comparative drought events. The drought conditions were particularly severe in Central and Eastern parts of East Malaysia, owing to El Nino events and the country's hilly terrain. Understanding how and when drought occurs can aid in establishing and developing drought mitigation strategies for the region. [ABSTRACT FROM AUTHOR]

Due to excessive streamflow (SF), Peninsular Malaysia has historically experienced floods and droughts. Forecasting streamflow to mitigate municipal and environmental damage is therefore crucial. Streamflow prediction has been extensively demonstrated in the literature to estimate the continuous values of streamflow level. Prediction of continuous values of streamflow is not necessary in several applications and at the same time it is very challenging task because of uncertainty. A streamflow category prediction is more advantageous for addressing the uncertainty in numerical point forecasting, considering that its predictions are linked to a propensity to belong to the pre-defined classes. Here, we formulate streamflow prediction as a time series classification with discrete ranges of values, each representing a class to classify streamflow into five or ten, respectively, using machine learning approaches in various rivers in Malaysia. The findings reveal that several models, specifically LSTM, outperform others in predicting the following n-time steps of streamflow because LSTM is able to learn the mapping between streamflow time series of 2 or 3 days ahead more than support vector machine (SVM) and gradient boosting (GB). LSTM produces higher F1 score in various rivers (by 5% in Johor, 2% in Kelantan and Melaka and Selangor, 4% in Perlis) in 2 days ahead scenario. Furthermore, the ensemble stacking of the SVM and GB achieves high performance in terms of F1 score and quadratic weighted kappa. Ensemble stacking gives 3% higher F1 score in Perak river compared to SVM and gradient boosting. [ABSTRACT FROM AUTHOR]

Forecasting sea level is critical for coastal structure building and port operations. There are, however, challenges in making these predictions, resulting from the complicated processes at various periods. This study discussed the continual development of the application and forecasting approaches for sea level rise, in standard and advanced modeling versions. To date, the tide gauge and satellite altimetry are the commonly used approaches for sea level measurement. Tide gauges are mostly deficient in typical offshore circumstances; but however, this may be compensated for with satellite altimetry, a complementing technique. With technological improvement, sea level measurement may be forecasted using a variety of computer science approaches known as artificial intelligence, including machine learning and deep learning; capable of extracting information and formulating relationships from the given dataset. Its potential and extensive advantages led to a sharp growth in its recognition among hydrologists. The most successful techniques for enhancing these approaches include hybridization, ensemble modeling, data decomposition, and algorithm optimization. These advanced techniques are a prominent study area and a viable strategy for determining intelligent forecasts of sea level rise with sufficient lead time. For improved performance, the modeling requires incorporating numerous input parameters, such as precipitation, wind direction, ocean current, and sea surface temperature; for better representing the process, thus reducing forecast error and uncertainty. Deep learning is more effective and enhances existing machine learning models for forecasting future sea level rise due to its automatic feature extraction and memory-storing capability. [ABSTRACT FROM AUTHOR]

Accurate prediction of short-term water demand, especially, in the case of extreme weather conditions such as flood, droughts and storms, is crucial information for the policy makers to manage the availability of freshwater. This study develops a hybrid model for the prediction of monthly water demand using the database of monthly urban water consumption in Melbourne, Australia. The dataset consisted of minimum, maximum, and mean temperature (°C), evaporation (mm), rainfall (mm), solar radiation (MJ/m2), maximum relative humidity (%), vapor pressure (hpa), and potential evapotranspiration (mm). The dataset was normalized using natural logarithm and denoized then by employing the discrete wavelet transform. Principle component analysis was used to determine which predictors were most reliable. Hybrid model development included the optimization of ANN coefficients (its weights and biases) using adaptive guided differential evolution algorithm. Post-optimization ANN model was trained using eleven different leaning algorithms. Models were trained several times with different configuration (nodes in hidden layers) to achieve better accuracy. The final optimum learning algorithm was selected based on the performance values (regression; mean absolute, relative and maximum error) and Taylor diagram. [ABSTRACT FROM AUTHOR]

The study explores the spatio-temporal variation of water quality parameters in the Hooghly estuary, which is considered an ecologically-stressed shallow estuary and a major distributary for the Ganges River. The estimated parameters are chlorophyll-a, total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM). The Sentinel-3 OLCI remote sensing imageries were analyzed for the duration of October 2018 to February 2019. We observed that the water quality of the Hooghly estuaries is comparatively low-oxygenated, mesotrophic, and phosphate-limited. Ongoing channel dredging for maintaining shipping channel depth keeps the TSM in the estuary at an elevated level, with the highest amount of TSM observed during March of 2019 (41.59g m−3) at station A, upstream point. Since the pre-monsoon season, TSM data shows a decreasing trend towards the mouth of the estuary. Chl-a concentration is higher during pre-monsoon than monsoon and post-monsoon periods, with the highest value observed in April at 1.09 mg m−3 in station D during the pre-monsoon period. The CDOM concentration was high in the middle section (January–February) and gradually decreased towards the estuary's head and mouth. The highest CDOM was found in February at locations C and D during the pre-monsoon period. Every station shows a significant correlation among CDOM, TSM, and Chl-a measured parameters. Based on our satellite data analysis, it is recommended that SNAP C2RCC be regionally used for TSM, Chl-a, and CDOM for water quality product retrieval and in various algorithms for the Hooghly estuary monitoring. [ABSTRACT FROM AUTHOR]

Freshwater is an essential resource used to generate electricity at coal-fired power plants. Previous literature has shown that a few states in Malaysia will face a high risk of water stress. Hence, coal-fired power plants located at risk states are exposed to potential water risk. This study aims (i) to determine the operational blue water footprint at coal-fired power plant from 2009 until 2020; (ii) to assess the water stress index at Perak, Negeri Sembilan and Johor; and (iii) to compare the water deficit impact across three states. This study accounted the operational water footprint using Water Footprint Assessment Global Manual. The study boundary focuses on water consumption of generating electricity at operational level. The water stress index was assessed based on ratio of water demand and water availability at Perak, Negeri Sembilan and Johor watershed. Next water deficit impact was determined to compare the impact of operational blue water footprint on local water resources. Data for this study were collected from the state's water authority, published literature, national reports, and statistics. Result of this study found the average operational blue water footprint recorded by coal-fired power plant located at Perak is 0.11 m3/MWh followed 0.09 m3/MWh (Johor) and 0.04 m3/MWh (Negeri Sembilan). Water stress index at Perak and Negeri Sembilan shows moderate water stress, whereas Johor indicates low water stress index. The water deficit result shows Perak has the highest total water deficit at 12,542,824 m3H2Oeqn from 2009 to 2020. This is due to moderate water stress condition at Perak and the total blue water footprint of coal-fired power plant at Perak is 67% and 42% more as compared to Negeri Sembilan and Johor, respectively. The result from this study is useful in enhancing understanding of water consumption pattern at coal-fired power plant and its impact on state's water resources for future electricity scenarios. [ABSTRACT FROM AUTHOR]

Rainfall prediction is an important issue in water resource management. Predicting rainfall helps researchers to monitor droughts, surface water and floods. The current study introduces a new deep learning model named convolutional neural network (CONN)- support vector machine (SVM)- Gaussian regression process (GPR) to predict daily and monthly rainfall data in Terengganu River Basin, Malaysia. The CONN-SVM-GRP model can extract the most important features automatically. The main advantage of the new model is to reflect the uncertainty values in the modelling process. The lagged rainfall values were used as the input variables to the models. The proposed CONN-SVM-GRP model successfully decreased the Mean Absolute Error (MAE) of other models by 5.9%-23% at the daily scale and 20%-61% at the monthly scale. The CONN-SVM-GRP model also provided the lowest uncertainty among other models, making it a reliable tool for predicting data points and intervals. Hence, it can be concluded that CONN-SVM-GRP model contributes to the sustainable management of water resources, even when satellite data is unavailable, by using lagged values to predict rainfall. Additionally, the model extracts important features without using preprocessing methods, further improving its efficiency. Overall, the CONN-SVM-GRP model can help researchers predict rainfall, which is essential for monitoring water resources and mitigating the impacts of droughts, floods, and other natural disasters. [ABSTRACT FROM AUTHOR]

As a result of global climate change, sustainable water supply management is becoming increasingly difficult. Dams and reservoirs are key tools for controlling and managing water resources; they have benefited human cultures in a variety of ways, including enhanced human health, increased food production, water supply for domestic and industrial use, economic growth, irrigation, hydro-power generation, and flood control. This study aims to compare the application of deep learning and conventional machine learning algorithms for predicting daily reservoir inflow. Long short-term memory (LSTM) has been applied as a deep learning algorithm and boosted regression tree (BRT) has been implemented as a machine learning algorithm. Five statistical indices have been selected to evaluate the performance of the proposed models. The selected statistical measurements are mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), Nash Sutcliffe Model Efficiency Coefficient (NSE), and the RMSE-observations standard deviation ratio (RSR). The findings showed that LSTM outperformed BRT with a significant difference in terms of accuracy. [ABSTRACT FROM AUTHOR]

Dam reservoir operations are a critical issue for decision-makers in maximizing the use of water resources. Artificial Intelligence and Machine Learning models (AI & ML) approaches are increasingly popular for reservoir inflow predictions. In this study, the multilayer perceptron neural network (MLP), Support Vector Regression (SVR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and the Extreme Gradient Boosting (XG-Boost), were adopted to forecast reservoir inflows for the monthly and daily timeframes. Results showed that: (1) For the monthly timeframe, all the four models were proficient in obtaining efficient monthly reservoir inflows by scoring at least an R² of 0.5; with the XG-Boost ranked as the best model, followed by the MLPNN, SVR, and lastly ANFIS. (2) the XG-Boost still outperforms all other models for forecasting daily inflow; but however, with reduced performance. The models were still ranked in the same order, with the ANFIS showing very poor performance in scenario-2, scenario-3, and scenario-4. (3) For daily inflows, the best scenarios are scenario-5, scenario-6, scenario-7 as the models were trained based on the 1,3,5, days-lag forecasted inflow, and overall, the XG-Boost outperforms all the other models. [ABSTRACT FROM AUTHOR]

The deterministic approach, which utilizes the gradient information in the search process, is prone to trapping at local minima, primarily due to the presence of saddle points and local minima in the non-convex objective function of an artificial neural network (ANN). This study investigated the efficacy of a hybrid model that adopted a meta-heuristic algorithm (MHA) as an optimizer to extend the training ANN method, from a gradient-based to a stochastic population-based approach for streamflow forecasting. In the latter, parameter tuning utilizing the design of experiment (DOE) technique, has become an integral element in the optimization process due to reliance on their parameters. For model convenience, a wavelet transform was employed to decompose the series into sub-series. The empirical studies of MHA performance showed that the hybrid MHA-ANN was superior for streamflow forecasting, especially with the firefly algorithm that had an average RMSE = 96.06, an improvement of approximately 17% over the gradient-based ANN (RMSE = 113.92). However, among the adopted MHAs, not all are compatible with optimizing the ANN for streamflow forecasting, thus requiring a thorough study as performance varies from case to case. Two additional statistical tests, such as the Kruskal-Wallis H test and the Mann-Whitney U test, further validated such disparity in the MHA's performance. [ABSTRACT FROM AUTHOR]

To ease water scarcity, dynamic programming, stochastic dynamic programming, and heuristic algorithms have been applied to solve problem matters related to water resources. Development, operation, and management are vital in a reservoir operating policy, especially when the reservoir serves a complex objective. In this study, an attempt via metaheuristic algorithms, namely the Harris Hawks Optimisation (HHO) Algorithm and the Opposite Based Learning of HHO (OBL-HHO) are made to minimise the water deficit as well as mitigate floods at downstream of the Klang Gate Dam (KGD). Due to trade-offs between water supply and flood management, the HHO and OBL-HHO models have configurable thresholds to optimise the KGD reservoir operation. To determine the efficacy of the HHO and OBL-HHO in reservoir optimisation, reliability, vulnerability, and resilience are risk measures evaluated. If inflow categories are omitted, the OBL-HHO meets 71.49% of demand compared to 54.83% for the standalone HHO. The HHO proved superior to OBL-HHO in satisfying demand during medium inflows, achieving 38.60% compared to 20.61%, even though the HHO may have experienced water loss at the end of the storage level. The HHO is still a promising method, as proven by its reliability and resilience indices compared to other published heuristic algorithms: at 62.50% and 1.56, respectively. The Artificial Bee Colony (ABC) outcomes satisfied demand at 61.36%, 59.47% with the Particle Swarm Optimisation (PSO), 55.68% with the real-coded Genetic Algorithm (GA), and 23.5 percent with the binary GA. For resilience, the ABC scored 0.16, PSO scored 0.15, and real coded GA scored 0.14 whilst the binary-GA has the worst failure recovery algorithm with 0.09. [ABSTRACT FROM AUTHOR]

Streamflow forecasting process exhibited highly nonstationary and stochastic pattern, thus not easy to be done with simple models. There is a need to develop an efficient and precise streamflow forecasting system which is vital for water management at hydrological infrastructures like Aswan High Dam (AHD). As the decision makers will be able to decide on water allocation for different purposes such as irrigation, domestic and industrial uses. This study explores the potential of AI model: nonlinear autoregressive neural network (NAR) in performing inflow forecasting to AHD. The dataset of past 130 years of Nile River discharge rate was used for the network development as well as evaluation of models' performance. This study also proposes an integration process of NAR with Holt-Winters exponential smoothing to improve the accuracy of the model. To determine the models' performance, different indicators were employed and calculated (MAE, MAPE, RMSE, R2). The results were compared to identify the optimal network architecture. The results show that the NAR models are capable of predicting the future values of AHD inflow in monthly time steps accurately. For standard NAR model, the root mean squared error (RMSE) was 2.0072, and the coefficient of determination (R2) between recorded and forecasted values was 0.9152. Values of RMSE = 1.5421 and R2 = 0.9760 and RMSE = 1.0843 and R2 = 0.9823 were obtained by NAR-SES and NAR-HW models respectively. The results reveal that combination of Holt-Winters exponential smoothing with NAR significantly improved the precision beyond the standard model. This study proved that NAR neural networks can be useful to address streamflow forecasting problems. [ABSTRACT FROM AUTHOR]

Sediment transport is a normal phenomenon in rivers and streams, contributing significantly to ecosystem production and preservation by replenishing vital nutrients and preserving aquatic life's natural habitats. Thus, sediment transport prediction through modeling is crucial for predicting flood events, tracking coastal erosion, planning for water supplies, and managing irrigation. The predictability of process-driven models may encounter various restrictions throughout the validation process. Given that data-driven models work on the assumption that the underlying physical process is not requisite, this opens up the avenue for AI-based model as alternative modeling. However, AI-based models, such as ANN and SVM, face problems, such as long-term dependency, which require alternative dynamic procedures. Since their performance as universal function approximation depends on their compatibility with the nature of the problem itself, this study investigated several distinct AI-based models, such as long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM), in predicting sediment transport in the Johor river. The collected historical daily sediment transport data from January 1, 2008, to December 01, 2018, through autocorrelation function, were used as input for the model. The statistical results showed that, despite their ability (deep learning and machine learning) to provide sediment predictions based on historical input datasets, machine learning, such as ANN, might be more prone to overfitting or being trapped in a local optimum than deep learning, evidenced by the worse in all metrics score. With RMSE = 11.395, MAE = 18.094, and R2 = 0.914, LSTM outperformed other models in the comparison. [ABSTRACT FROM AUTHOR]

This study aims to assess, compare, and attribute the effects due to separate and combined land use/land cover (LULC) and climate changes on hydrological processes in a tropical catchment. The Soil and Water Assessment Tool (SWAT) model is set up and calibrated for a small contributing sub-basin of the Tana River Basin (TRB) in Kenya. The model is then applied to simulate the hydrological components (i.e., streamflow (FLOW), evapotranspiration (ET), soil water (SW), and water yield (WYLD)) for different combinations of LULC and climate scenarios. Land use data generated from Land Satellite 5 Thematic Mapper (Landsat 5TM) images for two different periods (1987 and 2011) and satellite-based precipitation data from the African Rainfall Climatology version 2 (ARC2) dataset are utilized as inputs to the SWAT model. The Nash–Sutcliffe model efficiency (NSE), coefficient of determination (R2), percent bias (PBIAS), and the ratio of root mean square error to the standard deviation (RSR) for daily streamflow were 0.73, 0.76, 3.16%, and 0.51 in calibration period, respectively, and 0.45, 0.54, 12.53%, and 0.79 in validation period, respectively, suggesting that the model performed relatively good. An analysis of the LULC data for the catchment showed that there was an increase in agricultural, grassland, and forested land with a concomitant decrease in woodland and shrubland. Simulation results revealed that change in climate had a more significant effect on the simulated parameters than the change in LULC. It is shown that changes in LULC only had very minor effects in the simulated parameters. The monthly mean FLOW and WYLD decreased by 0.02% and 0.11%, respectively, while ET and SW increased by a monthly mean of 0.2% and 2.2%. Varying the catchment climate and holding the land use constant reduced FLOW, ET, SW, and WYLD by an average monthly mean of 43.2%, 21%, 13%, and 70%, respectively, indicating that climate changes have more significant effects on the catchment hydrological processes than changes in LULC. Thus, it is necessary to evaluate and identify the isolated and combined effects of LULC and climatic changes when assessing impacts on the TRB's hydrological processes. [ABSTRACT FROM AUTHOR]

Coastal zone has high value of socioeconomic activities. The number of developments and activities at the coastal areas of various sectors has increased day by day. Tsunami hazard is classified as one of the most devastating natural disasters that cause destruction along the coastal areas resulted to human fatalities. There are many causes of tsunami, but the most common is due to earthquake in the ocean. Previously recognised as a safe zone from natural disasters of tsunami, Malaysia was confronted by a rude awakening of the 2004 Andaman tsunami, causing a total loss of 68 lives. However, the potential impacts of tsunamis towards Malaysia coastal areas, especially at East Coast of Peninsular Malaysia, have not been explored extensively. This study, therefore, is critical and pertinent. It aims to investigate the potential seismic activities that generate tsunami occurrences affecting East Coast Peninsular Malaysia. This study further simulates Manila Trench earthquake-induced tsunami at various earthquake intensities, namely Mw 7.0, 8.0 and 9.0, by using TUNA model. Results obtained from this study indicated that tsunami wave generated by Manila Trench earthquake at Mw 9.0 resulted to the highest disastrous effects towards East Coast of Peninsular Malaysia. Findings from this study have established that Kelantan will experience the highest inundation depth at 4.0 m in Pasir Puteh with the highest inland inundation distance at 5.5 km in Bachok. Besides that, Terengganu will experience the highest inundation depth at 6.0 m in Kuala Terengganu with the highest inland inundation distance at 6.0 km also in Kuala Terengganu. In Pahang, the highest inundation depth at 4.5 m with the highest inland inundation distance at 3.3 km is anticipated to experience in Pekan, while in Johor, the highest inundation depth at 1.2 m with the highest inland inundation distance at 0.4 km is anticipated to experience in Mersing. On the other hand, Mw 8.0 and 7.0 earthquake was expected to be insignificant upon arriving at the coastal areas. Mitigation measures and evacuation plans can be effectively planned as findings obtained from this study are able to provide vital information on the possibilities of tsunami waves occurrences to overcome massive losses of properties and life. [ABSTRACT FROM AUTHOR]

For decision-making in farming, the operation of dams and irrigation systems, as well as other fields of water resource management and hydrology, evaporation, as a key activity throughout the universal hydrological processes, entails efficient techniques for measuring its variation. The main challenge in creating accurate and dependable predictive models is the evaporation procedure's non-stationarity, nonlinearity, and stochastic characteristics. This work examines, for the first time, a transformer-based deep learning architecture for evaporation prediction in four different Malaysian regions. The effectiveness of the proposed deep learning (DL) model, signified as TNN, is evaluated against two competitive reference DL models, namely Convolutional Neural Network and Long Short-Term Memory, and with regards to various statistical indices using the monthly-scale dataset collected from four Malaysian meteorological stations in the 2000–2019 period. Using a variety of input variable combinations, the impact of every meteorological data on the Ep forecast is also examined. The performance assessment metrics demonstrate that compared to the other benchmark frameworks examined in this work, the developed TNN technique was more precise in modelling monthly water loss owing to evaporation. In terms of predictive effectiveness, the proposed TNN model, enhanced with the self-attention mechanism, outperforms the benchmark models, demonstrating its potential use in the forecasting of evaporation. Relating to application, the predictive model created for Ep projection offers a precise estimate of water loss due to evaporation and can thus be used in irrigation management, agriculture planning based on irrigation, and the decrease in fiscal and economic losses in farming and related industries where consistent supervision and estimation of water are considered necessary for viable living and economy. [ABSTRACT FROM AUTHOR]

It is critical to monitor water quality to keep water bodies ecologically healthy and facilitate the sustainable development of Kenyir Lake. Water quality differs temporally and spatially and is affected by several factors. Typically, water quality inspection systems are cost- and labour-intensive depending on water quality indicator count and sampling frequency. Optimising the frequency and location of water quality sampling is crucial. This study focused on collecting water samples from 22 locations in Kenyir Lake during different seasons (normal, dry, and wet). The study aimed to assess the spatial and temporal variations in the water quality of Kenyir Lake based on multivariate statistical methods. In this study, the following water quality parameters were selected for analysis: temperature, dissolved oxygen (DO), pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), and ammoniacal nitrogen (NH3-N). In addition, a water quality index was also calculated. GIS software was used to assess water quality data, and various multivariate statistical methods like cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) were employed. The outcome shows minor spatial differences concerning Kenyir Lake; however, the temporal variations were noteworthy during this study duration. Cluster analysis divided the locations into 3 clusters with TSS being key parameter affecting the spatial differences in water quality. Stepwise discriminant analysis based on three parameters, pH, temperature, and TSS, produced the associated classification matrix that correctly estimated 69.7% of the input. NH3-N and TSS were found to be the two critical aspects that affect water quality during dry, wet, or normal climatic conditions. [ABSTRACT FROM AUTHOR]

Hydrologists rely extensively on anticipating river streamflow (SF) to monitor and regulate flood management and water demand for people. Only a few simulation systems, where previous techniques failed to anticipate SF data quickly, let alone cost-effectively, and took a long time to execute. The bat algorithm (BA), a meta-heuristic approach, was used in this study to optimize the weights and biases of the artificial neural network (ANN) model. The proposed hybrid work was validated in five different study areas in Malaysia. The statistical tests analysis of the preliminary results revealed that hybrid BA-ANN was superior to forecasting the SF at all five selected study areas, with average RMSE values of 0.103 m3/s for training and 0.143 m3/s for testing as compared to ANN standalone training and testing yielding 0.091 m3/s and 0.116 m3/s, respectively. This finding signifies that the implementation of BA into the ANN model resulted in a 20% improvement. In addition, with an R2 score of 0.951, the proposed model showed a better correlation than the 0.937 value of R2 of standard ANN. Nonetheless, while the proposed work outperformed the conventional ANN, the Taylor diagram, violin plot, relative error, and scatter plot findings confirmed the disparities in the proposed work's performance throughout the research regions. The findings of these evaluations highlighted that the adaptability of the proposed works would need detailed investigation because its performance differed from case to case. [ABSTRACT FROM AUTHOR]

One of the largest hydropower facilities currently in operation in Malaysia is the Terengganu hydroelectric facility. As a result, for hydropower generation to be sustainable, future water availability in hydropower plants must be known. Therefore, it is necessary to precisely estimate how the river flow will alter as a result of changing rainfall patterns. Finding the best value for the hyper-parameters is one of the problems with machine learning algorithms, which have lately been adopted by many academics. In this research, Artificial Neural Network (ANN) is integrated with a nature-inspired optimizer, namely Cuckoo search algorithm (CS-ANN). The performance of the proposed algorithm then will be examined based on statistical indices namely Root-Mean-Square Error (RSME) and Determination Coefficient (R2). Then, the accuracy of the proposed model will be then examined with the stand-alone Artificial Neural Network (ANN). The statistical indices results indicate that the proposed Hybrid CS-ANN model showed an improvement based on R2 value as compared to ANN model with R2 of 0.900 at training stage and R2 of 0.935 at testing stage. RMSE value, for ANN model, is 127.79 m3/s for training stage and 12.7 m3/s at testing stage. While for the proposed Hybrid CS-ANN model, RMSE value is equal to 121.7 m3/s for training stage and 10.95 m3/s for testing stage. The results revealed that the proposed model outperformed the stand-alone model in predicting the river flow with high level of accuracy. Although the proposed model could be applied in different case study, there is a need to tune the model internal parameters when applied in different case study. [ABSTRACT FROM AUTHOR]

Streamflow forecasting has always been important in water resources management, particularly the peak flow, which often determines the seriousness of the impending flood. However, the highly imbalanced flow distribution often hinders the machine learning algorithm's performance. In this paper, streamflow forecasting was approached through the formulation of two distinct machine learning problems: categorical streamflow forecast and regression streamflow forecast. Due to the distinctive characteristics of these two adopted forms, selecting the correct algorithm for the machine learning problem along with their hyperparameter tuning process is critical to the realization of the desired results. For the distinct streamflow formulated scenarios, three neural network algorithms and their hyperparameter tuning strategy were investigated. The comparative empirical studies had revealed that formulated categorical-based streamflow forecast is a better choice than a regression-based streamflow forecast, regardless of the algorithms used; for instance, the f1-score of 0.7 (categorical based) is obtained compared to the 0.53 (regression based) for the LSTM in scenario 1 (binary). Furthermore, forest-based algorithms were investigated and shown to be superior at forecasting high streamflow fluctuations in situations featuring low-dimensional streamflow input. Besides, encoding the streamflow time series as images (input) for forecasting purposes would require a thorough analysis as there is a discrepancy in the results, revealing that not all approaches are suitable for streamflow image transformation. The functional ANOVA analysis provided evidence to substantiate the Bayesian optimization results, implying that the hyperparameters were effectively optimized. [ABSTRACT FROM AUTHOR]

Earthquake is one of the natural disasters that have a big impact on society. Currently, there are many studies on earthquake detection. However, the vibrations that were detected by sensors were not only vibrations caused by the earthquake, but also other vibrations. Therefore, this study proposed an earthquake multi-classification detection with machine learning algorithms that can distinguish earthquake and non-earthquake, and vandalism vibration using acceleration seismic waves. In addition, velocity and displacement as integration products of acceleration have been considered additional features to improve the performances of machine learning algorithms. Several machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN) have been used to develop the best algorithm for earthquake multi-classification detection. The results of this study indicate that the ANN algorithm is the best algorithm to distinguish between earthquake and non-earthquake, and vandalism vibrations. Moreover, it's also more resistant to various input features. Furthermore, using velocity and displacement as additional features has been proven to increase the performance of every model. [ABSTRACT FROM AUTHOR]

Rainfall prediction is vital for the management of available water resources. Accordingly, this study used large lagged climate indices to predict rainfall in Iran's Sefidrood basin. A radial basis function neural network (RBFNN) and a multilayer perceptron (MLP) network were used to predict monthly rainfall. The models were trained using the naked mole rat (NMR) algorithm, firefly algorithm (FFA), genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. Large lagged climate indices, as well as three hybrid models, i.e., inclusive multiple model (IMM)-MLP, IMM-RBFNN, and the simple average method (SAM), were then employed to predict rainfall. This paper aims to predict rainfall using large climate indices, ensemble models, and optimized artificial neural network models. Also, the paper considers the uncertainty resources in the modeling process. The inputs were selected using a new input selection method, namely a hybrid gamma test (GT). The GT was integrated with the NMR algorithm to create a new test for determining the best input scenario. Therefore, the main innovations of this study were the introduction of the new ensemble and the new hybrid GT, as well as the new MLP and RBFNN models. The introduced ensemble models of the current study are not only useful for rainfall prediction but also can be used to predict other metrological parameters. The uncertainty of the model parameters and input data were also analysed. It was found that the IMM-MLP model reduced the root mean square error (RMSE) of the IMM-RBFNN, SAM, MLP-NMR, RBFNN-NMR, MLP-FFA, RBFNN-FFA, MLP-PSO, RBFNN-PSO, MLP-GA, and RBFNN-GA, MLP, and RBFNN models by 12%, 25%, 31%, 55%, 60%, 62%, 66%, 69%, 70%, 71%, 72%, and 72%, respectively. The IMMs, such as the IMM-MLP, IMM-RBFNN, and SAM, outperformed standalone models. The uncertainty bound of the multiple inclusive models was narrower than that of the standalone MLP and RBFNN models. The MLP-NMR model decreased the RMSE of the RBFNN-NMR, RBFNN-FFA, RBFNN-PSO, and RBFNN models by 15%, 26%, 37%, 42%, and 45%, respectively. The proposed ensemble models were robust tools for combining standalone models to predict hydrological variables. [ABSTRACT FROM AUTHOR]

Modeling wind speed has a significant impact on wind energy systems and has attracted attention from numerous researchers. The prediction of wind speed is considered a challenging task because of its natural nonlinear and random characteristics. Therefore, machine learning models have gained popularity in this field. In this paper, three machine learning approaches – Gaussian process regression (GPR), bagged regression trees (BTs) and support vector regression (SVR) – were applied for prediction of the weekly wind speed (maximum, mean, minimum) of the target station using other stations, which were specified as reference stations. Daily wind speed data, gathered via the Malaysian Meteorological Department at 14 measuring stations in Malaysia covering the period between 2000 and 2019, were used. The results showed that the average weekly wind speed had superior performance to the maximum and minimum wind speed prediction. In general, the GPR model could effectively predict the weekly wind speed of the target station using the measured data of other stations. Errors found in this model were within acceptable limits. The findings of this model were compared with the measured data, and only Kota Kinabalu station showed an unacceptable range of prediction. To investigate the prediction performance of the proposed model, two models were used as the comparison models: the BTs model and SVR model. Although the comparison of GPR with the BTs model at Kuching station showed slightly better performance for the BTs model in maximum and minimum wind speed prediction, the prediction outcomes of the other 13 stations showed better performance for the proposed GPR model. Moreover, the proposed model generated smaller prediction errors than the SVR model at all stations. [ABSTRACT FROM AUTHOR]

Solar power integration in electrical grids is complicated due to dependence on volatile weather conditions. To address this issue, continuous research and development is required to determine the best machine learning (ML) algorithm for PV solar power output forecasting. Existing studies have established the superiority of the artificial neural network (ANN) and random forest (RF) algorithms in this field. However, more recent studies have demonstrated promising PV solar power output forecasting performances by the decision tree (DT), extreme gradient boosting (XGB), and long short-term memory (LSTM) algorithms. Therefore, the present study aims to address a research gap in this field by determining the best performer among these 5 algorithms. A data set from the United States' National Renewable Energy Laboratory (NREL) consisting of weather parameters and solar power output data for a monocrystalline silicon PV module in Cocoa, Florida was utilized. Comparisons of forecasting scores show that the ANN algorithm is superior as the ANN16 model produces the best mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R2) with values of 0.4693, 0.8816 W, and 0.9988, respectively. It is concluded that ANN is the most reliable and applicable algorithm for PV solar power output forecasting. [ABSTRACT FROM AUTHOR]

The suspended sediment load (SSL) is one of the major hydrological processes affecting the sustainability of river planning and management. Moreover, sediments have a significant impact on dam operation and reservoir capacity. To this end, reliable and applicable models are required to compute and classify the SSL in rivers. The application of machine learning models has become common to solve complex problems such as SSL modeling. The present research investigated the ability of several models to classify the SSL data. This investigation aims to explore a new version of machine learning classifiers for SSL classification at Johor River, Malaysia. Extreme gradient boosting, random forest, support vector machine, multi-layer perceptron and k-nearest neighbors classifiers have been used to classify the SSL data. The sediment values are divided into multiple discrete ranges, where each range can be considered as one category or class. This study illustrates two different scenarios related to the number of categories, which are five and 10 categories, with two time scales, daily and weekly. The performance of the proposed models was evaluated by several statistical indicators. Overall, the proposed models achieved excellent classification of the SSL data under various scenarios. [ABSTRACT FROM AUTHOR]

Predicting the infiltration rate (IR) of treated wastewater (TWW) is essential in controlling clogging problems. Most researchers that predict the IR using neural network models considered the characteristics parameters of soil without considering those of TWW. Therefore, this study aims to develop a model for predicting the IR based on various combinations of TWW characteristics parameters (i.e. total suspended solids (TSS), biological oxygen demand (BOD), electric conductivity (EC), pH, total nitrogen (TN), total phosphorous (TP), and hydraulic loading rate (HLR)) as input parameters. Therefore, two different artificial neural network (ANN) architectures, multilayer perceptron model (MLP) and Elman neural network (ENN), were used to develop optimal model. The optimal model was selected through evaluating three stages: selecting the best division of data, selecting the best model, and deciding the best combination of input parameters based on several performance criteria. The study concluded that the first combination of inputs that include all the seven-parameter using MLP model associated with 90% division of data was the optimal model in predicting the IR depending on TWW characteristics parameters, achieving a promising result of 0.97 for the coefficient of determination, 0.97 for test regression, 0.012 for MSE with 32.4 of max relative percentage error. Abbreviations: IR: Infiltration Rate; TWW: Treated Wastewater; TSS: Total Suspended Solids; BOD: Biological Oxygen Demand; EC: Electric Conductivity; HC: Hydraulic Conductivity; TN: Total Nitrogen; TP: Total Phosphorous; HLR: Hydraulic Loading Rate; ANN: Artificial Neural Network; MLP: Multilayer Perceptron Model; ENN: Elman Neural Network; FFANN: Feedforward Artificial Neural Networks; R: Regression Values; SAR: Sodium Adsorption Ratio; DOC: Dissolved Organic Carbon; ANAMMOX: Anaerobic Ammonium Oxidation; CEC: Cation Exchange Capacity; BPNN: Back Propagation Neural Network; GRNN: General Regression Neural Networks; ELM: Extreme Learning Machine Neural Networks; TDNN: Time Delay Neural Network; TLRN: Time Lag Recurrent Network; NGWTP: North Gaza Wastewater Treatment Plant; MASL: Meters Above Sea Level; DNC: Dynamic Node Creation; PWA: Palestinian Water Authority; RBF: Radial Basis Function; ANFIS: Adaptive Neuro Fuzzy Inference System; BD: Bulk Density; RMSE: Root Mean Square Error; MAE: Mean Absolute Error; MSE: Mean Square Error; R2: Determination Coefficient; LLR: Local Linear Regression; DLLR: Dynamic Linear Regression; MNN: Modular Neural Networks; RNN: Recurrent Neural Network; NARX: Nonlinear Autoregressive with Exogenous input network; WNN: Wavelet Neural Networks [ABSTRACT FROM AUTHOR]

A reliable model to predict the changes in the water levels in a river is crucial for better planning to mitigate any risk associated with flooding. In this study, six different Machine Learning (ML) algorithms were developed to predict the river's water level, on a daily basis based on collected data from 1990 to 2019 which were used to train and test the proposed models. Different input combinations were explored to improve the accuracy of the model. Statistical indicators were calculated to examine the reliability of the proposed models with other models. The comparison of several data-driven regression methods indicate that the exponential Gaussian Process Regression (GPR) model offered better accuracy in predicting daily water levels with respect to different assessment criteria. The GPR model was then used to predict the water level after sorting the data based on 10 days maximum and minimum values of the water level, and the results proved the success of this model in catching the extremes of the water levels. In addition to that, based on two uncertainty indicators, it was concluded that the proposed model, the GPR, was capable of predicting the water level of the river with high precision and less uncertainty where the computed using the 95% prediction uncertainty (95PPU) and the d-factor were found to be equal to 98.276 and 0.000525, respectively. The findings of this study show the efficacy of the GPR model in capturing the changes in the water level in a river. Due to the importance of the water level of a river being an parameter for flood monitoring, this technique is likely beneficial to the design of the mitigation strategies for future flooding events. [ABSTRACT FROM AUTHOR]

The development of a river inflow prediction is a prerequisite for dam reservoir management. Precise forecasting leads to better irrigation water management, reservoir operation refinement, enhanced hydropower output and mitigation of risk of natural hazards such as flooding. Dam created reservoirs prove to be an essential source of water in arid and semi-arid regions. Over the years, Artificial Intelligence (AI) has been used for development of models for prediction of various natural variables in different engineering fields. Also, several AI models have been proved to be beneficial over the conventional models in efficient prediction of various natural variables. In this study, four AI models, namely, Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and Boosted Tree Regression (BTR) were developed and trained over 130-years of monthly historical rainfall data to forecast streamflow at Aswan High Dam, Egypt. The input parameters were selected according to the Autocorrelation Function (ACF) plot. The findings revealed that RF model outperformed other techniques and could provide precise monthly streamflow prediction with the lowest RMSE (2.2395) and maximum WI (0.998462), R2 (0.9012). The input combination for the optimum RF model was Qt-1, Qt-11, and Qt-12 (i.e., one-, eleven- and twelve-months delay inputs). The optimum RF model provides a reliable source of data for inflow predictions, which allows improved utilization of water resources and long-term water resource planning and management. [ABSTRACT FROM AUTHOR]