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Abstract This study addresses the critical need for effective groundwater (GW) management in Muzaffarabad, Pakistan, amidst challenges posed by rapid urbanization and population growth. By integrating Support Vector Machine (SVM) and Weight of Evidence (WOE) techniques, this study aimed to delineate GW potential zones and assess water quality. This study fills the gap in applying advanced machine learning and geostatistical methods for accurate GW potential mapping. Eight thematic layers based on topography, hydrology, geology, and ecology were utilized to compute the GW potential model. Additionally, water quality analysis was performed on collected samples. The findings indicate that flat and gently sloping terrains, areas with an elevation range of 611 –687 m, and concave slope geometries are associated with higher GW potential. Additionally, proximity to drainage and high-density lineament zones contribute to increased GW potential. The results showed that 31.1% of the area had excellent GW potential according to the WOE model, whereas the SVM model indicated that only 20.3% fell in the excellent potential zone. Results showed that both models performed well in the delineating GW potential zones. Nevertheless, the application of the SVM method is highly recommended which will be benefited in GW resources management related to urban planning. The study also evaluates the spatial distribution of GW quality, with a focus on physical and chemical parameters, including electrical conductivity, pH, turbidity, total dissolved solids, calcium, magnesium, chloride, nitrate, and sulphate. Bacterial contamination assessment reveals that 76% of spring water samples (30 out of 39 samples) are contaminated with E.coli, raising public health concerns. Based on the chemical analysis of GW samples the study identified exceedances of WHO guidelines for calcium in two samples, magnesium in seven samples, sulphate in ten samples, and nitrate levels were below the WHO guideline across all samples. These results highlight localized chemical contamination issues that require targeted remediation efforts to safeguard water quality for public health.

Abstract In India, the spatial coverage of air pollution data is not homogeneous due to the regionally restricted number of monitoring stations. In a such situation, utilising satellite data might greatly influence choices aimed at enhancing the environment. It is essential to estimate significant air contaminants, comprehend their health impacts, and anticipate air quality to safeguard public health from dangerous pollutants. The current study intends to investigate the spatial and temporal heterogeneity of important air pollutants, such as sulphur dioxide, nitrogen dioxide, carbon monoxide, and ozone, utilising Sentinel-5P TROPOMI satellite images. A comprehensive spatiotemporal analysis of air quality was conducted for the entire country with a special focus on five metro cities from 2019 to 2022, encompassing the pre-COVID-19, during-COVID-19, and current scenarios. Seasonal research revealed that air pollutant concentrations are highest in the winter, followed by the summer and monsoon, with the exception of ozone. Ozone had the greatest concentrations throughout the summer season. The analysis has revealed that NO2 hotspots are predominantly located in megacities, while SO2 hotspots are associated with industrial clusters. Delhi exhibits high levels of NO2 pollution, while Kolkata is highly affected by SO2 pollution compared to other major cities. Notably, there was an 11% increase in SO2 concentrations in Kolkata and a 20% increase in NO2 concentrations in Delhi from 2019 to 2022. The COVID-19 lockdown saw significant drops in NO2 concentrations in 2020; specifically, − 20% in Mumbai, − 18% in Delhi, − 14% in Kolkata, − 12% in Chennai, and − 15% in Hyderabad. This study provides valuable insights into the seasonal, monthly, and yearly behaviour of pollutants and offers a novel approach for hotspot analysis, aiding in the identification of major air pollution sources. The results offer valuable insights for developing effective strategies to tackle air pollution, safeguard public health, and improve the overall environmental quality in India. The study underscores the importance of satellite data analysis and presents a comprehensive assessment of the impact of the shutdown on air quality, laying the groundwork for evidence-based decision-making and long-term pollution mitigation efforts.

Abstract Background Peri-urbanization, the expansion of large metropolitan centers into adjacent peri-urban regions, is a growing concern due to land scarcity and escalating housing costs. These zones, a blend of rural and urban features, blur the line between urban and rural areas, creating new landscapes. This study examines historical, present, and potential growth trends in the peri-urban area surrounding Durgapur Municipal Corporation (DMC). Analytical techniques and spatial metrics are used to track development intensity changes over time, including built-up density, Shannon’s entropy, Landscape expansion index, Average Weighted Mean Expansion Index, Annual Built-Up Expansion Rate, Built-Up Expansion Intensity Index, and Built-Up Expansion Difference Index. Landscape indices like Patch Density, Edge Density, Landscape Shape Index, Largest Patch Index, Ratio of Open Space, and Area Weighted Mean Patch Fractal are used to understand fragmentation, connectivity, and spatial relationships. The Logistic Regression Model (LRM) is used to identify influencing factors and CA-Markov modeling for future built-up areas. Results Between 1991 and 2001, built-up area in the region increased significantly, primarily due to urban development near industrial zones, roadways, and mining areas. The growth was primarily concentrated in the western sector and near National Highway-2 (NH-2). Urban sprawl was a continuous trend, with the highest built-up density in the South-South-East (SSE) direction from 1991 to 2011. Additionally, a key determinant of built-up development was the distance to the city core. By 2031, the built-up area is expected to concentrate in the western and southeast regions, reaching 177.90 km2. Conclusions This expansion is attributed to urban development near industrial zones, roadways, mining areas, and other infrastructure. The study identifies distance to the city center as a significant influencing factor for built-up development. The results emphasize the need for inclusive urban planning methods prioritizing sustainable development principles and prudent resource management for future growth and efficient management in the DMC’s peri-urban area.

Agricultural Land Suitability Analysis plays a pivotal role in sustainable land use planning, aiding decision-makers in identifying areas most conducive to agriculture. This study employs a systematic approach integrating Analytical Hierarchy Process and Multi-Criteria Decision Analysis techniques to assess and prioritize the suitability of agricultural land in in Southern Punjab (Multan region). The methodology involves defining clear objectives, identifying relevant criteria and sub-criteria, establishing a hierarchical structure and conducting pairwise comparisons to determine the relative importance of each factor. Our outcomes indicated that almost 43% area was highly suitable for agriculture, 27% moderately suitable, 16% marginally suitable, 8% less suitable and 6% not suitable for agriculture in study area. All the suitable lands had silty clay or clay type soil, which had sandy loam type soil in Multan region. The output is a comprehensive suitability map that identifies the most suitable areas for agriculture in Multan region. Sensitivity analysis and validation are incorporated to enhance the robustness and reliability of the results. This methodology provides a valuable tool for land use planners and policymakers to make informed decisions regarding agricultural land allocation, contributing to sustainable agricultural practices and resource management.

Soil erosion significantly impacts dam functionality by leading to reservoir siltation, reducing capacity, and heightening flood risks. This study aims to map soil erosion within a Geographic Information Systems (GIS) framework to estimate the siltation of the K'sob dam and compare these estimates with bathymetric observations. Focused on one of the Hodna basin’s sub-basins, the K'sob watershed (1477 km2), the assessment utilizes the Revised Universal Soil Loss Equation (RUSLE) integrated with GIS and remote sensing data to predict the spatial distribution of soil erosion. Remote sensing data were pivotal in updating land cover parameters critical for RUSLE, enhancing the precision of our erosion predictions. Our results indicate an average annual soil erosion rate of 7.83 t/ha, with variations ranging from 0 to 224 t/ha/year. With a typical relative error of about 13% in predictions, these figures confirm the robustness of our methodology. These insights are crucial for crafting mitigation strategies in areas facing high to extreme soil loss and will assist governmental agencies in prioritizing actions and formulating effective soil erosion management policies. Future studies should explore the integration of real-time data and advanced modeling techniques to further refine these predictions and expand their applicability in similar environmental assessments.

Abstract In light of population growth and climate change, groundwater is one of the most important water resources globally. Groundwater is crucial for sustaining many vital sectors in Syria, including industrial and agricultural sectors. However, groundwater exploitation has significantly escalated to meet different water needs especially in the post-war period and the earthquake disaster. Therefore, the goal was this study delineation of the groundwater potential zones (GPZs) by integrating the analytic hierarchy process (AHP) method in a geographic information systems (GIS) within the AlAlqerdaha river basin in western Syria. In this study, ten criteria were used to map the spatial distribution of GPZs, including slope, geomorphology, drainage density, land use/land cover (LU/LC), lineament density, lithology, rainfall, soil, curvature and topographic wetness index (TWI). GPZs map was validated by using the location of 74 wells and the Receiver Operating Characteristic Curve (ROC). The findings suggest that the study area is divided into five GPZs: very low, 21.39 km2 (10.87%); low, 52.45 km2 (26.65%); moderate, 65.64 km2 (33.35%); high, 40.45 km2 (20.55%) and very high, 16.90 km2 (8.58%). High and very high zones mainly corresponded to the western regions of the study area. The conducted spatial modeling indicated that the AHP-based GPZs map showed a remarkably acceptable correlation with wells locations (AUC = 87.7%, n = 74), demonstrating the precision of the AHP–GIS as a rating method. The results of this study provide objective and constructive outputs that can help decision-makers to optimally manage groundwater resources in the post-war phase in Syria.

Abstract Champhai, the rice bowl of Mizoram, is known for wet rice practices. Rapid urbanization and global climate change increased the demand for groundwater. Champhai city, being a hilly township in northeast India, is very difficult to identify the potential groundwater water availability. The present study aimed at groundwater potentiality zonation in Champhai town. For these purposes ten (10) factors from topographic, climatic, hydrological and other conditions were assigned weightage based on the AHP (analytical hierarchy process) technique. The aspect was given maximum weightage as the aspect working as the controlling factors of precipitation intensity, vegetation and soil type, while LULC (land use land cover) was assigned minimum weightage that unscientific human activities reduced infiltration rate. Subsequently, an addition of ten weighted rasters was performed for groundwater potentiality zonation. As a result, it was seen that the maximum part of the study area covered low potentiality zones (31.88%), whereas high and very high potential zone covered 14.73% and 5.08, respectively. A scientific validation technique, namely AUC (area under curve) value of 0.737, denoted a good classified groundwater water potentiality zone. Further, the single-layer sensitivity analysis highlighted that elevation, slope, aspect, infiltration number, distance to streams and rainfall had the maximum effective factors based on their effective weightage. The map removal sensitivity analysis highlighted that TWI (Topographic Wetness Index) and aspect had the maximum influence on the model. However, in a hilly environment like Champhai, the output of the current study will work as a blueprint for planning and decision making.

A retrospective review of three patients with mycosis fungoides treated with total skin electron beam therapy , from the year 2007 to 2009 , at the Oncology department, Ziauddin University Hospital. Key Words: Mycosis Fungoides, Primary Cutaneous T-Cell Lymphoma, Total Skin Electron Beam Therapy.

Abstract Accurate and in-time prediction of crop yield plays a crucial role in the planning, management, and decision-making processes within the agricultural sector. In this investigation, utilizing area under irrigation (%) as an exogenous variable, we have made an exertion to assess the suitability of different hybrid models such as ARIMAX (Autoregressive Integrated Moving Average with eXogenous Regressor)–TDNN (Time-Delay Neural Network), ARIMAX–NLSVR (Non-Linear Support Vector Regression), ARIMAX–WNN (Wavelet Neural Network), ARIMAX–CNN (Convolutional Neural Network), ARIMAX–RNN (Recurrent Neural Network) and ARIMAX–LSTM (Long Short Term Memory) as compared to their individual counterparts for yield forecasting of major Rabi crops in India. The accuracy of the ARIMA model has also been considered as a benchmark. Empirical outcomes reveal that the ARIMAX–LSTM hybrid modeling combination outperforms all other time series models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) values. For these models, an average improvement of RMSE and MAPE values has been observed to be 10.41% and 12.28%, respectively over all other competing models and 15.83% and 18.42%, respectively over the benchmark ARIMA model. The incorporation of the area under irrigation (%) as an exogenous variable in the ARIMAX framework and the inbuilt capability of the LSTM model to process complex non-linear patterns have been observed to significantly enhance the accuracy of forecasting. The performance supremacy of other hybrid models over their individual counterparts has also been evident. The results also suggest avoiding any performance generalization of individual models for their hybrid structures.

This study aims to quantitatively and qualitatively assess the impact of urbanisation on the urban ecosystem in the city of Abha, Saudi Arabia, by analysing land use changes, urbanisation processes and their ecological impacts. Using a multidisciplinary approach, a novel remote sensing-based urban ecological condition index (RSUSEI) will be developed and applied to assess the ecological status of urban surfaces. Therefore, the identification and quantification of urbanisation is important. To do so, we used hyper-tuned artificial neural network (ANN) as well as Land Cover Change Rate (LCCR), Land Cover Index (LCI) and Landscape Expansion Index (LEI). For the development of (RSUSEI), we have used four advanced models such as fuzzy Logic, Principle Component Analysis (PCA), Analytical Hierarchy Process (AHP) and fuzzy Analytical Hierarchy Process (FAHP) to integrate various ecological parameters. In order to obtain more information for better decision making in urban planning, sensitivity and uncertainty analyses based on a deep neural network (DNN) were also used. The results of the study show a multi-layered pattern of urbanisation in Saudi Arabian cities reflected in the LCCR, indicating rapid urban expansion, especially in the built-up areas with an LCCR of 0.112 over the 30-year period, corresponding to a more than four-fold increase in urban land cover. At the same time, the LCI shows a remarkable increase in ‘built-up’ areas from 3.217% to 13.982%, reflecting the substantial conversion of other land cover types to urban uses. Furthermore, the LEI emphasises the complexity of urban growth. Outward expansion (118.98 km2), Edge-Expansion (95.22 km2) and Infilling (5.00 km2) together paint a picture of a city expanding outwards while filling gaps in the existing urban fabric. The RSUSEI model shows that the zone of extremely poor ecological condition covers an area of 157–250 km2, while the natural zone covers 91–410 km2. The DNN based sensitivity analysis is useful to determine the optimal model, while the integrated models have lower input parameter uncertainty than other models. The results of the study have significant implications for the management of urban ecosystems in arid areas and the protection of natural habitats while improving the quality of life of urban residents. The RSUSEI model can be used effectively to assess urban surface ecology and inform urban management techniques.

Soil erosion poses a significant threat to sustainable land management and agricultural productivity. Addressing this issue requires advanced predictive models that can accurately identify areas at risk and inform soil conservation strategies. This study focuses on the development and interpretation of well-optimized deep learning (DL) models to predict soil erosion probability, aiming to enhance decision-making in land management. Utilizing the Revised Universal Soil Loss Equation (RUSLE) in conjunction with ground-truthing, we identified critical erosion-prone areas. To predict soil erosion probability, we employed Bayesian optimization to fine-tune Deep Neural Network (DNN), Convolutional Neural Network (CNN), Fully Connected Neural Network (FCNN), and DNN-CNN hybrid models. These DL models were verified using a set of metrics. SHAP value analysis as a means of explainable artificial intelligence (XAI) was used to interpret these DL models for better decision-making. RUSLE estimations and ground truthing highlight that Soil erosion rates in the northeastern and northwestern regions are nearing the highest observed at 25 tonnes per hectare annually, largely due to steep slopes and limited vegetation. In contrast, the southern and southeastern areas have lower erosion rates, due to denser vegetation and gentler slopes. Deep learning models, optimized using Bayesian methods, demonstrate high performance in spatially modeling soil erosion probability. The DNN model achieved an accuracy of 0.93, a precision of 0.92, and an F1-score of 0.94, identifying 222.73 sq. km as highly susceptible to erosion, which indicates its strong ability to detect true erosion events. The CNN model identified 49.68% of the study area (503.30 sq. km) as high-risk, with an accuracy of 0.90 and a precision of 0.91. The FCNN model showed a balanced risk distribution, indicating 37.04% of the land (375.25 sq. km) as very low risk and 36.43% (369.11 sq. km) as very high risk, with an accuracy of 0.91. The DNN-CNN hybrid model highlighted 41.58% of the area (421.20 sq. km) as high risk, demonstrating its effectiveness in capturing spatial patterns of erosion susceptibility. SHAP value analysis indicates that land use and soil type (LULC and K-factor) are crucial in erosion predictions, with LULC having a significant predictive influence in the DNN model. These insights facilitate the prioritization of soil conservation measures, enabling decision-makers to focus on the most impactful factors for mitigating soil erosion.

In the face of rapid urbanization in Saudi Arabia, understanding the impact of landscape changes on land surface temperature (LST) is crucial for sustainable urban planning. This study assesses the influence of landscape morphology on LST and predicts future LST changes. A multi-temporal land use and land cover (LULC) analysis using random forest (RF) quantified urban expansion and its ecological impacts. Accuracy assessment using the kappa coefficient illustrated the precision of the classification techniques. Morphological Spatial Pattern Analysis (MSPA), developed in Python, analyzed the structural evolution of urban areas, vegetation and exposed rocks. Polynomial regression models established the relationship between landscape morphology and LST and predicted future temperature trends. Results for Asir show a significant improvement in accuracy of the LULC models over three decades, with overall accuracy increasing from 89.99% in 1990 to 91.72% in 2020. The bootstrapping trend analysis showed an urban expansion with a positive slope of 9.27 and a decline in water bodies with a negative slope of −0.03. The MSPA analysis reflected a significant urban expansion, with the core area growing from 45.19 km2 in 2001 to 230.33 km2 in 2021. The vegetated areas showed resilience and dynamic connectivity despite slight reduction and fragmentation. The polynomial regression predicted an increase in future average LST by 2030, with urban core areas reaching 58.47 °C, vegetated cores 42.19 °C and exposed rock cores 55.79 °C. These results highlight the link between urban expansion and LST rise and make the case for integrating green infrastructure and cooling strategies into urban development.

We aim to explore the seasonal influences of meteorological factors on COVID-19 era over two distinct locations in Bangladesh using a generalized linear model (GLM) and wavelet analysis. GLM model findings show that summer humidity drives COVID-19 transmission to coastal and inland locations. During the summer in the coastal area, a 1 °C earth’s skin temperature increase causes a 41.9% increase in COVID (95% CL 86.32%-2.54%) transmission compared to inland. Relative humidity was recorded as the highest at 73.97% (95% CL, 99.3%, and 48.63%) for the coastal region, while wind speed and precipitation reduced confirmed cases by −38.62% and −22.15%, respectively. Wavelet analysis showed that coastal meteorological parameters were more coherent with COVID-19 than inland ones. The outcomes of this study are consistent with subtropical climate regions. Seasonality and climatic similarity should address to estimate COVID-19 trends. High societal concern and strong public health measures may decrease meteorological effect on COVID-19.

AbstractBangladesh has a high risk of earthquakes because the Dauki, Jamuna, and Chittagong-Myanmar faults are still active. However, the assessment of seismicity remains a big challenge due to the complex geologic setting of Bangladesh. This study employed the Guttenberg-Richter relationship and the spectral models to assess and analyze the earthquake conditions in Bangladesh. Besides, an instrumental earthquake catalogue, obtained from the Bangladesh Meteorological Department (BMD), covering 1985–2017, is established. The results revealed that the Guttenberg-Richter constants of a and b were 2.981 and 0.392, which propagated a strain release from 1992 to 2017. The spectral model analyses, e.g. wavelet transform (WT), short-time Fourier transformation (STFT), and multitaper model (MTM), demonstrated the magnitude and strain release anomalies of the same magnitude ranging from 4.8 to 5.7, indicating the probable precursor of an upcoming earthquake. Notably, magnitudes have been running around 4.5–5.8, which may act as a signal to major earthquakes that have not been evident before. The proposed models allowed for the completion of the Bangladesh earthquake catalogue and provided a platform for future seismicity assessment and earthquake probability analysis. These results should be considered in determining how likely earthquakes are to happen in an area or region.

Soil erosion is a major problem in arid regions, including the Abha-Khamis watershed in Saudi Arabia. This research aimed to identify the soil erosional probability using various soil erodibility indices, including clay ratio (CR), modified clay ratio (MCR), Critical Level of Soil Organic Matter (CLOM), and principle component analysis based soil erodibility index (SEI). To achieve these objectives, the study used t-tests and an artificial neural network (ANN) model to identify the best SEI model for soil erosion management. The performance of the models were then evaluated using R2, Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE), with CLOM identified as the best model for predicting soil erodibility. Additionally, the study used Shapley additive explanations (SHAP) values to identify influential parameters for soil erosion, including sand, clay, silt, soil organic carbon (SOC), moisture, and void ratio. This information can help to develop management strategies oriented to these parameters, which will help prevent soil erosion. The research showed notable distinctions between CR and CLOM, where the 25–27% contribution explained over 89% of the overall diversity. The MCR indicated that 70% of the study area had low erodibility, while 20% had moderate and 10% had high erodibility. CLOM showed a range from low to high erodibility, with 40% of soil showing low CLOM, 40% moderate, and 20% high. Based on the T-test results, CR is significantly different from CLOM, MCR, and principal component analysis (PCA), while CLOM is significantly different from MCR and PCA, and MCR is significantly different from PCA. The ANN implementation demonstrated that the CLOM model had the highest accuracy (R2 of 0.95 for training and 0.92 for testing) for predicting soil erodibility, with SOC, sand, moisture, and void ratio being the most important variables. The SHAP analysis confirmed the importance of these variables for each of the four ANN models. This research provides valuable information for soil erosion management in arid regions. The identification of soil erosional probability and influential parameters will help to develop effective management strategies to prevent soil erosion and promote agricultural production. This research can be used by policymakers and stakeholders to make informed decisions to manage and prevent soil erosion.

Abstract The present study aimed to create novel hybrid models to produce groundwater potentiality models (GWP) in the Teesta River basin of Bangladesh. Six ensemble machine learning (EML) algorithms, such as random forest (RF), random subspace, dagging, bagging, naïve Bayes tree (NBT), and stacking, coupled with fuzzy logic (FL) models and a ROC-based weighting approach have been used for creating hybrid models integrated GWP. The GWP was then verified using both parametric and nonparametric receiver operating characteristic curves (ROC), such as the empirical ROC (eROC) and the binormal ROC curve (bROC). We conducted an RF-based sensitivity analysis to compute the relevancy of the conditioning variables for GWP modeling. The very high and high groundwater potential regions were predicted as 831–1200 km2 and 521–680 km2 areas based on six EML models. Based on the area under the curve of the ROC, the NBT (eROC: 0.892; bROC: 0.928) model outperforms rest of the models. Six GPMs were considered variables for the next step and turned into crisp fuzzy layers using the fuzzy membership function, and the ROC-based weighting approach. Subsequently four fuzzy logic operators were used to assimilate the crisp fuzzy layers, including AND, OR, GAMMA0.8, and GAMMA 0.9, as well as GAMMA0.9. Thus, we created four hybrid models using FL model. The results of the eROC and bROC curve showed that GAMMA 0.9 operator outperformed other fuzzy operators-based GPMs in terms of accuracy. According to the validation outcomes, four hybrid models outperformed six EML models in terms of performance. The present study will aid in enhancing the efficiency of GPMs in preparing viable planning for groundwater management.

Abstract The present research aims to build a unique ensemble model based on a high-resolution groundwater potentiality model (GPM) by merging the random forest (RF) meta classifier-based stacking ensemble machine learning method with high-resolution groundwater conditioning factors in the Bisha watershed, Saudi Arabia. Using high-resolution satellite images and other secondary sources, twenty-one parameters were derived in this study. SVM, ANN, and LR meta-classifiers were used to create the new stacking ensemble machine learning method. RF meta classifiers were used to create the new stacking ensemble machine learning algorithm. Each of these three models was compared to the ensemble model separately. The GPMs were then confirmed using ROC curves, such as the empirical ROC and the binormal ROC, both parametric and non-parametric. Sensitivity analyses of GPM parameters were carried out using an RF-based approach. Predictions were made using six hybrid algorithms and a new hybrid model for the very high (1835–2149 km2) and high groundwater potential (3335–4585 km2) regions. The stacking model (ROCe-AUC: 0.856; ROCb-AUC: 0.921) beat other models based on ROC's area under the curve (AUC). GPM sensitivity study indicated that NDMI, NDVI, slope, distance to water bodies, and flow accumulation were the most sensitive parameters. This work will aid in improving the effectiveness of GPMs in developing sustainable groundwater management plans by utilizing DEM-derived parameters.

IntroductionNatural hazards such as landslides and floods have caused significant damage to properties, natural resources, and human lives. The increased anthropogenic activities in weak geological areas have led to a rise in the frequency of landslides, making landslide management an urgent task to minimize the negative impact. This study aimed to use hyper-tuned machine learning and deep learning algorithms to predict landslide susceptibility model (LSM) and provide sensitivity and uncertainty analysis in Aqabat Al-Sulbat Asir region of Saudi Arabia.MethodsRandom forest (RF) was used as the machine learning model, while deep neural network (DNN) was used as the deep learning model. The models were hyper-tuned using the grid search technique, and the best hypertuned models were used for predicting LSM. The generated models were validated using receiver operating characteristics (ROC), F1 and F2 scores, gini value, and precision and recall curve. The DNN based sensitivity and uncertainty analysis was conducted to analyze the influence and uncertainty of the parameters to the landslide.ResultsResults showed that the RF and DNN models predicted 35.1–41.32 and 15.14–16.2 km2 areas as high and very high landslide susceptibility zones, respectively. The area under the curve (AUC) of ROC curve showed that the LSM by the DNN model achieved 0.96 of AUC, while the LSM by RF model achieved 0.93 of AUC. The sensitivity analysis results showed that rainfall had the highest sensitivity to the landslide, followed by Topographic Wetness Index (TWI), curvature, slope, soil texture, and lineament density.DiscussionRoad density and geology map had the highest uncertainty to the landslide prediction. This study may be helpful to the authorities and stakeholders in proposing management plans for landslides by considering potential areas for landslide and sensitive parameters.

Urbanization and land-use change are significant contributors to cumulative greenhouse gas emissions, causing climate change and environmental damage. Research on land-use-derived carbon storage and sequestration capability is crucial for emission reduction strategy and sustainable development. Because cities are becoming significant sources of carbon emissions, an accurate assessment of city-level carbon storage and sequestration is now required. This study intended to estimate the carbon storage and sequestration capabilities using the InVEST model under different land use land cover (LULC) classes in Abha, Saudi Arabia. We used a support vector machine (SVM) for LULC mapping. For the future forecasting of the LULC map, we employed cellular automata (ANN-CA). Expansion of built-up land is the primary source of CO2. From 1990 to 2040, agriculture lost 145 megagram of carbon per hectare (Mg C/ha), whereas vegetation lost 34,948.15 Mg C/ha. Vegetation would get 17,363.7 Mg C/ha during the same period, whereas urban areas would receive 3924.1 Mg C/ha. Approximately 17,000 Mg C/ha would be lost from the vegetation area in future periods. The approaches outlined in this paper may add a new dimension to carbon emission analyses and provide helpful information for low-carbon city.

There are a number of wastewater studies, however research work of hospital wastewater is still lacking. This is owing to low volume of wastewater as compared to municipal wastewater. Nevertheless, hospital wastewater is very distinct in nature as compared to wastewater of other origins particularly owing to various hospital related activities. Hence this study evaluated treatment performance of hospital wastewater using Submerged Membrane bioreactor (SMBR) and Extended aeration process. The investigation parameters for both treatment process was kept same for comparison of treatment efficiency of both processes. Additionally, Tubesettler was employed to enhance treatment efficiency. The treatment efficiency of both processes was low. However, encoupled with Tubesettler satisfactory results were obtained. The BOD5/COD ratio for SMBR effluent varied between 0.22 and 0.92 while for extended aeration process ratio ranged between 0.29 and 0.92. The increase in nitrate concentration in SMBR and EA indicated nitrification. But since the system was well aerated and no anoxic or anaerobic condition prevailed denitrification was not observed. The results of this study suggested that using hospital wastewater as seeding and as influent provided lesser efficiency as compared to synthetic wastewater as influent. Also 85% COD reduction and 91% BOD5 removal in EA indicated better performance as compared to SMBR with 72% COD reduction and 78% BOD5 removal. However, for nitrification SMBR (295%) performed better than EA (140%) Future studies are required to establish conventional parameters with pharmaceutical removal, variation of control parameters on treatment efficiency and effect on microbial activities.

Rapid urban land use and land cover changes have become a major environmental issue because of their ecological effects, including loss of green space and urban heat islands. Effective monitoring and management techniques are required. The Saudi Arabian twin city of Abha-Khamis Mushyet was selected as a case study for this research. As a result, the current study aimed to statistically and spatially investigate the relationship between land surface temperature (LST) and land use land cover based urban biophysical parameters such as normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI). This study used random forest (RF) to classify LULC in 1990, 2000, and 2018. We also validated the LULC maps in a novel way. Using mono window algorithm techniques, we extracted LST for three periods. The dynamics of LULC, LST, and biophysical parameters were investigated using standard statistical graphs such as the heat map and the Sankey diagram. The correlation coefficient and the global bivariate Moran’ I approach were used to determine the association between LST and urban biophysical parameters. The relationship was then established in greater detail by categorizing the entire pixel into percentile classes and employing parallel coordinate plots. Finally, the association was built using GeoDA software and a conditional map. The LULC maps revealed a 334.4 percent increase in urban areas between 1990 and 2018. The built-up region is the largest stable LULC, with an 83.6 percent transitional probability matrix between 1990 and 2018. While 17.9%, 21.8%, 12.4%, and 10.5% of agricultural land, scrubland, exposed rocks, and water bodies were converted to built-up areas, respectively. The LST has increased rapidly over time because of LULC changes. The link between LST and urban biophysical parameters revealed that NDBI had a positive relationship, whereas NDWI and NDVI had a negative relationship. As a result, this study could be very important because it could help decision makers figure out how to lessen the effects of urban heat islands because of changes in LULC.

The finite element technique has been accepted as a tool for modeling geotechnical complex processes. In this study, finite element (FE) modeling of various stages of the soil-nailing process, i.e., construction stages and overburden pressure stages, is carried out considering different soil parameters, simulating with in-house developed laboratory models. The soil-nailing process built in laboratory models is idealized as a plain strain problem and modeled in PLAXIS software. The laboratory models of the soil-nailing process consist of a Perspex sheet box containing a sandy soil slope, a Perspex sheet facing, steel bars as reinforcement and a steel plate as foundation. The stress–strain relationship of the sand is represented by a Hardening-Soil model. The interface at the soil and nail is described by the Coulomb friction model. The behavior of the soil-nailing process, during the construction stage and under varying overburden pressure and varying soil density, are investigated in terms of displacements of slope and stress conditions in slope soil mass. The slope displacements and stress conditions in slope soil mass are all well presented by the FE modeling and compared with laboratory model test data. The sensitivity analysis of the laboratory models’ dimensions is carried out by three-dimensional modeling of the nailed-soil slope. It can be concluded that the developed finite element model has the potential to simulate the performance of a field nailed-soil slope during construction and working stages and could provide guidance for the construction/maintenance of soil-nailed cut slopes in granular soils/weathered rocks.

Land-use and land-cover (LULC) changes have become a crucial issue that urgently needs to be addressed due to global environmental change. Many studies have employed remote sensing data for assessing LULC changes, however, the investigation of fragmentation probability modeling is still scarce in the existing literature. Thus, the coupling of bagging, random forest (RF), random subspace (RSS), and their ensemble model with multi-temporal datasets within the GIS environment makes it possible to model the fragmentation probability of LULC in the Teesta River Basin (TRB), Bangladesh. The number of patch (NP), edge density (ED), largest patch index (LPI), contagion index (%) (CONTAG), aggregation index (AI), perimeter area ratio (P/A ratio), the class area (CA), percentage of landscape (PLAND), patch density (PD), total edge (TE), largest shape index (LSI) and total core area (TCA) were the landscape and class matrices, which were derived from the LULC maps using FRAGSTATS software. The machine learning-based sensitivity models, such as decision tree and support vector machine-based feature selection techniques were implemented to explore the influence of the parameters for fragmentation probability modeling. The results showed that water bodies and barren land were substantially decreased by (6.21%), and (14.59%) respectively while the built-up areas increased by 1.45% from 2010 to 2019. Results revealed that the dominance of the agricultural area has been increased as human interference has been elevated in the TRB. However, twelve class-level and landscape matrices were used to delineate the fragmentation probability zone with the aid of bagging, RF, and RSS algorithms. LULC images and fragmentation probability models were validated using the kappa coefficient and the area under curve (AUC) of the receiver operating characteristics (ROC). The validation outcomes depicted that the three models such as bagging (AUC = 0.864), RF (AUC = 0.819), RSS (AUC = 0.859), and ensemble model (AUC = 0.912) have a good capability to appraise the fragmentation probability, and ensemble model has the highest precision level among three models. Nearly 49% (1789 km2) of the LULC was under a high to very high fragmentation potential zone that requires to be protected with direct measures. The results of sensitivity analysis showed that the number of patches significantly influenced the fragmentation probability model, while the largest patch index was the least sensitive parameter for modeling.

Geopolymer concrete (GPC) is a new material in the construction industry, with different chemical compositions and reactions involved in a binding material. The pozzolanic materials (industrial waste like fly ash, ground granulated blast furnace slag (GGBFS), and rice husk ash), which contain high silica and alumina, work as binding materials in the mix. Geopolymer concrete is economical, low energy consumption, thermally stable, easily workable, eco-friendly, cementless, and durable. GPC reduces carbon footprints by using industrial solid waste like slag, fly ash, and rice husk ash. Around one tonne of carbon dioxide emissions produced one tonne of cement that directly polluted the environment and increased the world’s temperature by increasing greenhouse gas production. For sustainable construction, GPC reduces the use of cement and finds the alternative of cement for the material’s binding property. So, the geopolymer concrete is an alternative to Portland cement concrete and it is a potential material having large commercial value and for sustainable development in Indian construction industries. The comprehensive survey of the literature shows that geopolymer concrete is a perfect alternative to Portland cement concrete because it has better physical, mechanical, and durable properties. Geopolymer concrete is highly resistant to acid, sulphate, and salt attack. Geopolymer concrete plays a vital role in the construction industry through its use in bridge construction, high-rise buildings, highways, tunnels, dams, and hydraulic structures, because of its high performance. It can be concluded from the review that sustainable development is achieved by employing geopolymers in Indian construction industries, because it results in lower CO2 emissions, optimum utilization of natural resources, utilization of waste materials, is more cost-effective in long life infrastructure construction, and, socially, in financial benefits and employment generation.

A soil nailing system is a proven effective and economic method used to stabilize earth slopes from the external (factors increasing the shear stress) and internal (factors decreasing material strength) failure causes. The laboratory models with scales of 1:10 are used to study the behavior of nailed soil slope with different soil and building foundation parameters. The models consist of Perspex strips as facing and steel bars as a nailing system to increase the stability of the soil slope. The models of sand beds are formed using an automatic sand raining system. Devices and instruments are installed to monitor the behavior of soil-nailed slope during and after construction. The effect of the soil type, soil slope angle, foundation width and position on the force mobilized in the nail, lateral displacement of the slope, settlement of the foundation and the earth pressure at the slope face, under and behind the soil mass at various foundation pressures, has been observed. It is found that the increase of soil density reduces both slopes facing displacement and building foundation settlements. The slope face displacement and footing settlement will increase with an increase in the width of the foundation and foundation position near the crest of the slope.

Ranking of vegetable cash crop is an important factor in the sustainable green chamber farming practices. In sustainable farming, a careful strategy based on the multi-criteria decision making is required. The present research stresses the cash crop ranking in the Saudi Arabia and takes into account wide standards linked with sustainable green chamber vegetable cash crop harvest. Considering the specialists' opinion and wide-ranging review of the literature, important criteria connected to sustainable green chamber agricultural practices apply for cash crops harvest. The twelve important criteria were chosen based on environmental conditions, social, economicand nutrient of soil for ranking the vegetable cash crops in the green chamber system modelled to attain the sustainability for the vegetable cash crop production. A mathematical model has been developed keeping in account the fuzzy-based multi-criteria method (FMCM). The model will also help the different stakeholder involved in the green chamber harvesting system and ministry of agriculture to develop the policies related to sustainable green harvesting practices and also improve the self-sufficiency of the farming sector subsequently improve the national gross domestic product.

The present paper proposes a novel fuzzy-VORS (vigor, organization, resilience, ecosystem services) model by integrating fuzzy logic and a VORS model to predict ecosystem health conditions in Abha city of Saudi Arabia from the past to the future. In this study, a support vector machine (SVM) classifier was utilized to classify the land use land cover (LULC) maps for 1990, 2000, and 2018. The LULCs dynamics in 1990–2000, 2000–2018, and 1990–2018 were computed using delta (Δ) change and Markovian transitional probability matrix. The future LULC map for 2028 was predicted using the artificial neural network-cellular automata model (ANN-CA). The machine learning algorithms, such as random forest (RF), classification and regression tree (CART), and probability distribution function (PDF) were utilized to perform sensitivity analysis. Pearson’s correlation technique was used to explore the correlation between the predicted models and their driving variables. The ecosystem health conditions for 1990–2028 were predicted by integrating the fuzzy inference system with the VORS model. The results of LULC maps showed that urban areas increased by 334.4% between 1990 and 2018. Except for dense vegetation, all the natural resources and generated ecosystem services have been decreased significantly due to the rapid and continuous urbanization process. A future LULC map (2028) showed that the built-up area would be 343.72 km2. The new urban area in 2028 would be 169 km2. All techniques for sensitivity analysis showed that proximity to urban areas, vegetation, and scrubland are highly sensitive to land suitability models to simulate and predict LULC maps of 2018 and 2028. Global sensitivity analysis showed that fragmentation or organization was the most sensitive parameter for ecosystem health conditions.

The pullout resistance and displacement performance of reinforcement have significant effects on the safe and economic design of a reinforced-soil system. In this study, the nail pullout tests are conducted to assess the pullout behavior of soil nail reinforcement at different levels in the soil slope of granular materials. The similitude laboratory models of a reinforced soil system with a scale of 1:10 are prepared. The construction sequence used in a full scale slope was precisely followed in the laboratory model. The models consist of a Perspex wall box filled with sand and steel bars as a reinforcement. The models of sand beds are formed using an automatic sand raining system. Devices and instruments are installed to record the nails pullout resistance and displacement. The tests are carried out at variable footing pressures to get the pullout force of the nails based on a strain control technique. The finite element models of nailed soil slope are also analyzed to validate the laboratory model results. It infers from the numerical model results that the laboratory models underestimate the pullout behavior of nail reinforcement in nailed soil slope. The pull-out force in nail reinforcement increases as the displacement increases and then decreases slightly and becomes constant with an increase in displacement in the case of deeper placed nails, but it becomes constant immediately for upper nails.

The Normalized Difference Vegetation Index (NDVI) and rainfall data were used to model the spatial relationship between vegetation and rainfall. Their correlation in previous studies was typically based on a global regression model, which assumed that the correlation was constant across space. The NDVI–rainfall association, on the other hand, is spatially non-stationary, non-linear, scale-dependent, and influenced by local factors (e.g., soil background). In this study, two statistical methods are used in the modeling, i.e., traditional ordinary least squares (OLS) regression and geographically weighted regression (GWR), to evaluate the NDVI–rainfall relationship. The GWR was implemented annually in the growing seasons of 2000 and 2016, using climate data (Normalized Vegetation Difference Index and rainfall). The NDVI–rainfall relationship in the studied Bisha watershed (an eco-sensitive zone with a complex landscape) was found to have a stable operating scale of around 12 km. The findings support the hypothesis that the OLS model’s average impression could not accurately represent local conditions. By addressing spatial non-stationarity, the GWR approach greatly improves the model’s accuracy and predictive ability. In analyzing the relationship between NDVI patterns and rainfall, our research has shown that GWR outperforms a global OLS model. This superiority stems primarily from the consideration of the relationship’s spatial variance across the study area. Global regression techniques such as OLS can overlook local details, implying that a large portion of the variance in NDVI is unexplained. It appears that rainfall is the most significant factor in deciding the distribution of vegetation in these regions. Furthermore, rainfall had weak relationships with areas predominantly located around wetlands, suggesting the need for additional factors to describe NDVI variations. The GWR method performed better in terms of accuracy, predictive power, and reduced residual autocorrelation. Thus, GWR is recommended as an explanatory and exploratory technique when relations between variables are subject to spatial variability. Since the GWR is a local form of spatial analysis that aligned to local conditions, it has the potential for more accurate prediction; however, a larger amount of data is needed to allow a reliable local fitting.

The earth nailing system is a ground improvement technique used to stabilize earth slopes. The behavior of the earth nailing system is dependent on soil and nailing characteristics, such as the spacing between nails, the orientation, length, and method of installation of nails, soil properties, slope height and angle, and surcharge loading, among others. In the present study, a three-dimensional physical model was built to simulate a soil nailed slope with a model scale of 1:10 with various soil nail characteristics. The simulated models consist of Perspex strips as facing and steel bars as a reinforcing system to stabilize the soil slope. Sand beds in the model were formed, using a sand raining system. The performance of nailed soil slope models under three important nails characteristics, i.e., length, spacing and orientation, with varying surcharge loading were studied. It was observed that there is a reduction in the lateral movement of slope and footing settlements with an increase in length. It was found that the slope face horizontal pressure is non-linear with different nail characteristics. The increase in length and inclination of the soil nails decreased the vertical, horizontal stress and footing settlement, while the increase in spacing of the nails increased the vertical and horizontal stress behind the soil mass.

The deflection and cracking behavior of concrete structure depend on the flexural tensile strength of concrete. Many factors have been shown to influence the flexural tensile strength of concrete, particularly the level of stress, size, age and confinement to concrete flexure member, etc. The concrete members, in general, are of large continuous size and have at least minimum reinforcement introducing a confining effect to the concrete. The confining reinforcement increases ductility and large deflections in structures provide a good warning of failure prior to complete failure of the flexure member and also for efficient use of constructional material, it is desirable to take full advantage of long-term strength gain. Therefore, the effect of the factors like level of stress, age and confinement of concrete member should be given prime importance while studying the flexure tensile strength of concrete. This paper presents an experimental study done to predict the flexural tensile strength considering the confinement conditions and age of concrete for a wide range of concrete strengths (from 30 to 85 MPa). It is concluded that the factors like confinement conditions and age of concrete should be given due consideration in deriving the flexural tensile strength and compressive strength proportionality equations.

The penetration to geological shield occurs in many situations at various velocities and scales, for example, meteor-cratering, pile driving, falling of objects from high-rise building construction, and debris/fragments from failed components. The soil media is an efficient energy dissipation system and effective shock protection shield. Impact circumstances are currently getting widespread attention. A lot of research has been done on soil media for impact and penetration. The phenomenon of dynamic penetration in heterogeneous particulate soil medium is very complex and the target soil media under dynamic impact especially under high speed and deep penetration neither behave completely as solid nor as liquid. The topics of recent research interest in the field of penetration to soil media and their significant findings are critically reviewed in the present study. The dedicated review of analytical, empirical, experimental, and computational methods to predict the response of soils media-impacting objects to penetration is presented. The emerging challenges in fundamental research of penetration into soil media are outlined and it is an attempt to formulate the future research directions in the field of soil media penetration.

Land surface temperature (LST) can fully reflect the water–heat exchange cycle of the earth surface that is important for the study of environmental change. There is little research on LST in the semi-arid region of Abha-Khamis-Mushyet, which has a complex topography. The study used LST data, retrieved from ASTER data in semi-arid mountain areas and discussed its relationship with land use/land cover (LULC), topography and the normalized difference vegetation index (NDVI). The results showed that the LST was significantly influenced by altitude and corresponding LULC type. In the study area, during the summer season, extreme high-temperature zones were observed, possibly due to dense concrete surfaces. LST among different types of land use differed significantly, being the highest in exposed rocky areas and built-up land, and the lowest in dense vegetation. NDVI and LST spatial distributions showed opposite trends. The LST–NDVI feature space showed a unique ABC obtuse-angled triangle shape and showed an overall negative linear correlation. In brief, the LST could be retrieved well by the emissivity derived NDVI TES method, which relied on upwelling, downwelling, and transmittance. In addition, the LST of the semi-arid mountain areas was influenced by elevation, slope zenith angle, aspect and LULC, among which vegetation and elevation played a key role in the overall LST. This research provides a roadmap for land-use planning and environmental conservation in mountainous urban areas.

Due to resource scarcity, environmental impact, and socioeconomic constraints, sustainable criteria should be given prime importance in the construction industry. The sustainability in concrete can be attained using supplementary materials. The numbers of supplementary concrete materials (SCMs) are available ranging from industries by-products, agro-products, waste product, and newly engineered materials. The selection of the supplementary materials is challenging due to the wide range of environmental, technical, and economic constraints. The paper proposes a decision support framework considering technical, environmental, social, and economic sustainability criteria for ranking of concrete supplementary material. The decision framework includes the optimal scoring method- (OSM-) based shortlisting of supplementary materials, with technical and cost characteristics, and subsequent prioritizing material according to sustainable potentiality order using AHP-TOPSIS. The stability of the ranking of sustainable concrete supplementary materials has been verified using sensitivity analysis. The present study develops decision support framework by employing technical characteristics of the most technically sound material used as concrete material. The technical, environmental, and socioeconomic sustainable performance indicators have considered its selection to obtain much needed sustainability and to boost the eco-friendly environment. From the proposed MCDM framework, it is found that engineered siliceous materials and agriculture-based industrial waste products as SCMs will prove to be most sustainable in the construction industry.

Concrete mix design is the science to obtain concrete proportions of cement, water, and aggregate, based on the particular concrete design method and their mix design parameters. However, the suitability of concrete proportion for high-performance concrete depends on resulting mix factors, namely, water, cement, fine aggregate, and coarse aggregate ratios. This paper implements the multicriteria decision-making techniques (MCDM) for ranking concrete mix factors and representative mix design methods. The study presents a framework to identify critical mix factors found from the concrete mix design methods for high-performance concrete using the two-phase AHP and TOPSIS approach. Three methods of concrete mix design, namely, American Concrete Institute (ACI) mix design method, Department of Energy (DOE) method, and Fineness Modulus (FM) method, are considered for ranking mix design methods and the resulting mix factors. Three hierarchy levels, having three criteria and seven subcriteria, and three alternatives are considered. The present research is attempted to provide MCDM framework to rank the concrete mix guidelines for any given environment such as concrete under sulphate and chloride attack and for evolving the performance-based concrete mix design techniques. Sensitivity and validation analysis is also provided to demonstrate the effectiveness of the proposed approach.

Concrete manufacturing, a high energy and natural resources demanding process, can play a vital role in sustainable development by offering solutions to environmental and socio-economic issues. Concrete manufactured with siliceous materials can extend concrete life and reduce costs, and judicious management of siliceous utilization can make concrete manufacturing sustainable. A number of industrial and agro-based by-products, waste products, and new engineered materials are being use as siliceous material in concrete. The present research aims to implement the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach, a Multi-Criteria Decision Making (MCDM) technique, for the orderly management of siliceous materials based on sustainable criteria, namely, technical, environmental, social, and economic aspects. The present research adopts twenty indicators of sustainability to evolve a comprehensive model for a sustainability ranking of concrete siliceous materials and to provide siliceous materials management. The present research also provides a methodology for the systematic ranking of sustainable criteria and indicators along with a siliceous materials sustainability order for enhanced sustainable development and management. It can be concluded that the proper material management of siliceous concrete materials, especially nano-engineered materials in construction industry, will help in the conservation of basic concrete materials and environmental protection without direct impact on social development.