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This study proposes a new groundwater potentiality model (GPM) in the Bisha watershed, Saudi Arabia, by integrating logisticregression (LR)-weighted and fuzzy logic-based ensemble machine learning (EML) models for the present and future scenarios. We applied random forest, bagging, and random subspace models for predicting the GPMs. We also used the general circulation model’s (GCM) minimum and maximum representative concentration pathway (RCP) 2.6 and 8.5 scenarios for the future GWP mapping. Results showed that the bagging hybrid model (Area under Curve: 0.986) outperformed other models. GWP models predicted 4058.57 km² as very high, 4267.45 km² as high, 4185.23 km² as moderate, 4342. km² as low, and 4430.24 km² as very low groundwater potential zones. The best model combined with the future climatic conditions shows very high and high groundwater potential zones would cover 2319–2590 km² and 3100–2795 km² . The current research will aid in the development of long-term sustainable groundwater management plans by increasing the effectiveness of GPMs. [ABSTRACT FROM AUTHOR]

The confluence of the Ganges, Jamuna, and Padma rivers is one of the most dynamic in the world, an internationally important research area because of the confluence of two of the world's major rivers, the Ganges and the Brahmaputra. Morphological changes in this area have resulted in severe erosion along the banks. Riverbank erosion is one of Bangladesh's most critical issues, requiring a substantial solution. Riverbank erosion affects millions of people in Bangladesh each year because of erosion in this confluence zone. Consequently, understanding the morphological shifting pattern of the confluence is crucial. The research aims to quantify actual bank shifting near the confluence of the Ganges, Jamuna, and Padma rivers in terms of shifting rate and area during a 25-year period (1990–2015). The acquired satellite images were geo-referenced, and bank lines were digitized to carry out this research. The bank line is the linear construction that separates the outside boundary of the river channel from the floodplains. To assess channel width fluctuation, the distance between the extreme borders of the left and right banks, including mid-channel sandbars, was measured. This period is split into five stages, each lasting 5 years, to measure the maturity of change. Furthermore, the long-term trend from 1972 to 2015 is qualitatively discernible. Landsat satellite pictures were used to investigate this morphological change. The study provides current and reliable information on the planform dynamics of the Ganga–Jamuna confluence. This study will help design and implement drainage development plans and erosion control techniques in this crucial confluence zone. [ABSTRACT FROM AUTHOR]

The groundwater resource in the Kingdom of Saudi Arabia is a non-renewable resource. Geographical information system and remote sensing are proven to be an efficient tool for locating water harvesting and recharge structures by prioritization of sub-watersheds through morphometric analysis. In this study, the watershed prioritization in Asir Province has been assessed by linear, aerial, and relief morphometric parameters. Morphometric analysis has been attempted to prioritize a total of eleven subwatersheds, of which six sub-watersheds in Tathlith Wadi and five sub-watersheds in Bisha Wadi were delineated using digital elevation model (DEM). Advanced space-borne thermal emission and reflection radiometer of 30m resolution DEM has used to generate drainage networks and delineation of subwatersheds using ArcGIS software. The important derived morphometric parameters have computed on the base of already developed mathematical formulae and methods. A novel and quantitative approach based on the compound parameter by using linear and shape parameters of the respective sub-watershed was attempted for its prioritization. The total area of sub-watershed in Wadi Bisha, which lies under high, medium, and low priority is approximately 40%, 20% and 40% respectively. whereas in Wadi Tathlith, the area under high and medium priority is 67% and 33%. The watersheds BSW2 and BSW4 in Wadi Bisha and TSW1, TSW2, TSW3, and TSW5 in Wadi Tathlith would require immediate intervention and efficient action plan for water and soil conservation. The interrelationship between the various morphometric factors of the basin has been studied using a correlation matrix. Further, morphometric factors have been studied in relation to the sub-basins to understand the existing relation between the factors and the sub-basins. The prioritized watersheds were validated using the water yield data from borewells in the respective watersheds. All high prioritized watersheds but one in Wadi Tathlith (TSW3) showed poor water yield. Prioritized sub-watersheds require immediate soil & water conservation initiative according to the given rank to ensure efficient watershed planning and management and improving the potential of water resources under sustainable watershed management and development in the Asir province of the Kingdom of Saudi Arabia. [ABSTRACT FROM AUTHOR]

Accurate soil organic matter (SOM) estimation could provide critical information to understand soil organic carbon sequestration, soil fertility, and the global carbon cycle. A nearest-neighbourhood autoregressive moving average (NN-ARMA) modelling technique along with linear regression has been used to predict localized soil SOM variation based on topographical characteristics and vegetation indices in semi-arid region of Saudi Arabia. Seven topographic variables derived using DEM, and twelve vegetation indices obtained from Landsat 8 used in the model. The best NN-ARMA model showed seven significant variables explaining 96.4% of the total variation of SOM, whereas the best linear regression model could explain 78.8% of the total variation of SOM. The results showed that NN-ARMA model gave better results compared to the linear regression model. Our research gave a better understanding of the possibility of accurate estimation of SOM using the NN-ARMA approach using topographical characteristics and vegetation indices easily acquired by RS sensors. [ABSTRACT FROM AUTHOR]

Geographical information system and remote sensing are proven to be an efficient tool for locating water harvesting and recharge structures, groundwater potential, runoff, watershed prioritization through morphometric analysis. The management and conservation of watershed require scientific evaluation so that appropriate strategies can be implemented by the policymakers and stakeholders. This study evaluates the Parvati River Basin for prioritization of sub-watershed. We use analytical hierarchical approach with pairwise comparison matrix for ranking the parameters which has been derived through morphometric analysis of the watersheds. The linear (stream order, stream length, mean, stream length, stream length ratio, bifurcation ratio, rho coefficient), relief (basin relief, relief ratio, ruggedness number) and areal morphometric parameters (drainage density, stream frequency, texture ratio, form factor, circulatory ratio, elongation ratio, length of overland flow, constant channel maintenance) have been used for prioritization of watershed in the Basin. The results revealed that the basin comprises of complex topography with mountainous and hilly relief structures which results into high run-off. The prioritization of sub-watershed suggests that the largest watershed comprising of 36.09% total watershed area falls into medium category for water potential. The total area under very high to high category is 5.36% and 8.44% respectively. The relationship between morphometric parameter and watershed prioritization shows that the sub-watersheds SW1 and SW8 falls under very high to high category having high water holding capacity. [ABSTRACT FROM AUTHOR]

Soil erosion is the most important factor in land degradation and influences desertification in semi-arid areas. A comprehensive methodology that integrates revised universal soil loss equation (RUSLE) model and GIS was adopted to determine the soil erosion risk (SER) in semi-arid Aseer region, Saudi Arabia. Geoenvironmental factors viz. rainfall (R), soil erodibility (K), slope (LS), cover management and practice factors were computed to determine their effects on average annual soil loss. The high potential soil erosion, resulting from high denuded slope, devoid of vegetation cover and high intensity rainfall, is located towards the north western part of the study area. The analysis is investigated that the SER over the vegetation cover including dense vegetation, sparse vegetation and bushes increases with the higher altitude and higher slope angle. The erosion maps generated with RUSLE integrated with GIS can serve as effective inputs in deriving strategies for land planning/management in the environmentally sensitive mountainous areas. [ABSTRACT FROM AUTHOR]

The present work aims to build a unique hybrid model by combining six fuzzy operator feature selection-based techniques with logistic regression (LR) for producing groundwater potential models (GPMs) utilising high resolution DEM-derived parameters in Saudi Arabia's Bisha area. The current work focuses exclusively on the influence of DEM-derived parameters on GPMs modelling, without considering other variables. AND, OR, GAMMA 0.75, GAMMA 0.8, GAMMA 0.85, and GAMMA 0.9 are six hybrid models based on fuzzy feature selection. The GPMs were validated by using empirical and binormal receiver operating characteristic curves (ROC). An RF-based sensitivity analysis was performed in order to examine the influence of GPM settings. Six hybrid algorithms and one unique hybrid model have predicted 1835–2149 km2 as very high and 3235–4585 km2 as high groundwater potential regions. The AND model (ROCe-AUC: 0.81; ROCb-AUC: 0.804) outperformed the other models based on ROC's area under curve (AUC). A novel hybrid model was constructed by combining six GPMs (considering as variables) with the LR model. The AUC of ROCe and ROCb revealed that the novel hybrid model outperformed existing fuzzy-based GPMs (ROCe: 0.866; ROCb: 0.892). With DEM-derived parameters, the present work will help to improve the effectiveness of GPMs for developing sustainable groundwater management plans. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

India's increasing population growth and unsystematic land cover transformation have led to land degradation and a decline in agricultural production. To achieve optimum advantage from the land, proper exploitation of its resources is necessary. Remote sensing, advanced fuzzy logic, and multi-criteria decision-making like analytical hierarchy process (AHP) integrated agricultural land suitability analysis (ALAS) may facilitate identifying and formulating effective agricultural management strategies required for smart agriculture. The present study was conducted to construct India's robust agricultural suitability model by developing hybrid fuzzy logic and the AHP based model. Fourteen topographical, climatological, soil-related, land-use, and land-cover-related factors were prepared and employed to model agricultural suitability. Agricultural suitability models predicted multi-parameters based agricultural suitable zones for the entire country using three fuzzy operators (AND, Gamma 0.8, Gamma 0.9) and a hybrid fuzzy-AHP model. Sensitivity analysis was conducted to test the models' reliability using Moris technique-based global sensitivity analysis, random forest (RF), and correlation coefficient. The best agricultural suitable model was compared with the production of major crops in India. Results showed that 19.8% of the study area was permanently not suitable in the northernmost region, 19.7% was currently not suitable in the northernmost region, while 20.1% and 20.2% areas were predicted as moderately suitable and highly suitable zones, respectively. The rainfall, elevation, slopes, evapotranspiration, and aridity index had a prime influence on the output of the agricultural suitability model. The adopted method and its application processes can analyze agricultural land suitability and recommend optimal farming methods. It is also comprehended as a promising option for meeting food, nutrition, energy, and job demands while still protecting our threatened environment. [Display omitted] • Agriculture land suitability (ALS) mapping for the country has not been yet done due to data scarcity and lack of technology. • e developed and applied fuzzy-AHP and fuzzy logic-based ALS models, as well as several sensitivity analyses. • 20.2% and 19.78% area were identified as highly suitable and permanently unsuitable for agriculture. • The integrated fuzzy-AHP based ALS model was found to be the best model for ALS mapping. • A robust ALS model could aid agricultural decisions that support smart agricultural practices with greater production. [ABSTRACT FROM AUTHOR]