Machine learning models for gully erosion susceptibility assessment in the Tensift catchment, Haouz Plain, Morocco for sustainable development

Journal of African Earth Sciences, Год журнала: 2024, Номер 213, С. 105229 - 105229

Опубликована: Март 11, 2024

Gully erosion is a widespread environmental danger, threatening global socio-economic stability and sustainable development. This study comprehensively applied seven machine learning (ML) models including SVM, KNN, RF, XGBoost, ANN, DT, LR, evaluated gully susceptibility in the Tensift catchment predict it within Haouz plain, Morocco. To ensure reliability of findings, employed robust combination inventory, sentinel images, Digital Surface Model. Eighteen predictors, encompassing topographical, geomorphological, environmental, hydrological factors, were selected after multicollinearity analyses. The revealed that approximately 28.18% at very high risk erosion. Furthermore, 15.13% 31.28% are categorized as low respectively. These findings extend to where 7.84% surface area highly risking erosion, while 18.25% 55.18% characterized areas. gauge performance ML models, an array metrics specificity, precision, sensitivity, accuracy employed. highlights XGBoost KNN most promising achieving AUC ROC values 0.96 0.93 test phase. remaining namely RF (AUC = 0.89), LR 0.80), SVM 0.81), DT 0.86), ANN 0.78), also displayed commendable performance. novelty this research its innovative approach combat through cutting edge offering practical solutions for watershed conservation, management, prevention land degradation. insights invaluable addressing challenges posed by region, beyond geographical boundaries can be used defining appropriate mitigation strategies local national scale.

Язык: Английский

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Unveiling the Hidden Connections: Using Explainable Artificial Intelligence to Assess Water Quality Criteria in Nine Giant Rivers

Journal of Cleaner Production, Год журнала: 2025, Номер unknown, С. 144861 - 144861

Опубликована: Янв. 1, 2025

Язык: Английский

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Artificial Intelligence in Hydrology: Advancements in Soil, Water Resource Management, and Sustainable Development

Sustainability, Год журнала: 2025, Номер 17(5), С. 2250 - 2250

Опубликована: Март 5, 2025

Hydrology relates to many complex challenges due climate variability, limited resources, and especially, increased demands on sustainable management of water soil. Conventional approaches often cannot respond the integrated complexity continuous change inherent in system; hence, researchers have explored advanced data-driven solutions. This review paper revisits how artificial intelligence (AI) is dramatically changing most important facets hydrological research, including soil land surface modeling, streamflow, groundwater forecasting, quality assessment, remote sensing applications resources. In AI techniques could further enhance accuracy texture analysis, moisture estimation, erosion prediction for better management. Advanced models also be used as a tool forecast streamflow levels, therefore providing valuable lead times flood preparedness resource planning transboundary basins. quality, AI-driven methods improve contamination risk enable detection anomalies, track pollutants assist treatment processes regulatory practices. combined with open new perspectives monitoring resources at spatial scale, from forecasting storage variations. paper’s synthesis emphasizes AI’s immense potential hydrology; it covers latest advances future prospects field ensure

Язык: Английский

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Landscape connectivity significantly influences the spatial spillover effects of soil erosion: Based on examples from typical karst watersheds

Ecological Indicators, Год журнала: 2025, Номер 173, С. 113373 - 113373

Опубликована: Март 23, 2025

Язык: Английский

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Interpreting optimised data-driven solution with explainable artificial intelligence (XAI) for water quality assessment for better decision-making in pollution management

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(30), С. 42948 - 42969

Опубликована: Июнь 17, 2024

Язык: Английский

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Evaluating soil loss and sediment yield for sustainable management of the Hassan II dam within Morocco’s Upper Moulouya watershed using RUSLE model and GIS

Environmental Earth Sciences, Год журнала: 2024, Номер 83(7)

Опубликована: Март 25, 2024

Язык: Английский

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The impact of rainfall and slope on hillslope runoff and erosion depending on machine learning

Frontiers in Environmental Science, Год журнала: 2025, Номер 13

Опубликована: Апрель 24, 2025

Introduction Soil erosion is a critical issue faced by many regions around the world, especially in purple soil hilly areas. Rainfall and slope, as major driving factors of erosion, pose significant challenge quantifying their impact on hillslope runoff sediment yield. While existing studies have revealed effects rainfall intensity slope comprehensive analysis interactions between different types still lacking. To address this gap, study, based machine learning methods, explores type, amount, maximum 30-min (I30), depth (H) erosion-induced yield (S), unveils among these factors. Methods The K-means clustering algorithm was used to classify 43 events into three types: A-type, B-type, C-type. A-type characterized long duration, large amounts, moderate intensity; B-type short small high C-type intermediate B-type. Random Forest (RF) employed assess impacts yield, along with feature importance analysis. Results results show that amount has most Under types, ranking I30 H S follows: (C>A>B), (A>B>C). follows trend first increasing then decreasing, varying degrees influence depending type. Discussion novelty study lies combining techniques systematically evaluate, for time, type This research not only provides theoretical basis control but also offers scientific support precise prediction management conservation measures regions.

Язык: Английский

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Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

Water, Год журнала: 2025, Номер 17(9), С. 1351 - 1351

Опубликована: Апрель 30, 2025

This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone erosion due diverse topography climatic conditions. Traditional models like Universal Soil Loss Equation (USLE) its revised version (RUSLE) often fall short capturing complex environmental interactions, leading inaccurate predictions. research introduces novel approach using Convolutional Neural Networks (CNNs) combined with Geographic Information Systems (GISs) improve precision spatial resolution of risk assessments. High-resolution satellite imagery, maps, data were processed extract critical factors, such as slope, land cover, rainfall erosivity, which then fed into CNN model. The findings revealed that model outperformed traditional methods, achieving low Root Mean Square Error (RMSE) 2.3 an R-squared value 0.92, significantly surpassing USLE RUSLE models. resulting high-resolution maps identified high-risk areas, particularly central eastern regions watershed, rates exceeding 40 tons/ha/year. These demonstrate superior predictive capabilities learning, offering valuable insights for targeted conservation strategies highlighting potential advanced computational techniques revolutionize modeling.

Язык: Английский

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Quantitative assessment of morphometry and GIS integrated RUSLE model-based soil loss estimation from Pahuj river basin, central India

DELETED, Год журнала: 2024, Номер 90(4), С. 1049 - 1066

Опубликована: Май 10, 2024

In this study the morphometric indices of Pahuj river basin (PRB) were evaluated by applying remote sensing and GIS. The Shuttle Radar Topographic Mission (SRTM) based 30 m digital elevation (DEM) data was used in order to extract parameters using standard methods. PRB covering an area (3648 km2) is controlled homogenous lithology geological structures. drainage density indicates that permeable soil with coarse texture dominantly occurring large low-lying flat areas basin. Contrary high gradient consist impermeable hard granitic rocks Neoarchean Precambrian age a low quantity soil. value elongation ratio form factor reveal elongated show peak flows. To assess erosion susceptibility, attributes Revised Universal Soil Loss Equation (RUSLE) model integrated GIS estimate loss from results rainfall erosivity (R-factor) along pattern indicate upper catchment relatively exhibits intensity than middle lower region. findings (R), erodibility (K), topographic (LS), crop management (C) factors infer quite area. ruggedness number Melton (4.16) imply moderately rugged less prone erosion, particularly relief effective practices water conservation will enhance storage capacity prevent sediment PRB. research may be helpful resolve crisis can such drought-prone

Язык: Английский

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Revealing spatiotemporal heterogeneity of water conservation and its drivers: Enlightenment to water ecology protection and restoration

Journal of Environmental Management, Год журнала: 2024, Номер 370, С. 122517 - 122517

Опубликована: Сен. 20, 2024

Язык: Английский

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