Interpretability of simple RNN and GRU deep learning models used to map land susceptibility to gully erosion

The Science of The Total Environment, Год журнала: 2023, Номер 904, С. 166960 - 166960

Опубликована: Сен. 9, 2023

Язык: Английский

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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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Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python

Examples and Counterexamples, Год журнала: 2025, Номер 7, С. 100180 - 100180

Опубликована: Фев. 3, 2025

Язык: Английский

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Artificial Intelligence in Agricultural Mapping: A Review

Agriculture, Год журнала: 2024, Номер 14(7), С. 1071 - 1071

Опубликована: Июль 3, 2024

Artificial intelligence (AI) plays an essential role in agricultural mapping. It reduces costs and time increases efficiency management activities, which improves the food industry. Agricultural mapping is necessary for resource requires technologies farming challenges. The AI applications gives its subsequent use decision-making. This study analyses AI’s current state through bibliometric indicators a literature review to identify methods, resources, geomatic tools, types, their management. methodology begins with bibliographic search Scopus Web of Science (WoS). Subsequently, data analysis establish scientific contribution, collaboration, trends. United States (USA), Spain, Italy are countries that produce collaborate more this area knowledge. Of studies, 76% machine learning (ML) 24% deep (DL) applications. Prevailing algorithms such as Random Forest (RF), Neural Networks (ANNs), Support Vector Machines (SVMs) correlate activities In addition, contributes associated production, disease detection, crop classification, rural planning, forest dynamics, irrigation system improvements.

Язык: Английский

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Implementation of random forest, adaptive boosting, and gradient boosting decision trees algorithms for gully erosion susceptibility mapping using remote sensing and GIS

Environmental Earth Sciences, Год журнала: 2024, Номер 83(3)

Опубликована: Фев. 1, 2024

Язык: Английский

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Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

Water, Год журнала: 2024, Номер 16(8), С. 1141 - 1141

Опубликована: Апрель 17, 2024

Mapping spatial data is essential for the monitoring of flooded areas, prognosis hazards and prevention flood risks. The Ganges River Delta, Bangladesh, world’s largest river delta prone to floods that impact social–natural systems through losses lives damage infrastructure landscapes. Millions people living in this region are vulnerable repetitive due exposure, high susceptibility low resilience. Cumulative effects monsoon climate, rainfall, tropical cyclones hydrogeologic setting Delta increase probability floods. While engineering methods mitigation include practical solutions (technical construction dams, bridges hydraulic drains), regulation traffic land planning support systems, geoinformation rely on modelling remote sensing (RS) evaluate dynamics hazards. Geoinformation indispensable mapping catchments areas visualization affected regions real-time monitoring, addition implementing developing emergency plans vulnerability assessment warning supported by RS data. In regard, study used monitor southern segment Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated (March) post-flood (November) periods analysis extent landscape changes. Deep Learning (DL) algorithms GRASS GIS modules qualitative quantitative as advanced image processing. results constitute a series maps based classified

Язык: Английский

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Mapping and modeling groundwater potential using machine learning, deep learning and ensemble learning models in the Saiss basin (Fez-Meknes Region, Morocco)

Groundwater for Sustainable Development, Год журнала: 2024, Номер 26, С. 101281 - 101281

Опубликована: Авг. 1, 2024

Язык: Английский

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The combination of Multi-Criteria Decision-Making (MCDM) and morphometric parameters for prioritizing the erodibility of sub-watersheds in the Ouljet Es Soltane basin (North of Morocco)

Heliyon, Год журнала: 2024, Номер 10(19), С. e38228 - e38228

Опубликована: Сен. 28, 2024

Preserving water and soil resources ranks among the top priorities outlined in national strategy. Indeed, integrated management of vulnerable territories, particularly Morocco, requires a deep knowledge hydrological functioning use these regions. The diverse hydroclimatic morphological features within Ouljet Es Soltane watershed, which is sub-basin extensive Oued Sebou present significant challenges managing its resources. Identifying areas susceptible to erosion crucial for implementing preventive measures basin ensuring sustainable development. Morphometric analysis plays an important role effective utilization basin's This study used four MCDM models, including CF (Compound Factor), VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), TOPSIS (Technique Order Preference by Similarity Ideal Solution), SAW (Simple Additive Weighing), prioritize 20 sub-watersheds watershed. Based on sub-watershed prioritization results obtained from VIKOR, TOPSIS, 16 achieved scores 0, 0.59, 0.8, respectively, positioning it as first rank. These findings highlight that exhibits high susceptibility classified one most terms risk. can be into categories: low, moderate, high, very high. On other hand, model only has two low moderate susceptibility. Overall, suggest morphometric parameters are highly identifying at risk erosion. Furthermore, methods exhibit greater predictive accuracy compared model. comparison models involved Spearman correlation coefficient test (SCCT). this provide valuable insights making informed decisions developing framework control strategies.

Язык: Английский

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Future groundwater drought analysis under data scarcity using MedCORDEX regional climatic models and machine learning: The case of the Haouz Aquifer

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 58, С. 102249 - 102249

Опубликована: Фев. 18, 2025

Язык: Английский

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Advancements in ultraprecision machining for precision engineering

AIP conference proceedings, Год журнала: 2025, Номер 3157, С. 110002 - 110002

Опубликована: Янв. 1, 2025

Язык: Английский

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