Performance evaluation of convolutional neural network and vision transformer models for groundwater potential mapping

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132840 - 132840

Опубликована: Фев. 1, 2025

Язык: Английский

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Determining prone areas to gully erosion and the impact of land use change on it by using multiple-criteria decision-making algorithm in arid and semi-arid regions

Geoderma, Год журнала: 2021, Номер 403, С. 115379 - 115379

Опубликована: Авг. 9, 2021

Язык: Английский

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Bagging-based machine learning algorithms for landslide susceptibility modeling

Natural Hazards, Год журнала: 2021, Номер 110(2), С. 823 - 846

Опубликована: Авг. 21, 2021

Язык: Английский

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Drought risk assessment: integrating meteorological, hydrological, agricultural and socio-economic factors using ensemble models and geospatial techniques

Geocarto International, Год журнала: 2021, Номер 37(21), С. 6087 - 6115

Опубликована: Июнь 25, 2021

Among natural disasters, drought hits almost half of the world every year, regardless climatic zones. Identifying vulnerability regions is fundamental to plan and adopt mitigation measures. Here we apply a multi-criteria-based machine learning technique that integrates spatial data for preparing map different categories. We adopted remote sensing tools with three models namely support vector (SVM), random forest (RF) regression (SVR) their ensembles (i.e. Bagging, Boosting Stacking), as applied northwestern part Iran case study. Various types geo-environmental factors were considered including meteorological, hydrological, agricultural socio-economic. The result model was evaluated through arithmetic logic values (area under curve [AUC]) receiver operating (ROC). Through multi-collinearity test, prominent causative occurrences are defined. AUC value from ROC SVR-Stacking, RF-Stacking SVM-Stacking training datasets 0.942, 0.918 0.896, respectively. SVR-Stacking yielded best (AUC = 0.94) confirming SVR serves robust preparation susceptibility maps can be used by governmental other administrative agencies.

Язык: Английский

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Assessing landslide susceptibility using a machine learning-based approach to achieving land degradation neutrality

Environmental Earth Sciences, Год журнала: 2021, Номер 80(17)

Опубликована: Авг. 18, 2021

Язык: Английский

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