Novel ensemble machine learning modeling approach for groundwater potential mapping in Parbhani District of Maharashtra, India

M Masroor, H Sajjad, P Kumar, TK Saha, MH Rahaman… - Water, 2023 - mdpi.com
Groundwater is an essential source of water especially in arid and semi-arid regions of the
world. The demand for water due to exponential increase in population has created stresses …

Modelling of soil erosion susceptibility incorporating sediment connectivity and export at landscape scale using integrated machine learning, InVEST-SDR and …

RK Bhattacharya, ND Chatterjee, K Das - Journal of Environmental …, 2024 - Elsevier
Evaluating the linkage between soil erosion and sediment connectivity for export
assessment in different landscape patterns at catchment scale is valuable for optimization of …

[HTML][HTML] Machine learning models for gully erosion susceptibility assessment in the Tensift catchment, Haouz plain, Morocco for sustainable development

Y Bammou, B Benzougagh, O Abdessalam… - Journal of African Earth …, 2024 - Elsevier
Gully erosion is a widespread environmental danger, threatening global socio-economic
stability and sustainable development. This study comprehensively applied seven machine …

Classification of soil horizons based on VisNIR and SWIR hyperespectral images and machine learning models

KM de Oliveira, JVF Gonçalves, R Falcioni… - Remote Sensing …, 2024 - Elsevier
The use of spectral signature to classify soil horizons and orders is becoming increasingly
popular in the field of geotechnology. With the introduction of precise sensors and robust …

[PDF][PDF] Options for digital twin application in developing country river basin management: a review

JO Botai, S Ghosh, K Matheswaran, C Dickens… - 2023 - researchgate.net
Abstract A Digital Twin (DT) is a digital representation of reality. This report explores the
implementation of DT in the context of basin scale water management, with a particular …

[HTML][HTML] Soil erosion susceptibility prediction using ensemble hybrid models with multicriteria decision-making analysis: Case study of the Medjerda basin, northern …

A Bouamrane, H Boutaghane, A Bouamrane… - International Journal of …, 2024 - Elsevier
Soil erosion is considered one of the most prevalent natural hazards in semiarid regions,
leading to the instability of ecosystems and human life. The main purpose of this research …

Developing a hybrid deep learning model with explainable artificial intelligence (XAI) for enhanced landslide susceptibility modeling and management

S Alqadhi, J Mallick, M Alkahtani, I Ahmad, D Alqahtani… - Natural Hazards, 2024 - Springer
Landslides in the Nainital district of Uttarakhand, India, pose a significant threat to human
communities and local ecosystems. This study aims to improve landslide susceptibility …

Harnessing the Power of Remote Sensing and Unmanned Aerial Vehicles: A Comparative Analysis for Soil Loss Estimation on the Loess Plateau

N Kariminejad, M Kazemi Garajeh, M Hosseinalizadeh… - Drones, 2023 - mdpi.com
This study explored the innovative use of multiple remote sensing satellites and unmanned
aerial vehicles to calculate soil losses in the Loess Plateau of Iran. This finding emphasized …

Renewable energy, forest cover, export diversification, and ecological footprint: a machine learning application in moderating eco-innovations on agriculture in the …

H Padhan, S Ghosh, S Hammoudeh - Environmental Science and …, 2023 - Springer
Abstract The United Nations Climate Change Conference (COP26) recommended that the
member nations enhance their technological progression and structural transformation to …

[HTML][HTML] Accommodating uncertainty in soil erosion risk assessment: Integration of Bayesian belief networks and MPSIAC model

H Bashari, A Boali, S Soltani - Natural Hazards Research, 2024 - Elsevier
Accommodating uncertainty stands as one of the most salient challenges in the
development of soil erosion assessment tools. We presented a novel approach integrating …