River flood risk modeling by applying random forest and support vector machine: The case of Sago-Sile river catchments, Southern Ethiopia

The Implications of Missing Data on the Stability of Random Forest and Support Vector Machine Model's Output

The study utilized two machine learning (ML) techniques namely Random Forest (RF) and Support Vector

Flood risk assessment along the River Niger

The dataset used to build a novel Flood Risk Assessment (FRA) for semi-arid rural areas is provided

Comparative Analysis of Artificial Neural Networks (ANNs) and Support Vector Machine (SVM) Models for Flood Forecasting in the Vaal River Basin, South Africa

Background/Purpose: Flooding poses severe risks to South Africa's Vaal River Basin, a criti

Flood Risk Prediction Using Remote Sensing and Machine Learning — Tana River, Kenya

This project implements an end-to-end flood prediction pipeline for Tana River County, Keny

Flood Warning System for the Búzi River Basin, Mozambique - Flood Modeling -

Performance of Random Forest and Support Vector Machine Models for Drought Prediction in Kano, State, Northwest Nigeria

Abstract <