A data-driven framework for flood hazard mapping using integrated geospatial and multi-model machine learning approaches in a tropical mountainous region: insights from Aceh Jaya, Aceh Province, Indonesia

Integrating remote sensing and machine learning for flood hazard zonation in Gomati district of Tripura, Northeast India

The integrated methodology demonstrates the potential of combining SAR data and ML techniques for reliable flood susceptibility assessment, providing a replicable framework for other flood-prone regions.

Cross-regional assessment of urban flood vulnerability in climate-exposed coastal megacities: Casablanca, Morocco, and Jakarta, Indonesia

National-Scale Flood Susceptibility Mapping of Nigeria Using Statistical and Machine Learning Models with Satellite-Driven Validation for Data-Sparse Environments

Flooding is the most recurrent and economically devastating natural hazard in Nigeria, yet no standard or consistent nationwide assessment method exists. Spatially explicit flood susceptibility information also remains scarce, particularly given limited hydrometric monitoring. This study comparatively evaluates nationa...

Forest fire risk mapping using machine learning technique based on GIS and remote sensing data, a case study in Dak Lak province, Vietnam

This study investigates the application of advanced machine learning models, specifically Random Forest, Convolutional Neural Network, and Long Short-Term Memory, in conjunction with geospatial data, to construct a forest fire risk prediction map for Dak Lak province in the Central Highlands of Vietnam.

Flood-Prone Area Mapping Using the Integration of Hydrological Data and Google Earth Engine (GEE)

Flood susceptibility mapping that integrates hydrological and geospatial information remains limited at the sub-watershed scale, particularly in rapidly developing coastal urban areas. This study maps flood susceptibility in the Batang Kandis Sub-watershed, Padang City, by integrating hydrological data through Google E...

Multi-Temporal Flood Mapping and Prediction using Sentinel-1 and Landsat-8 Data Fusion