Evaluating the Potential of Nature-Based Solutions for Drought Mitigation Using Remote Sensing–Driven LSTM Modeling and Climate Projections

This repository supports the study “Evaluating the Potential of Nature-Based Solutions for Drought Mitigation Using Remote Sensing–Driven LSTM Modeling and Climate Projections.” It contains Python workflows for study design and spatial data extraction, data preparation and multicollinearity testing, machine-learning model evaluation, and LSTM-based prediction of soil moisture and runoff. The repository is organized into separate folders for source code, trained models, and datasets, including raw, cleaned, and standardized data, to ensure transparency and reproducibility of the analysis.