Optimizing Drought Prediction Models: A Comparative Study of Machine Learning Algorithms in Hot and Arid Districts of Pakistan

Drought has adverse consequences on arid and semi-arid regions, where water supplies are already scarce. With a diverse climate, a significant portion of Sindh, the southernmost province of Pakistan, is classified as arid and semi-arid regions. Sindh experiences severe water scarcity, erratic rainfall, and prolonged droughts. These challenges affect agricultural sectors, livelihoods, and biodiversity, making sustainable water management essential for the region’s development. In this situation, it is necessary to develop a drought prediction model to mitigate these challenges. Despite advances in scientific technology, drought forecasting methods often fail to provide timely and accurate forecasts due to complex climate variables and regional differences in drought indices. The integration of Machine learning Techniques with remotely sensed data has emerged as a promising solution for early drought detection and prediction with improved accuracy. This chapter focuses on developing geospatial platforms that integrate data fusion models with machine learning (ML) for effective drought prediction. This study highlights the underutilization of remotely sensed drought indices and aims to collect time series data, evaluate ML models, and compare their accuracy in forecasting drought conditions. Remote sensing-based indices such as NDVI, LST, VCI, TCI, and VHI used in machine learning models can enhance prediction. Therefore, these indices are used as variables for drought prediction in Sindh. Drought prediction models provide early drought warning systems to mitigate the severe impacts of drought.