Drought Monitoring and Analysis: A Multi-Source Data and Machine Learning-Based Approach

Drought is a significant environmental challenge with widespread implications for ecosystems, economies, and societies. Despite its critical importance, drought remains one of the most challenging natural disasters to monitor due to its gradual onset and complex interactions with climatic and environmental factors. In a conflict-affected region such as northeast Syria, understanding the drought patterns and trends is a crucial step in post-conflict rehabilitation, particularly because this area relies heavily on agriculture. To enhance our understanding of the drought phenomenon in northeast Syria, a dataset was created that covers a range of meteorological, vegetation, and soil parameters. A machine learning model based on the XGBoost algorithm was used to identify the most significant features influencing the drought. The Standardized Precipitation Evapotranspiration Index, Vegetation Health Index, and Soil Moisture Anomaly were selected as targets to represent the meteorological, vegetation, and soil data, respectively. For more reliable results, when choosing a target, the data of this target was left out of the training process, which enables the detection of how other parameters affect that target. The results showed that the most critical parameter affecting the drought is the temperature.