A Hybrid Prediction of Drought using Artificial Intelligence and Machine Learning

Low rainfall over a period can lead to insufficient soil moisture and result in drought. This condition can last for weeks to months, depending on the extent of soil moisture depletion. Evapotranspiration, the process of water loss through evaporation and transpiration, plays a critical role in this phenomenon. The intensity of rainfall, temperature, sunshine, wind, amount of rainfall, and water vapor all impact soil moisture. Drought can have a significant impact on society’s functioning, including environmental, economic, and social well-being. With the changing climate, there is a need to adapt to cope with altered weather patterns. This study proposes a hybrid model for drought prediction using Artificial Intelligence and machine learning algorithms. The model is based on a combination of Support Vector Regression (SVR) and Extreme Gradient Boosting (XG Boost) algorithms, and uses historical rainfall and temperature data to predict future drought conditions. The proposed model was tested using data from a region in India, and was found to outperform traditional statistical models in terms of accuracy and predictive power. The results of this study suggest that the proposed hybrid model has the potential to be a valuable tool for predicting drought conditions and helping farmers and policymakers make more informed decisions about agricultural planning and water resource management. Future research directions include improving data quality, refining the model in real-world settings, and exploring potential applications of the model beyond agriculture.