Artificial Intelligence–Driven Predictive Modeling for Climate-Resilient Agricultural Systems in Semi-Arid Regions

Abstract

Climate change has intensified the vulnerability of agricultural systems in semi-arid regions, where limited water availability and unpredictable weather patterns significantly affect crop productivity. Traditional agricultural decision-making approaches, largely dependent on historical observations, are increasingly inadequate in addressing dynamic climatic uncertainties. This study proposes an artificial intelligence–driven predictive modeling framework to enhance climate resilience in semi-arid agricultural systems. The framework integrates multi-source datasets, including meteorological records, soil properties, crop growth parameters, and satellite-derived vegetation indices, to develop predictive models using machine learning algorithms such as random forest, support vector machines, and deep neural networks. The results demonstrate that AI-based models outperform conventional statistical techniques in predicting crop yield, drought occurrence, and soil moisture variability. Furthermore, the integration of predictive models into intelligent decision support systems enables real-time optimization of irrigation scheduling and crop management strategies. The study highlights the potential of artificial intelligence to improve resource efficiency, reduce climate-induced risks, and promote sustainable agricultural practices in semi-arid regions.