Managing Climate-Change Impacts through Predictive Analytics and AI-Driven Automated Irrigation

Abstract

Climate change poses a major challenge to the agricultural sector because it disrupts weather patterns and reduces water availability. Effective irrigation-water management and accurate climate prediction are essential for mitigating these effects and supporting sustainable farming. This study analyzes the combined use of convolutional neural networks (CNNs) and artificial intelligence (AI) to predict future climate variations and automate irrigation. The proposed system uses a CNN to analyze historical climate data, satellite imagery, and weather forecasts and to generate accurate regional climate predictions. These forecasts are then supplied to an AI-based irrigation system that allocates water according to predicted weather conditions, soil-moisture levels, and crop requirements. The AI system uses real-time sensor data and dynamically adjusts irrigation schedules to improve water-use efficiency and minimize waste. Experimental findings indicate that integrating CNN-based prediction with AI-driven control can improve water management under farming conditions by providing farmers with insight into climate variability and an automated mechanism for reducing water loss. The approach demonstrates a sustainable and scalable method for improving agricultural productivity while mitigating climate-change risks.