A Way to Integrate ML Algorithm with the NN for the Purpose of Crop Production Through Continuous Monitoring

This check examines the evolution of machine studying (ML) strategies in forecasting hydrological droughts, focusing at the Standardized Runoff Index (SRI) in the Han River basin, South Korea. Given the dramatic climatic shifts witnessed throughout this era, our research evaluates the general performance of six ML models—Decision Trees (DT), Support Vector Machines (SVM), Deep Learning Neural Networks (DLNN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Artificial Neural Networks (ANN), and Fuzzy Rule-Based Systems (FRBS)—in predicting drought severity and frequency. Emphasis is placed on the model and refinement of these fashions to account for converting climatic styles and their impact on drought characteristics. The DT version, renowned for its predictive accuracy and computational performance, is identified as specifically promising. This has a look at targets to manual water resource manage and disaster preparedness via advanced drought forecasting methodologies.