Implementation of Anticipating Rainfall Using Machine Learning - Shaik Shafeeq Ahmed; Shivaraj; Suneet Lionel Dsouza; Simran Pal R (International Journal of Satellite Remote Sensing, 2023)
Rainfall forecasting is crucial for many aspects of our national economy and should help prevent major seasonal droughts. Since agriculture is a beloved profession in many states, some Asian countries are economically hooked to decline. Previous precipitation info is beneficial. Farmers are cancerous in managing their crops, resulting in economic progress for the country. downfall prediction is hard for earth science scientists because of unordering time and unordered quantity of downfall. This enables the development of fresh herb designs and the efficient use of aquatic resources for the crops. Linear and non-linear style square measures are widely used to forecast seasonal decline. Several algorithms that are computer-aided rule-based techniques include the support vector machine (SVM), genetic algorithm (GA), and CART. Regression, Naive Bayes, Random Forest, and other analytical techniques are frequently used in this study. Overall, we tend to look at how the algorithmic rule that can be applied qualitatively is anticipating failure. The task appears challenging and sophisticated since it requires a vast array of specialist workers, and every person's choice is enthralled with no assurance of success
Keywords: Anticipation; Genetic Algorithm; Machine Learning; Rainfall detection; prediction