1. Rainfall prediction system using machine learning algorithms.
- Author
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Brindha, R., Firoz, Sk. Md Khaja, and Reddy, C. Ramnath
- Subjects
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ARTIFICIAL neural networks , *BACK propagation , *RANDOM forest algorithms , *PRECIPITATION forecasting , *DECISION trees , *MACHINE learning - Abstract
Agriculture is vital for survival in India. Rainfall is crucial to agriculture. Predicting rainfall has become a significant issue recently. Rainfall forecasting helps people be prepared and informed of impending rain so they can take the necessary safety measures to preserve their crops from the rain. There are numerous methods available to predict rainfall. Predicting rainfall is where machine learning techniques are most beneficial. XGBoost, Decision Tree, Random Forest, Light BGM, and Logistic Regression are some of the most important machine learning algorithms. The linear and non-linear models, which are both often used, forecast seasonal precipitation. Logistic regression models are the initial models. Rainfall can be predicted when utilising Artificial Neural Networks (ANN) by employing Back Propagation Neural Networks, Decision Trees, and regression models like Random Forest. Due to the atmosphere's dynamic character, applied mathematics techniques are unable to guarantee reliable precision for a statement about precipitation. Regression may be used in the prediction of precipitation utilising machine learning approaches. The goal of this project is to provide non-experts with simple access to the methods and approaches used in the field of precipitation prediction as well as to compare different machine learning techniques. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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