Abstract
Predicting rainfall has become a problematic and unpredictable activity that profoundly impacts civilization. Precise and appropriate predictions can aid in pre-emptively reducing financial and human harm. Rainfall prediction helps with flooding warnings, water resources management, air transport strategic planning, mobility restrictions, building construction, and other significant human aspects. In this paper, various existing methodologies of rainfall prediction are reviewed and compared. The current techniques of rainfall prediction have different attributes for rainfall estimation. Humidity, temperature, the flow of wind, pressure, sunlight, evaporation, etc., are some attributes of rainfall prediction. Some machine learning-based models of heavy rainfall prediction and optimization models, such as artificial neural network (ANN ), Naive Bayes, decision forest regression (DFR), and boosted decision tree regression (BDTR), and optimization techniques such as firefly, particle swarm optimization, genetic algorithm are compared in this paper. RMSE, MAE, and correlation evaluation parameters are used to evaluate the rainfall prediction model.
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Pachpor, N.N., Suresh Kumar, B., Parsad, P.S., Shaikh, S.G. (2022). Different Nature-Inspired Optimization Models Using Heavy Rainfall Prediction: A Review. In: Raj, J.S., Shi, Y., Pelusi, D., Balas, V.E. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 458. Springer, Singapore. https://doi.org/10.1007/978-981-19-2894-9_58
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