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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67170
Volume:
10 |
9 |
Sep 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science
College of Agricultural Engineering and Technology, NAU, Dediapada, Gujarat, India
Computer Science
College of Agriculture ,NAU, Waghai, Dangs, Gujarat, India
Computer Science
Vimal Tormal Poddar BCA College, Pandesara, Surat, Gujarat, India
Computer Science
Department of Computer Science, School of Science & Technology, Vanita Vishram Women’s University, Surat, Gujarat, India
Rain is a natural source of water and it is not only important for the human beings but also for the survival of plants and animals. It plays a significant role in industry, agriculture, electrical energy and others. Due to the uncertainty and irregularity of rain can lead to occurrence of natural disasters like floods and droughts and also make harm to the infrastructure and life of the living being. Therefore, it is essential to predict rainfall for the effective management in agricultural sector, drought assessment, water management and other areas. Now days, many more neural networks are used for rainfall prediction. One of these is the long short-term memory (LSTM), which has good prediction results and can be trained for sequence synthesis by processing real data sequences step by step. In this study, a hybrid rainfall prediction model is developed using Long Short Term Memory neural network and Genetic Algorithm using historical data of 20 years of rainfall. To assess the model's performance, a categorical cross-entropy loss function is employed. The outcomes of the experiment show that a hybrid rainfall prediction model is suitable for rainfall prediction and has exceptional prediction accuracy.
KEYWORDS
In this research, the proposed rainfall prediction model has developed based on 20 years of historical data of rainfall. The model is built by the Keras API framework using a genetic algorithm and an LSTM neural network. This prediction aids in preventing unpredictable natural disasters like drought and floods and supports farmers in making important agricultural decisions. According to the experimental findings, models that are evaluated based on accuracy achieve a performance of about 83.33 percent. The study comes to the conclusion that the proposed model is ideally appropriate for rainfall prediction and has a respectable degree of accuracy.
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Publication Date
9/10/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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