Affiliation:
1. Computer Science Engineering, IIMS, Chinchwad, India
2. Computer Science Engineering, Sanjay Ghodawat University, Atigre Taluka, Maharashtra, India
3. IT Department, Priyadarshini College of Engineering, Nagpur, Maharashtra, India
Abstract
Nowadays, various research works is explored to predict the rainfall in the different areas. The emerging research is assisted to make effective decision capacities that are involved in the field of agriculture broadly related to the irrigation process and cultivation. Here, the atmospheric and climatic factors such as wind speed, temperature, and humidity get varies from one place to another place. Thus, it makes the system more complex, and it attains higher error rate during computation for providing accurate rainfall prediction results. In this paper, the major intention is to design an advanced Artificial Intelligent (AI) model for rainfall prediction for different areas. The rainfall data from diverse areas are collected initially, and data cleaning is performed. Further, data normalization is done for ensuring the proper organization and related data in each record. Once these pre-processing phases are completed, rainfall recognition is the main step, in which Adaptive Membership Enhanced Fuzzy Classifier (AME-FC) is adopted for classifying the data into low, medium, and high rainfall. Then for each degree of low, medium, and high rainfall, the prediction process is performed individually by training the developed Tri-Long Short-Term Memory (TRI-LSTM). Additionally, the output achieved from the trained TRI-LSTM rainfall prediction in cm for each low, medium, and high rainfall. The meta-heuristic technique with Hybrid Moth-Flame Colliding Bodies Optimization (HMFCBO) enhances the recognition and prediction phases. The experimental outcome shows that the different rainfall prediction databases prove the developed model overwhelms the conventional models, and thus it would be helpful to predict more accurate rainfall.
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software
Reference38 articles.
1. Prediction of drought-induced reduction of agricultural productivity in Chile from MODIS, rainfall estimates, and climate oscillation indices;Vrieling;Remote Sensing of Environment.,2018
2. Ghorbanzadeh O, Meena SR, van Westen CJ. Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach. Landslides. 2021.
3. Chowdhur I, Pal SC, Chakrabortty R, Da B, Roy P, Pal SC, Malik S. Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya. Nat Hazards, 2021.
4. A Data-Driven Approach for Accurate Rainfall Prediction;Manandhar;IEEE Transactions on Geoscience and Remote Sensing,2019
5. Cowan T, Wheeler MC, Griffith M, Stone R. Cowan T. Improving the seasonal prediction of Northern Australian rainfall onset to help with grazing management decisions. Climate Services. 2020 August; 19.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献