Consensus forecast of rainfall using hybrid climate learning model

Madhukumar, Neethu, Wang, Eric, Zhang, Yi-Fan, and Xiang, Wei (2021) Consensus forecast of rainfall using hybrid climate learning model. IEEE Internet of Things Journal, 8 (9). pp. 7270-7278.

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Rainfall event forecasting is prominently done using climate models (CMs) to produce multiple forecasts for the same rainfall event. The best forecast is complicated to find and hence has not yet been explored in the CMs. Recent advances in deep learning methods have provided an exceptional ability to investigate intricate weather patterns from big climate data. In this paper, a hybrid climate learning model (HCLM) is proposed that utilises both the CM and the deep learning models for improving the rainfall forecast. More specifically, a probabilistic multi-layer perceptron (PMLP) network evaluates multiple forecasts from the CM-generated forecasts and selects the best one. The selected forecast is next passed onto a hybrid deep long short term memory (HD-LSTM) network, which looks back and learns the relationship of the selected forecast with corresponding rainfall and temperature observations to produce the next-day rainfall forecast. The experimental results from various climate zones in Australia show that the HCLM outperforms existing state-of-the-art climate and deep learning models.

Item ID: 65219
Item Type: Article (Research - C1)
ISSN: 2327-4662
Keywords: Climate model, hybrid model, Long short term memory, Multi-layer perceptron, Rainfall forecast
Copyright Information: © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.
Date Deposited: 17 Dec 2020 01:33
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 50%
37 EARTH SCIENCES > 3701 Atmospheric sciences > 370108 Meteorology @ 50%
SEO Codes: 82 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 8298 Environmentally Sustainable Plant Production > 829805 Management of Water Consumption by Plant Production @ 50%
96 ENVIRONMENT > 9609 Land and Water Management > 960905 Farmland, Arable Cropland and Permanent Cropland Water Management @ 50%
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