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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Abstract

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.

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