Abstract
Precise daily rainfall forecasting play a very significant role in modern society that it can not only help for planning of people’s day-to-day activities, agriculture and business, but also assist water resource management in the region to warn or alleviate the effect of drought or flood disaster. However, various inherently complex meteorological factors and dynamic behavior influence the rainfall, with result that it is very difficult to accurately forecast daily rainfall. This study presents a soft computing modeling method based on Extreme Learning Machine (ELM) and Gene Expression Programming (GEP) to enhance the forecast performance. The proposed mode is compared with other five rainfall forecasting models to assess its performance for rainfall forecasting by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Experimental results show that the proposed method outperforms other models in terms of accuracy.
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Acknowledgements
We highly appreciate that this work is supported by the National Science Foundation of China Grant #61562008 and #41575051, and Guangxi scientific research and technology development project# 1598019-1 and #AB16450013, and the National Science Foundation of Guangxi Grant #2017GXNSFAA198228, #2017GXNSFBA198153, #2016GXNSFAA380209 and #2014GXNSFDA118037, and The basic ability promotion project of young and middle-aged teachers in Guangxi universities #2017KY0896, and the open research project of Guangxi Colleges and Universities Key Laboratory of Data Science, and the grant of “Bagui Scholars” Program of Guangxi Province of China.
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Peng, Y., Zhao, H., Li, J., Qin, X., Liao, J., Liu, Z. (2020). A Soft Computing-Based Daily Rainfall Forecasting Model Using ELM and GEP. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_35
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DOI: https://doi.org/10.1007/978-3-030-23307-5_35
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