Research on Precipitation Forecast Based on LSTM–CP Combined Model

The tremendous progress made in the field of deep learning allows us to accurately predict precipitation and avoid major and long-term disruptions to the entire socio-economic system caused by floods. This paper presents an LSTM–CP combined model formed by the Long Short-Term Memory (LSTM) network a...

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Autores principales:	Yan Guo, Wei Tang, Guanghua Hou, Fei Pan, Yubo Wang, Wei Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	precipitation forecast long short-term memory network Chebyshev polynomial BP neural network Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/26159ca290c34267be2bb61213fe78a6
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spelling	oai:doaj.org-article:26159ca290c34267be2bb61213fe78a62021-11-11T19:21:36ZResearch on Precipitation Forecast Based on LSTM–CP Combined Model10.3390/su1321115962071-1050https://doaj.org/article/26159ca290c34267be2bb61213fe78a62021-10-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/11596https://doaj.org/toc/2071-1050The tremendous progress made in the field of deep learning allows us to accurately predict precipitation and avoid major and long-term disruptions to the entire socio-economic system caused by floods. This paper presents an LSTM–CP combined model formed by the Long Short-Term Memory (LSTM) network and Chebyshev polynomial (CP) as applied to the precipitation forecast of Yibin City. Firstly, the data are fed into the LSTM network to extract the time-series features. Then, the sequence features obtained are input into the BP (Back Propagation) neural network with CP as the excitation function. Finally, the prediction results are obtained. By theoretical analysis and experimental comparison, the LSTM–CP combined model proposed in this paper has fewer parameters, shorter running time, and relatively smaller prediction error than the LSTM network. Meanwhile, compared with the SVR model, ARIMA model, and MLP model, the prediction accuracy of the LSTM–CP combination model is significantly improved, which can aid relevant departments in making disaster response measures in advance to reduce disaster losses and promote sustainable development by providing them data support.Yan GuoWei TangGuanghua HouFei PanYubo WangWei WangMDPI AGarticleprecipitation forecastlong short-term memory networkChebyshev polynomialBP neural networkEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 11596, p 11596 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	precipitation forecast long short-term memory network Chebyshev polynomial BP neural network Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	precipitation forecast long short-term memory network Chebyshev polynomial BP neural network Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Yan Guo Wei Tang Guanghua Hou Fei Pan Yubo Wang Wei Wang Research on Precipitation Forecast Based on LSTM–CP Combined Model
description	The tremendous progress made in the field of deep learning allows us to accurately predict precipitation and avoid major and long-term disruptions to the entire socio-economic system caused by floods. This paper presents an LSTM–CP combined model formed by the Long Short-Term Memory (LSTM) network and Chebyshev polynomial (CP) as applied to the precipitation forecast of Yibin City. Firstly, the data are fed into the LSTM network to extract the time-series features. Then, the sequence features obtained are input into the BP (Back Propagation) neural network with CP as the excitation function. Finally, the prediction results are obtained. By theoretical analysis and experimental comparison, the LSTM–CP combined model proposed in this paper has fewer parameters, shorter running time, and relatively smaller prediction error than the LSTM network. Meanwhile, compared with the SVR model, ARIMA model, and MLP model, the prediction accuracy of the LSTM–CP combination model is significantly improved, which can aid relevant departments in making disaster response measures in advance to reduce disaster losses and promote sustainable development by providing them data support.
format	article
author	Yan Guo Wei Tang Guanghua Hou Fei Pan Yubo Wang Wei Wang
author_facet	Yan Guo Wei Tang Guanghua Hou Fei Pan Yubo Wang Wei Wang
author_sort	Yan Guo
title	Research on Precipitation Forecast Based on LSTM–CP Combined Model
title_short	Research on Precipitation Forecast Based on LSTM–CP Combined Model
title_full	Research on Precipitation Forecast Based on LSTM–CP Combined Model
title_fullStr	Research on Precipitation Forecast Based on LSTM–CP Combined Model
title_full_unstemmed	Research on Precipitation Forecast Based on LSTM–CP Combined Model
title_sort	research on precipitation forecast based on lstm–cp combined model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/26159ca290c34267be2bb61213fe78a6
work_keys_str_mv	AT yanguo researchonprecipitationforecastbasedonlstmcpcombinedmodel AT weitang researchonprecipitationforecastbasedonlstmcpcombinedmodel AT guanghuahou researchonprecipitationforecastbasedonlstmcpcombinedmodel AT feipan researchonprecipitationforecastbasedonlstmcpcombinedmodel AT yubowang researchonprecipitationforecastbasedonlstmcpcombinedmodel AT weiwang researchonprecipitationforecastbasedonlstmcpcombinedmodel
_version_	1718431515502706688

Research on Precipitation Forecast Based on LSTM–CP Combined Model

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