Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm

Flood forecasting plays an important role in flood control and water resources management. Recently, the data-driven models with a simpler model structure and lower data requirement attract much more attentions. An extreme learning machine (ELM) method, as a typical data-driven method, with the adva...
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Autor*in:	Lu Chen [verfasserIn] Na Sun [verfasserIn] Chao Zhou [verfasserIn] Jianzhong Zhou [verfasserIn] Yanlai Zhou [verfasserIn] Junhong Zhang [verfasserIn] Qing Zhou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	flood forecasting extreme learning machine (ELM) backtracking search optimization algorithm (BSA) the upper Yangtze River

Übergeordnetes Werk:	In: Water - MDPI AG, 2010, 10(2018), 10, p 1362
Übergeordnetes Werk:	volume:10 ; year:2018 ; number:10, p 1362

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/w10101362

Katalog-ID:	DOAJ019491824

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520			\|a Flood forecasting plays an important role in flood control and water resources management. Recently, the data-driven models with a simpler model structure and lower data requirement attract much more attentions. An extreme learning machine (ELM) method, as a typical data-driven method, with the advantages of a faster learning process and stronger generalization ability, has been taken as an effective tool for flood forecasting. However, an ELM model may suffer from local minima in some cases because of its random generation of input weights and hidden layer biases, which results in uncertainties in the flood forecasting model. Therefore, we proposed an improved ELM model for short-term flood forecasting, in which an emerging dual population-based algorithm, named backtracking search algorithm (BSA), was applied to optimize the parameters of ELM. Thus, the proposed method is called ELM-BSA. The upper Yangtze River was selected as a case study. Several performance indexes were used to evaluate the efficiency of the proposed ELM-BSA model. Then the proposed model was compared with the currently used general regression neural network (GRNN) and ELM models. Results show that the ELM-BSA can always provide better results than the GRNN and ELM models in both the training and testing periods. All these results suggest that the proposed ELM-BSA model is a promising alternative technique for flood forecasting.
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callnumber-first	T - Technology
author	Lu Chen
spellingShingle	Lu Chen misc TC1-978 misc TD201-500 misc flood forecasting misc extreme learning machine (ELM) misc backtracking search optimization algorithm (BSA) misc the upper Yangtze River misc Hydraulic engineering misc Water supply for domestic and industrial purposes Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm
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topic_title	TC1-978 TD201-500 Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm flood forecasting extreme learning machine (ELM) backtracking search optimization algorithm (BSA) the upper Yangtze River
topic	misc TC1-978 misc TD201-500 misc flood forecasting misc extreme learning machine (ELM) misc backtracking search optimization algorithm (BSA) misc the upper Yangtze River misc Hydraulic engineering misc Water supply for domestic and industrial purposes
topic_unstemmed	misc TC1-978 misc TD201-500 misc flood forecasting misc extreme learning machine (ELM) misc backtracking search optimization algorithm (BSA) misc the upper Yangtze River misc Hydraulic engineering misc Water supply for domestic and industrial purposes
topic_browse	misc TC1-978 misc TD201-500 misc flood forecasting misc extreme learning machine (ELM) misc backtracking search optimization algorithm (BSA) misc the upper Yangtze River misc Hydraulic engineering misc Water supply for domestic and industrial purposes
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title	Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm
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title_full	Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm
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author_browse	Lu Chen Na Sun Chao Zhou Jianzhong Zhou Yanlai Zhou Junhong Zhang Qing Zhou
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title_sort	flood forecasting based on an improved extreme learning machine model combined with the backtracking search optimization algorithm
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title_auth	Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm
abstract	Flood forecasting plays an important role in flood control and water resources management. Recently, the data-driven models with a simpler model structure and lower data requirement attract much more attentions. An extreme learning machine (ELM) method, as a typical data-driven method, with the advantages of a faster learning process and stronger generalization ability, has been taken as an effective tool for flood forecasting. However, an ELM model may suffer from local minima in some cases because of its random generation of input weights and hidden layer biases, which results in uncertainties in the flood forecasting model. Therefore, we proposed an improved ELM model for short-term flood forecasting, in which an emerging dual population-based algorithm, named backtracking search algorithm (BSA), was applied to optimize the parameters of ELM. Thus, the proposed method is called ELM-BSA. The upper Yangtze River was selected as a case study. Several performance indexes were used to evaluate the efficiency of the proposed ELM-BSA model. Then the proposed model was compared with the currently used general regression neural network (GRNN) and ELM models. Results show that the ELM-BSA can always provide better results than the GRNN and ELM models in both the training and testing periods. All these results suggest that the proposed ELM-BSA model is a promising alternative technique for flood forecasting.
abstractGer	Flood forecasting plays an important role in flood control and water resources management. Recently, the data-driven models with a simpler model structure and lower data requirement attract much more attentions. An extreme learning machine (ELM) method, as a typical data-driven method, with the advantages of a faster learning process and stronger generalization ability, has been taken as an effective tool for flood forecasting. However, an ELM model may suffer from local minima in some cases because of its random generation of input weights and hidden layer biases, which results in uncertainties in the flood forecasting model. Therefore, we proposed an improved ELM model for short-term flood forecasting, in which an emerging dual population-based algorithm, named backtracking search algorithm (BSA), was applied to optimize the parameters of ELM. Thus, the proposed method is called ELM-BSA. The upper Yangtze River was selected as a case study. Several performance indexes were used to evaluate the efficiency of the proposed ELM-BSA model. Then the proposed model was compared with the currently used general regression neural network (GRNN) and ELM models. Results show that the ELM-BSA can always provide better results than the GRNN and ELM models in both the training and testing periods. All these results suggest that the proposed ELM-BSA model is a promising alternative technique for flood forecasting.
abstract_unstemmed	Flood forecasting plays an important role in flood control and water resources management. Recently, the data-driven models with a simpler model structure and lower data requirement attract much more attentions. An extreme learning machine (ELM) method, as a typical data-driven method, with the advantages of a faster learning process and stronger generalization ability, has been taken as an effective tool for flood forecasting. However, an ELM model may suffer from local minima in some cases because of its random generation of input weights and hidden layer biases, which results in uncertainties in the flood forecasting model. Therefore, we proposed an improved ELM model for short-term flood forecasting, in which an emerging dual population-based algorithm, named backtracking search algorithm (BSA), was applied to optimize the parameters of ELM. Thus, the proposed method is called ELM-BSA. The upper Yangtze River was selected as a case study. Several performance indexes were used to evaluate the efficiency of the proposed ELM-BSA model. Then the proposed model was compared with the currently used general regression neural network (GRNN) and ELM models. Results show that the ELM-BSA can always provide better results than the GRNN and ELM models in both the training and testing periods. All these results suggest that the proposed ELM-BSA model is a promising alternative technique for flood forecasting.
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title_short	Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm
url	https://doi.org/10.3390/w10101362 https://doaj.org/article/2c3c23dc34bb45e5972487027d780f19 http://www.mdpi.com/2073-4441/10/10/1362 https://doaj.org/toc/2073-4441
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author2	Na Sun Chao Zhou Jianzhong Zhou Yanlai Zhou Junhong Zhang Qing Zhou
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up_date	2024-07-03T23:44:51.643Z
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Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm

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