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Open AccessArticle

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

1
School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2
Changjiang Institute of Survey, Planning, Design and Research, Wuhan 430010, China
3
Department of Geosciences, University of Oslo, 1047 Oslo, Norway
4
College of Resource and Environment, South-Central University for Nationalities, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Water 2018, 10(10), 1362; https://doi.org/10.3390/w10101362
Received: 6 September 2018 / Revised: 22 September 2018 / Accepted: 22 September 2018 / Published: 29 September 2018
(This article belongs to the Special Issue Flood Forecasting Using Machine Learning Methods)
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. View Full-Text
Keywords: flood forecasting; extreme learning machine (ELM); backtracking search optimization algorithm (BSA); the upper Yangtze River flood forecasting; extreme learning machine (ELM); backtracking search optimization algorithm (BSA); the upper Yangtze River
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Chen, L.; Sun, N.; Zhou, C.; Zhou, J.; Zhou, Y.; Zhang, J.; Zhou, Q. Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm. Water 2018, 10, 1362.

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