Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia

Ghorbani, Mohammad Ali and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Kim, Sungwon and Kashani, Mahasa Hasanpour and Karimi, Vahid and Izadkhah, Maryam (2020) Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia. Soft Computing. ISSN 1432-7643


Abstract

Accurately predicting river flows over daily timescales is considered as an important task for sustainable management of freshwater ecosystems, agricultural applications, and water resources management. In this research paper, artificial intelligence (AI) techniques, namely the cascade correlation neural networks (CCNN) and the random forest (RF) models, were employed in daily river stage and river flow prediction for two river systems (i.e., Dulhunty River and Herbert River) in Australia. To develop the CCNN and RF models, a significant 3-day antecedent river stage and river flow time series were used. 80% of the whole data were used for model training and the remaining 20% for model testing. A total of ten different model structures with different input combinations were used to evaluate the optimal model in the training phase, and the results were analyzed using statistical metrics including the root mean square error (RMSE), Nash–Sutcliffe coefficient (NS), Willmott’s index of agreement (WI), and Legate and McCabe’s index (ELM) in the testing phase. The inter-comparison of CCNN and RF models for both river systems showed that the CCNN model was able to generate a more accurate prediction of the river stage and river flow compared to the RF model. Due to hydro-geographic differences leading to a different underlying historical data characteristics, the optimal CCNN’s performance for the Dulhunty River was found to be most accurate, in terms of ELM = 0.779, WI = 0.964, and ENS = 0.862 versus 0.775, 0.968, and 0.885 for the Herbert River. Following the performance accuracies, the authors ascertained that the CCNN model can be taken as a preferred data intelligent tool for river stage and river flow prediction.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published online: 9 January 2020. Permanent restricted access to ArticleFirst version, in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Date Deposited: 05 Feb 2020 03:05
Last Modified: 08 May 2020 01:53
Uncontrolled Keywords: Australia, cascade correlation neural networks, prediction, random forest, river flow
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
05 Environmental Sciences > 0502 Environmental Science and Management > 050205 Environmental Management
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
D Environment > 96 Environment > 9603 Climate and Climate Change > 960303 Climate Change Models
Identification Number or DOI: 10.1007/s00500-019-04648-2
URI: http://eprints.usq.edu.au/id/eprint/37752

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