NDLI: Comparison of multi-objective evolutionary neural network, adaptive neuro-fuzzy inference system and bootstrap-based neural network for flood forecasting

Content Provider	Springer Nature Link
Author	Kant, Amal Suman, Pranmohan K. Giri, Brijesh K. Tiwari, Mukesh K. Chatterjee, Chandranath Nayak, Purna C. Kumar, Sawan
Copyright Year	2013
Abstract	Accurate flood forecasting is of utmost importance in mitigating flood disasters. Flood causes severe public and economic loss especially in large river basins. In this study, multi-objective evolutionary neural network (MOENN) model is developed for accurate and reliable hourly water level forecasting at Naraj gauging site in Mahanadi river basin, India. The performance of the developed model is compared with adaptive neuro-fuzzy inference system (ANFIS) and bootstrap-based neural network (BNN) models. The performance of the models is compared in terms of Nash–Sutcliffe efficiency, root mean square error, mean absolute error and percentage deviation in peak (D). The performance of the models in forecasting floods is also evaluated using existing performance evaluation criterion of Central Water Commission, India as well as a multiple linear regression model. A partitioning analysis in conjunction with threshold statistics is carried out to evaluate the performance of the developed models in forecasting floods for low, medium and high water levels. It is found that the performance of MOENN and BNN models is more stable and consistent compared to ANFIS model. For longer lead times, the performance of MOENN model is found to be the best, with its performance in forecasting higher water levels being significantly better compared to ANFIS and BNN models. Overall, it is found that MOENN model has great potential to be applied in flood forecasting.
Starting Page	231
Ending Page	246
Page Count	16
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	23
Issue Number	1
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2013-02-08
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Neural network Multi-objective evolutionary neural network Neuro-fuzzy inference system Bootstrap-based neural network Flood forecasting Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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