En-Bing Lin
ABSTRACT
A method of river flow modeling and forecast is implemented, and results are presented to provide comparisons based on different techniques and training parameters. Here we implement a forecast based on the well-established feed forward back propagation design multilayer perceptron artificial neural network. In order to improve predictive ability, two new methods are designed to incorporate the multi-resolution information from a Daubechies type wavelet transform as input to the network. The novel methods are compared with the existing one in a case study to assess the performance of the wavelet neural networks, and to obtain results to help guide future network design and select of training parameters. The new predictive network design is inspired by existing methods but adds more repeatability and stability to the result. By using a genetic algorithm for selecting trained networks and averaging the results of many trials, we can incorporate the inherent randomness created from network training. In this case study, we combine wavelet analysis and artificial neural networks to perform river flow forecasting of the Tittabawassee River. Our results are superior to some existing methods.
- [1] https://www.detroitnews.com/story/news/local/michigan/2020/05/20/tittabawassee-river-midland-flooding/5226734002/Google Scholar
- [2] https://waterdata.usgs.gov/nwis/uv?site_no=04156000Google Scholar
- [3] Gürsoy, Ömer and Engin, Seref Naci, A wavelet neural network approach to predict daily river discharge using meteorological data. Measurement and Control , 52(5-6), 599–607, 2019.Google ScholarCross Ref
- [4] Kişi, Özgür, Neural networks and wavelet conjunction model for intermittent streamflow forecasting. Journal of Hydrologic Engineering, 14(8), 773-782, 2009.Google ScholarCross Ref
- [5] Makwana, Jaydip J and Tiwari, Mukesh K, Intermittent streamflow forecasting and extreme event modelling using wavelet based artificial neural networks. , Water resources management, 28(13), 4857–4873, 2014.Google ScholarCross Ref
- [6] S. Mallat, A Wavelet Tour of Signal Processing. Academic Press, 1998.Google Scholar
- [7] Ortega, Luis F, A neuro-wavelet method for the forecasting of financial time series. Proceedings of the World Congress on Engineering and Computer Science, 24-26, 2012.Google Scholar
- [8] Partal, Turgay and Cigizoglu, H Kerem, Prediction of daily precipitation using wavelet-neural networks. Hydrological sciences journal 54(21), 234–246, 2009.Google Scholar
- [9] Popoola, Ademola Olayemi, Fuzzy-wavelet method for time series analysis. University of Surrey, United Kingdom, 2006.Google Scholar
- [10] Solgi, Abazar and Zarei, Heidar and Nourani, Vahid and Bahmani, Ramin, A new approach to flow simulation using hybrid models,. Applied Water Science, 7(7), 3691–3706, 2017.Google ScholarCross Ref
- [11] Wei, Shouke and Song, Jinxi and Khan, Nasreen Islam, Simulating and predicting river discharge time series using a wavelet-neural network hybrid modelling approach. Hydrological Processes, 26(21), 281–296, 2012.Google ScholarCross Ref
Index Terms
- On Wavelet Neural Networks and River Flow Forecasting
Recommendations
River flow forecasting with ANN
EC'05: Proceedings of the 6th WSEAS international conference on Evolutionary computingProviding stream flow forecasting models is one of the most important problems in water resources planning and management. Traditional models in this field have been developed in the form of regression models, and time series models. Nowadays, ...
RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia
Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a ...
Suitability of different neural networks in daily flow forecasting
Alternative forms of neural networks have been applied to forecast daily river flows on a continuous basis with the purpose of understanding how recent architectures like ANFIS, GRNN and RBF compare with traditional FFBP when monsoon-fed rivers ...
Comments