Neural network model for discharge and water-level prediction for Ramganga River catchment of Ganga Basin, India

Modeling the relationship between precipitation and water level is of great significance in the prevention of flood disaster. In recent years, the use of machine learning algorithms for precipitation–water level prediction has attracted wide attention in flood forecasting and other fields; however, a clear method to model the relationship of precipitation and water level using grid precipitation products with a neural network model is lacking. The issues of the method include how to select a neural network model, as well as how to influence the modeling results with different types and resolutions of remote sensing data. The purpose of this paper is to provide some findings for the issues. We used the back-propagation (BP) neural network and a nonlinear autoregressive exogenous model (NARX) time series network to model the relationship between precipitation and water level, respectively. The water level of Pingshan hydrographic station at a catchment area in the Jinsha River Basin was simulated by the two network models using three different grid precipitation products. The results showed that when the ground station data are missing, the grid precipitation product is a good alternative to construct the precipitation–water level relationship. In addition, using the NARX network as a model fitting network using extra inputs was better than using the BP neural network; the Nash efficiency coefficients of the former were all higher than 97%, while the latter were all lower than 94%. Furthermore, the input of grid products with different spatial resolutions has little significant effect on the modeling results of the model.

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Artificial Neural Network Model Application on Long Term Water Level Predictions of South Florida Coastal Waters

2010 International Conference on Computational Intelligence and Software Engineering ◽

10.1109/cise.2010.5677263 ◽

2010 ◽

Author(s):

Sudong Xu ◽

Wenrui Huang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Water Level ◽

Network Model ◽

Neural Network Model ◽

Coastal Waters ◽

Artificial Neural Network Model ◽

South Florida ◽

Model Application

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An Improved Genetic Algorithm Coupling a Back-Propagation Neural Network Model (IGA-BPNN) for Water-Level Predictions

Water ◽

10.3390/w11091795 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1795 ◽

Cited By ~ 4

Author(s):

Nengcheng Chen ◽

Chang Xiong ◽

Wenying Du ◽

Chao Wang ◽

Xin Lin ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Water Level ◽

Network Model ◽

Neural Network Model ◽

Local Convergence ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Improved Genetic Algorithm ◽

Genetic Strategies

Accurate water-level prediction is of great significance to flood disaster monitoring. A genetic algorithm coupling a back-propagation neural network (GA-BPNN) has been adopted as a hybrid model to improve forecast performance. However, a traditional genetic algorithm can easily to fall into locally limited optimization and local convergence when facing a complex neural network. To deal with this problem, a novel method called an improved genetic algorithm (IGA) coupling a back-propagation neural network model (IGA-BPNN) is proposed with a variety of genetic strategies. The strategies are to supply a genetic population by a chaotic sequence, multi-type genetic strategies, adaptive dynamic probability adjustment and an attenuated genetic strategy. An experiment was tested to predict the water level in the middle and lower reaches of the Han River, China, with meteorological and hydrological data from 2010 to 2017. In the experiment, the IGA-BPNN, traditional GA-BPNN and an artificial neural network (ANN) were evaluated and compared using the root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE) coefficient and Pearson correlation coefficient (R) as the key indicators. The results showed that IGA-BPNN moderately correlates with the observed water level, outperforming the other two models on three indicators. The IGA-BPNN model can settle problems including the limited optimization effect and local convergence; it also improves the prediction accuracy and the model stability regardless of the scenario, i.e., sudden floods or a period of less rainfall.

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