Optimal application of conceptual rainfall–runoff hydrological models in the Jinshajiang River basin, China

Tayyab, Muhammad; Zhou, Jianzhong; Zeng, Xiangyong; Chen, Lu; Ye, Lei

doi:10.5194/piahs-368-227-2015

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…The raising population is enhancing the demand for freshwater, which results in the need for optimal water resource management [1,2]. According to Penman (1961) [3], hydrology is the science that tries to respond to the query 'what happens to the rain'?…”

Section: Introductionmentioning

confidence: 99%

Application of Integrated Artificial Neural Networks Based on Decomposition Methods to Predict Streamflow at Upper Indus Basin, Pakistan

et al. 2018

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Consistent streamflow forecasts play a fundamental part in flood risk mitigation. Population increase and water cycle intensification are extending not only globally but also among Pakistan’s water resources. The frequency of floods has increased in the last few decades in the country, which emphasizes the importance of efficient practices needed to adopt for various aspects of water resource management such as reservoir scheduling, water sustainability, and water supply. The purpose of this study is to develop a novel hybrid model for streamflow forecasting and validate its efficiency at the upper Indus basin (UIB), Pakistan. Maximum streamflow in the River Indus from its upper mountain basin results from melting snow or glaciers and climatic unevenness of both precipitation and temperature inputs, which will, therefore, affect rural livelihoods at both a local and a regional scale through effects on runoff in the Upper Indus basin (UIB). This indicates that basins receive the bulk of snowfall input to sustain the glacier system. The present study will help find the runoff from high altitude catchments and estimated flood occurrence for the proposed and constructed hydropower projects of the Upper Indus basin (UIB). Due to climate variability, the upper Indus basin (UIB) was further divided into three zone named as sub-zones, zone one (z1), zone two (z2), and zone three (z3). The hybrid models are designed by incorporating artificial intelligence (AI) models, which includes Feedforward backpropagation (FFBP) and Radial basis function (RBF) with decomposition methods. This includes a discrete wavelet transform (DWT) and ensemble empirical mode decomposition (EEMD). On the basis of the autocorrelation function and the cross-correlation function of streamflow, precipitation and temperature inputs are selected for all developed models. Data have been analyzed by comparing the simulation outputs of the models with a correlation coefficient (R), root mean square errors (RMSE), Nash-Sutcliffe Efficiency (NSE), mean absolute percentage error (MAPE), and mean absolute errors (MAE). The proposed hybrid models have been applied to monthly streamflow observations from three hydrological stations and 17 meteorological stations in the UIB. The results show that the prediction accuracy of the decomposition-based models is usually better than those of AI-based models. Among the DWT and EEMD based hybrid model, EEMD has performed significantly well when compared to all other hybrid and individual AI models. The peak value analysis is also performed to confirm the results’ precision rate during the flood season (May-October). The detailed comparative analysis showed that the RBFNN integrated with EEMD has better forecasting capabilities as compared to other developed models and EEMD-RBF can capture the nonlinear characteristics of the streamflow time series during the flood season with more precision.

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Section: Introductionmentioning

confidence: 99%

Application of Integrated Artificial Neural Networks Based on Decomposition Methods to Predict Streamflow at Upper Indus Basin, Pakistan

et al. 2018

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“…Due to no assessment of flood magnitude, a flood resulted in a loss of thousand lives and damage of agriculture land of million dollars in Pakistan in 2010 [3]. It is not possible to provide complete safety from flood, but high amounts of money and many lives can be saved by providing accurate flood predictions, flood magnitude, and flood duration [4]. The importance of water measurement compelled researchers to apply various types of forecasting methods to estimate and forecast river flows.…”

Section: Introductionmentioning

confidence: 99%

Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm

Adnan

Yuan

Kisi

et al. 2017

Advances in Meteorology

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River flow prediction is essential in many applications of water resources planning and management. In this paper, the accuracy of multivariate adaptive regression splines (MARS), model 5 regression tree (M5RT), and conventional multiple linear regression (CMLR) is compared with a hybrid least square support vector regression-gravitational search algorithm (HLGSA) in predicting monthly river flows. In the first part of the study, all three regression methods were compared with each other in predicting river flows of each basin. It was found that the HLGSA method performed better than the MARS, M5RT, and CMLR in river flow prediction. The effect of log transformation on prediction accuracy of the regression methods was also examined in the second part of the study. Log transformation of the river flow data significantly increased the prediction accuracy of all regression methods. It was also found that log HLGSA (LHLSGA) performed better than the other regression methods. In the third part of the study, the accuracy of the LHLGSA and HLGSA methods was examined in river flow estimation using nearby river flow data. On the basis of results of all applications, it was found that LHLGSA and HLGSA could be successfully used in prediction and estimation of river flow.

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Rainfall-runoff modeling at Jinsha River basin by integrated neural network with discrete wavelet transform

Tayyab

Zhou

Dong

et al. 2017

Meteorol Atmos Phys

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Optimal application of conceptual rainfall–runoff hydrological models in the Jinshajiang River basin, China

Cited by 4 publications

References 17 publications

Application of Integrated Artificial Neural Networks Based on Decomposition Methods to Predict Streamflow at Upper Indus Basin, Pakistan

Application of Integrated Artificial Neural Networks Based on Decomposition Methods to Predict Streamflow at Upper Indus Basin, Pakistan

Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm

Rainfall-runoff modeling at Jinsha River basin by integrated neural network with discrete wavelet transform

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