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

Tayyab, Muhammad; Zhou, Jing; Dong, Xiaohua; Ahmad, Ijaz; Sun, Na

doi:10.1007/s00703-017-0546-5

Cited by 36 publications

(14 citation statements)

References 33 publications

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“…During this study, a total of 42 years of observation, a minimum of 34 years (1975 to 2008, 80%) of the data was used for model calibration, while a substantial (or residual) data 09 years (2009-2017, 20 %) was used for model validation. Until normalization was applied, all datasets were set to be between 0 and 1 to remove any variation between them, the dataset's comparative value was to any external information (Tayyab et al 2017).…”

Section: Data Classifications and Inputs Selectionsmentioning

confidence: 99%

Optimum Application of Hybrid Data Driven Models With Two Step Verification Method at Mangla Watershed, Pakistan

Tayyab

Dong

Sibtain

et al. 2021

Preprint

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In this study, 03 ensemble and decomposition methods (DMs) i.e., empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN) were coupled artificial intelligence and machine learning based method AI-ML, i.e., multilayer perceptron (MLP), support vector regression (SVR) to develop 06 fundamental hybrid models to predict streamflow with one-month lead time. Developed models in this study were categorized into runoff models (RMs) and rainfall-runoff models (RRMs). Results indicated that (i) among standalone models (SMs), support vector regression (SVR) performs better than multilayer perceptron (MLP), (ii) decomposition methods (DMs) have ability to improve the accuracy rate of the standalone models (SMs) and, (iii) rainfall runoff models (RRMs) have shown great accuracy throughout the investigation as compared to runoff models (RMs). To compare model performances flow-hydrographs (FHG) were generated, 05 performance evaluation criteria (PEC) were used to quantify the model precision. Two step verification method i.e., extreme value analysis (EVA) and least value analysis (LVA) approaches were proposed to verify the performances. Among all developed hybrid models (HMs), i.e., EMD- (MLP, SVR), EEMD- (MLP, SVR) and ICEEMDAN- (MLP, SVR), rainfall-runoff ICEEMDAN-(SVR) model was selected as optimal model with MAE (59.56), RMSE (91.82), R (0.97) MAPE (8.75), and NSEC (0.97) for Mangla watershed, Pakistan.

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Section: Data Classifications and Inputs Selectionsmentioning

confidence: 99%

Optimum Application of Hybrid Data Driven Models With Two Step Verification Method at Mangla Watershed, Pakistan

Tayyab

Dong

Sibtain

et al. 2021

Preprint

Self Cite

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“…The mean daily streamflow, temperature and precipitation data of the Jinsha River Basin from 1974 to 2010 (37 years) were collected. The mean daily temperature and precipitation of the Jinsha River Basin were calculated based on 32 meteorological stations' datasets in the Jinsha River Basin [49]. These meteorological stations' datasets were collected from the National Meteorological Information Center (http://data.cma.cn/).…”

Section: Data Collectionmentioning

confidence: 99%

Intra- and Inter-Annual Variability of Hydrometeorological Variables in the Jinsha River Basin, Southwest China

2019

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In this study, the intra- and inter-annual variability of three major elements in the water system, temperature, precipitation and streamflow, from 1974 to 2010 in the Jinsha River Basin, China, were analyzed. An exploratory data analysis method, namely, moving average over shifting horizon (MASH), was introduced and combined with the Mann–Kendall (MK) test and Sen’s slope estimation to analyze the intra- and inter-annual variations. The combination of MASH with the MK test and Sen’s slope estimation demonstrated that the annual temperature, precipitation and streamflow from 1974 to 2010 showed, on average, an increasing trend. The highest change in temperature was detected in early January, 0.8 ℃, that of precipitation was detected in late June, 0.4 mm/day, and that of streamflow was detected mid-August, 138 mm/day. Sensitivity analysis of the smoothing parameters on estimated trends demonstrated that Y parameters smaller than 2 and w parameters smaller than 6 were not suitable for trend detection when applying the MASH method. The correlation between the smoothed data was generally greater than that between the original hydrometeorological data, which demonstrated that the application of MASH could eliminate the influence of periodicity and random fluctuations on hydrometeorological time series and could facilitate regularity and the detection of trends.

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“…Wang and Zhou (2020) examined streamflow prediction at each hydrological station in the mainstream of the Yellow River in China by coupling PCA with time series analysis method, but it can only be utilized to linear dimensionality reduction of data. Discrete wavelet transform (DWT), as a classical data analysis method, is capable of helping a forecasting model to extract useful information (Feng et al 2015;Tayyab et al 2019), but it may suffer from signal loss. Zuo et al (2020) proposed a single-model forecasting (SF) framework namely SF-VMD-LSTM, to forecast daily streamflow.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Hybrid Forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network

Wang

Chau

et al. 2021

Water Resour Manage

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Accurate and consistent annual runoff prediction in regions is a hot topic in the management, optimization, and monitoring of water resources. A novel prediction model (ESMD-SE-WPD-LSTM) is presented in this study. Firstly, the extreme-point symmetric mode decomposition (ESMD) is used to produce several intrinsic mode functions (IMF) and a residual (Res) by decomposing the original runoff series.Secondly, the sample entropy (SE) method is employed to measure the complexity of each IMF. Thirdly, we adopt wavelet packet decomposition (WPD) to further decompose the IMF with the maximum SE into several appropriate components and detailed components. Then the LSTM model, a deep learning algorithm based recurrent approach, is employed to predict all components obtained in the previous step. Finally, the forecasting results of all components are aggregated to generate the final prediction.The proposed model, which is applied to five annual series from different areas in China, is evaluated based on four quantitative indexes (R, NSEC, MAPE and RMSE). The results indicate that the ESMD-SE-WPD-LSTM outperforms other benchmark models in terms of four quantitative indexes. Hence the proposed model can provide higher accuracy and consistency for annual runoff prediction, making it an efficient instrument for scientific management and planning of water resources.

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

Cited by 36 publications

References 33 publications

Optimum Application of Hybrid Data Driven Models With Two Step Verification Method at Mangla Watershed, Pakistan

Optimum Application of Hybrid Data Driven Models With Two Step Verification Method at Mangla Watershed, Pakistan

Intra- and Inter-Annual Variability of Hydrometeorological Variables in the Jinsha River Basin, Southwest China

An Ensemble Hybrid Forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network

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