Remote Sensing and Hydrologic-Hydrodynamic Modeling Integrated Approach for Rainfall-Runoff Simulation in Farm Dam Dominated Basin

Xu, Chaowei; Han, Ziyan; Fu, Hao

doi:10.3389/fenvs.2021.817684

Cited by 10 publications

(5 citation statements)

References 65 publications

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“…The 2D model solves the 2D shallow water equations (SWEs) on the basis of the conservation of mass principles and momentum conservation under steady-state flow conditions. The two-dimensional unsteady flow equation is composed of mass balance and the momentum balance, and can be represented as follows [54,60,61]:…”

Section: Two-dimensional Hydraulic Model (2d Model)mentioning

confidence: 99%

“…These models can not only provide more hydrological spatial information but also better simulate the hydraulic properties. In recent years, benefiting from the continuous enrichment of remote sensing and 3S (GIS, RS, and GPS) technology, hydrologists and hydraulic researchers are paying increasing attention to 2D models, and hydraulic models were rapidly enhanced [51][52][53][54][55].…”

Section: Introductionmentioning

confidence: 99%

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Application of Remote-Sensing-Based Hydraulic Model and Hydrological Model in Flood Simulation

Yang

Wang

2022

Sustainability

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Floods are one of the main natural disaster threats to the safety of people’s lives and property. Flood hazards intensify as the global risk of flooding increases. The control of flood disasters on the basin scale has always been an urgent problem to be solved that is firmly associated with the sustainable development of water resources. As important nonengineering measures for flood simulation and flood control, the hydrological and hydraulic models have been widely applied in recent decades. In our study, on the basis of sufficient remote-sensing and hydrological data, a hydrological (Xin’anjiang (XAJ)) and a two-dimensional hydraulic (2D) model were constructed to simulate flood events and provide support for basin flood management. In the Chengcun basin, the two models were applied, and the model parameters were calibrated by the parameter estimation (PEST) automatic calibration algorithm in combination with the measured data of 10 typical flood events from 1990 to 1996. Results show that the two models performed well in the Chengcun basin. The average Nash–Sutcliffe efficiency (NSE), percentage error of peak discharge (PE), and percentage error of flood volume (RE) were 0.79, 16.55%, and 18.27%, respectively, for the XAJ model, and those values were 0.76, 12.83%, and 11.03% for 2D model. These results indicate that the models had high accuracy, and hydrological and hydraulic models both had good application performance in the Chengcun basin. The study can a provide decision-making basis and theoretical support for flood simulation, and the formulation of flood control and disaster mitigation measures in the basin.

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Section: Two-dimensional Hydraulic Model (2d Model)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Remote-Sensing-Based Hydraulic Model and Hydrological Model in Flood Simulation

Yang

Wang

2022

Sustainability

Self Cite

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“…Increasing the number of observed data does not necessarily increase the accuracy and precision of system prediction; it may also affect the estimation performance (Liu and Xie 2020). Due to the nonlinear and complex nature of these processes, using traditionally physical and mathematical models is tricky; thus, using data-driven models would be the best solution to these problems (Li et al 2022, Xu et al 2022.…”

Section: Introductionmentioning

confidence: 99%

Dynamic and Static Soft Computation of Effluent Quality Parameters of Dissolved Air Flotation of an Industrial Wastewater Treatment Plant – A case study

Abouzari,

Pahlavani,

Ahmadi

2023

Preprint

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In the present study, several static and dynamic intelligent computing methods, including Gene Expression Programming (GEP), newly approach of Robusted/ Least Squares Support Vector Machines (RO/LS-SVM) with Grid search and radial basic functions, robust LS-SVM with grid search, Focused Time Delay Neural Network (FTDNN) with Levenberg-Marquardt Learning Algorithm, FTDNN with Bayesian regularization Learning Algorithm, and FTDNN with Broyden-Fletcher-Goldfarb-Shanno (BFGS) Learning Algorithm have been used to estimate Acidity and Total dissolved solids (TDS) from the dissolved air flotation (DAF) unit. Furthermore, static and dynamic models perform differently under the conditions of shock loads accompanied by the temporal impact of past data on the system. Moreover, four related variables, such as COD, pH, Turbidity, and TDS from the effluent of the equalization tank (E. tank), were selected as inputs of the models, collected in an 8-hour monitoring during a year. The results demonstrated that GEP and FTDNN (with Bayesian regularization) with static and dynamic conditions modeled TDS and pH parameters as the best results, respectively. The statistical quality of these models was found to be significant due to their high correlation between the experimental and observed chemical values, although some points in modeling, such as the implication of time lags, showed greater potential in model prediction along with simplicity and efficiency in a variety of complex physical, chemical, and biological processes.

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“…Generally, it is a challenging task to forecast monthly runoff with reliable and applicable forecast accuracy. Hydrological models can be approximately divided into two categories: physical-driven (Liao et al, 2016;Solakian et al, 2020;Zhang et al, 2020;Dunkerley, 2021;Nonki et al, 2021;Xu et al, 2022) and data-driven models (Quilty et al, 2019;Feng et al, 2020;Liao et al, 2020;Darabi et al, 2021;Niu et al, 2021;Feng et al, 2022;Nguyen et al, 2022). Physical-driven models usually consider the physical genesis and mechanism of the runoff yield process and require a large amount of data for modeling.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model of Ensemble Empirical Mode Decomposition and Sparrow Search Algorithm-Based Long Short-Term Memory Neural Networks for Monthly Runoff Forecasting

Yang

Luo³

et al. 2022

Front. Environ. Sci.

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Monthly runoff forecasting plays a vital role in reservoir ecological operation, which can reduce the negative impact of dam construction and operation on the river ecosystem. Numerous studies have been conducted to improve monthly runoff forecast accuracy, of which machine learning methods have been paid much attention due to their unique advantages. In this study, a conjunction model, EEMD-SSA-LSTM for short, which comprises ensemble empirical mode decomposition (EEMD) and sparrow search algorithm (SSA)–based long short-term neural networks (LSTM), has been proposed to improve monthly runoff forecasting. The EEMD-SSA-LSTM model is mainly carried out in three steps. First, the original time series data is decomposed into several sub-sequences. Second, each sub-sequence is simulated by LSTM, of which the hyperparameters are optimized by SSA. Finally, the simulated results for each sub-sequence are summarized as the final results. The data obtained from two reservoirs located in China are used to validate the proposed model performance. Meanwhile, four commonly used statistical evaluation indexes are utilized to evaluate model performance. The results demonstrate that compared to several benchmark models, the proposed model can yield satisfactory forecast results and can be conducive to improving monthly runoff forecast accuracy.

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Remote Sensing and Hydrologic-Hydrodynamic Modeling Integrated Approach for Rainfall-Runoff Simulation in Farm Dam Dominated Basin

Cited by 10 publications

References 65 publications

Application of Remote-Sensing-Based Hydraulic Model and Hydrological Model in Flood Simulation

Application of Remote-Sensing-Based Hydraulic Model and Hydrological Model in Flood Simulation

Dynamic and Static Soft Computation of Effluent Quality Parameters of Dissolved Air Flotation of an Industrial Wastewater Treatment Plant – A case study

A Hybrid Model of Ensemble Empirical Mode Decomposition and Sparrow Search Algorithm-Based Long Short-Term Memory Neural Networks for Monthly Runoff Forecasting

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