Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm

Safari, Mir Jafar Sadegh; Arashloo, Shervin Rahimzadeh; Mehr, Ali Danandeh

doi:10.1016/j.jhydrol.2020.125014

Cited by 33 publications

(14 citation statements)

References 52 publications

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“…Rainfall-runoff modeling is among the most challenging task for hydrologists, particularly in regions with scarce rainfall and runoff data records. The complexity of the rainfall-runoff modeling also comes from the non-stationary features of its components, such as seasonality, potential trend, and the non-linear behavior of the variables involved in the modeling process [11,40]. Geomorphological features characterizing the watershed influence significantly the runoff regime; namely, in urban areas, high imperviousness areas cause increased runoff by originating floods while the same behavior is not observed in fewer imperviousness areas [1,3].…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…The findings resulted from this study contribute to enhance the understanding of the hydrological parameters and processes that govern a watershed system. Also, it offers new insights on the application of the GR2M model in regions characterized by a similar climate and geomorphological conditions to support decision-makers and optimize the planning and operation rules of water resources systems [21,40]. Last, for areas, especially large basins suffering from a lack of hydrometeorological data records it is important to assess the areal inhomogeneity of the investigated gauging station network [41,42].…”

Section: Sensitivity Analysismentioning

confidence: 99%

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Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

Ditthakit

Pinthong

Salaeh

et al. 2021

Water

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Accurate monthly runoff estimation is crucial in water resources management, planning, and development, preventing and reducing water-related problems, such as flooding and droughts. This article evaluates the monthly hydrological rainfall-runoff model’s performance, the GR2M model, in Thailand’s southern basins. The GR2M model requires only two parameters: production store (X1) and groundwater exchange rate (X2). Moreover, no prior research has been reported on its application in this region. The 37 runoff stations, which are located in three sub-watersheds of Thailand’s southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. The Thornthwaite method was utilized for the determination of evapotranspiration. The model’s performance was conducted using three statistical indices: Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The model’s calibration results for 37 runoff stations gave the average NSE, r, and OI of 0.657, 0.825, and 0.757, respectively. Moreover, the NSE, r, and OI values for the model’s verification were 0.472, 0.750, and 0.639, respectively. Hence, the GR2M model was qualified and reliable to apply for determining monthly runoff variation in this region. The spatial distribution of production store (X1) and groundwater exchange rate (X2) values was conducted using the IDW method. It was susceptible to the X1, and X2 values of approximately more than 0.90, gave the higher model’s performance.

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Section: Sensitivity Analysismentioning

confidence: 99%

Section: Sensitivity Analysismentioning

confidence: 99%

Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

Ditthakit

Pinthong

Salaeh

et al. 2021

Water

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“…A Bayesian ridge employs all explanatory variables as well as basic ridge regression, whereas ARD is also one of the sparse estimations ( Tipping, 2001 ). Previous studies have applied several machine learning algorithms to establish predictive models for water quality and rainfall runoff ( Park et al., 2018 ; Lu and Ma, 2020 ; Safari et al., 2020 ), but the predictive inactivation models for waterborne viruses, which are based on Bayesian regularized regression analyses using water quality and operational parameters, have not been reported.…”

Section: Introductionmentioning

confidence: 99%

Predictive water virology using regularized regression analyses for projecting virus inactivation efficiency in ozone disinfection

et al. 2021

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Wastewater reclamation and reuse have been practically applied to water-stressed regions, but waterborne pathogens remaining in insufficiently treated wastewater are of concern. Sanitation Safety Planning adopts the hazard analysis and critical control point (HACCP) approach to manage human health risks upon exposure to reclaimed wastewater. HACCP requires a predetermined reference value (critical limit: CL) at critical control points (CCPs), in which specific parameters are monitored and recorded in real time. A disinfection reactor of a wastewater treatment plant (WWTP) is regarded as a CCP, and one of the CCP parameters is the disinfection intensity ( e.g. , initial disinfectant concentration and contact time), which is proportional to the log reduction value (LRV) of waterborne pathogens. However, the achievable LRVs are not always stable because the disinfection intensity is affected by water quality parameters, which vary among WWTPs. In this study, we established models for projecting virus LRVs using ozone, in which water quality and operational parameters were used as explanatory variables. For the model construction, we used five machine learning algorithms and found that automatic relevance determination with interaction terms resulted in better prediction performances for norovirus and rotavirus LRVs. Poliovirus and coxsackievirus LRVs were predicted well by a Bayesian ridge with interaction terms and lasso with quadratic terms, respectively. The established models were relatively robust to predict LRV using new datasets that were out of the range of the training data used here, but it is important to collect LRV datasets further to make the models more predictable and flexible for newly obtained datasets. The modeling framework proposed here can help WWTP operators and risk assessors determine the appropriate CL to protect human health in wastewater reclamation and reuse.

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“…Safari et al [1] have investigated the rainfallrunoff modeling through regression in the reproducing kernel Hilbert space algorithm. Najafi et al [2] have worked on the combining fractional differential transform method and reproducing kernel Hilbert space method to solve fuzzy impulsive fractional differential equations.…”

Section: Introductionmentioning

confidence: 99%

On the solutions of boundary value problems

Akgül

Hashemi

Seyfi

2021

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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We investigate the nonlinear boundary value problems by reproducing kernel Hilbert space technique in this paper. We construct some reproducing kernel Hilbert spaces. We define a bounded linear operator to obtain the solutions of the problems. We demonstrate our numerical results by some tables. We compare our numerical results with some results exist in the literature to present the efficiency of the proposed method.

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Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm

Cited by 33 publications

References 52 publications

Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

Predictive water virology using regularized regression analyses for projecting virus inactivation efficiency in ozone disinfection

On the solutions of boundary value problems

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