Modeling rainfall-runoff process using soft computing techniques

Kisi, Özgür; Shiri, Jalal; Tombul, Mustafa

doi:10.1016/j.cageo.2012.07.001

Cited by 215 publications

(66 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The expressions of RMSE, MAE, E, RSR, VAF, ρ, and NMBE are given below [23][24][25][26][27]. Table 2 shows the values of different parameters.…”

Section: Resultsmentioning

confidence: 99%

Determination of Electrical Resistivity of Soil Based on Thermal Resistivity Using RVM and MPMR

Samui¹,

Kim²

2016

Period. Polytech. Civil Eng.

View full text Add to dashboard Cite

This article adopts Relevance Vector Machine (RVM) and Minimax Probability Machine Regression (MPMR) for prediction Soil Electrical Resistivity(R E ) of soil. RVM uses an improper hierarchical prior. It optimizes over hyperparameters. MPMR is a probabilistic model. Two models (MODEL I and MODEL II) have been adopted. Percentage sum of the gravel and sand size fractions (F) and Soil Thermal Resistivity(R T ) has been takes as inputs in MODEL I. MODEL II uses F,R T and saturation of soils(S) as input variables. The results of RVM and MPMR have been compared with the Artificial Neural Network (ANN). The developed RVM and MPMR proves his ability for prediction of R E of soil.

show abstract

“…The expressions of RMSE, MAE, E, RSR, VAF, ρ, and NMBE are given below [23][24][25][26][27]. Table 2 shows the values of different parameters.…”

Section: Resultsmentioning

confidence: 99%

Determination of Electrical Resistivity of Soil Based on Thermal Resistivity Using RVM and MPMR

Samui¹,

Kim²

2016

Period. Polytech. Civil Eng.

View full text Add to dashboard Cite

show abstract

“…Some recent time series methods, such as the neural network model (Shoaib et al [17]) and binary-coded particle swarm optimization (Taormina and Chau [18]), have shown improvement of rainfall runoff simulations. Kisi et al [19] conducted rainfall-runoff process modeling using soft computing techniques. Granata et al [20] presented vector regression for rainfall-runoff modeling in urban drainage.…”

Section: Introductionmentioning

confidence: 99%

Application of Hydrological Model PRMS to Simulate Daily Rainfall Runoff in Zamask-Yingluoxia Subbasin of the Heihe River Basin

Fei

Huang

Cai

et al. 2017

Water

View full text Add to dashboard Cite

Abstract:The Precipitation-Runoff Modeling System (PRMS) has been applied to simulate rainfall runoff in Zamask-Yingluoxia subbasin of the Heihe River Basin in this study. By using observed data in the subbasin, the model has been calibrated by comparing model simulations of daily stream flow to observed data at Yinglouxia station for the period of summer in 2004. Then model verification was conducted by keeping the same model parameters for the simulation of the period from 1 January 2003 to 31 December 2006. Results from model verification indicate that the model is able to provide good accuracy of simulations of daily rainfall runoff and river flow at Yinglouxia station, with a Nash-Sutcliffe Efficiency coefficient of 0.90 and the root-mean-square error of 15.7 m 3 /s. The error of maximum peak flow is 6.9 m 3 /s (1.8%) and the error of mean flow is 1.4 m 3 /s (2.5%). Comparing to previous studies, results indicate the improvement of model accuracy in simulations of daily rainfall runoff. The calibrated and verified hydrological model can be used to support flood hazard mitigations and water resource management in the Zamask-Yingluoxia subbasin.

show abstract

“…In this study, the GP based models have been employed for forecasting groundwater level. In fact, GP has been reported in variety of hydrologic and hydro-geologic modeling such as: flood routing (Sivapragasam et al 2008), evaporation , ground water remediation (Aly and Peralta 1999), suspended sediment modeling (Kisi et al 2012), stage discharge curve (Azamathulla et al 2011), short-term water level fluctuations (Shiri and Kisi 2011) and rainfall-runoff model (Kisi et al 2013). The detailed review of application of GP in water resources has been presented in ASCE Task Committee (2010).…”

Section: Introductionmentioning

confidence: 99%

Genetic programming based monthly groundwater level forecast models with uncertainty quantification

Kasiviswanathan

Saravanan

Balamurugan

et al. 2016

Model. Earth Syst. Environ.

View full text Add to dashboard Cite

Modeling hydrogeologic processes facilitates in accurate prediction/forecasting of groundwater level variations. Still, the uncertainty in model prediction is a major concern that requires detailed investigation. There could be several factors which introduce uncertainty such as inherent assumption, various levels of model complexity and simplicity. In general, model inputs, parameters and structure are the major sources of uncertainty while quantifying model prediction uncertainty. In this study, a genetic programming (GP) based models have been employed for forecasting groundwater level variation along with prediction uncertainty quantification. Though various sources induce uncertainty in the model prediction, the input uncertainty quantification has received little attention. Hence, the input uncertainty has been considered for the analysis in this study. The proposed method is demonstrated using measured monthly values of rainfall and corresponding groundwater level data of Amarawathi basin, India. It is observed that the prediction along with uncertainty quantification improves the confidence level of models while making decisions, in particular for effective planning and management of groundwater resources.

show abstract

Modeling rainfall-runoff process using soft computing techniques

Cited by 215 publications

References 44 publications

Determination of Electrical Resistivity of Soil Based on Thermal Resistivity Using RVM and MPMR

Determination of Electrical Resistivity of Soil Based on Thermal Resistivity Using RVM and MPMR

Application of Hydrological Model PRMS to Simulate Daily Rainfall Runoff in Zamask-Yingluoxia Subbasin of the Heihe River Basin

Genetic programming based monthly groundwater level forecast models with uncertainty quantification

Contact Info

Product

Resources

About