GA-designed Fuzzy Rule-based System for Parameter Estimation of Distributed Rainfall-runoff Model Considering Various Cell Characteristics

Alam, Abul Hasan Md. Badiul; Takeuchi, Juji; Kawachi, Toshihiko

doi:10.7132/jrcsa.kj00004557612

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…The conceptual model parameters were identified on a trial and error basis, considering their physical meanings and referring to the published works (Alam et al 2006(Alam et al , 2007Nakagiri et al 1998), so that the reported results of the annual evapotranspiration (Lake Biwa Research Institute 1988), and the annual amounts of irrigation water and groundwater recharge from paddy fields (Mitsuno and Nagahori 1987) could also be obtained from the model. Furthermore, the time-varying groundwater levels observed at the boring points B5 and B6 from September 1st, 2006 to March 31st, 2006 were used for calibration of the model.…”

Section: Calibrationmentioning

confidence: 99%

“…For this, our earlier distributed watershed model (Alam et al 2006(Alam et al , 2007 is revised and sophisticated in which the soil moisture model is incorporated to evaluate runoff processes and evapotranspiration depending dynamically on water contents retained in the surface soil. Part of the subsurface tank model built in this watershed model is replaced with a mathematical unconfined shallow groundwater flow model, and in addition a network of on-farm drainage canals is considered so that the model could successfully be applied to a watershed including farmlands in a low-lying area.…”

Section: Introductionmentioning

confidence: 99%

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A distributed hydro-environmental watershed model with three-zoned cell profiling

Takeuchi

Kawachi

Unami

et al. 2009

Paddy Water Environ

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A cell-based distributed watershed model is developed which enables us to simulate the hydrological and hydraulic aspects of the watershed in a refined fashion. With three-zoned cell profiling, the model is composed of three sub-models; tank model for a surface water zone, soil moisture model for a surface soil zone, and unconfined shallow groundwater flow model for a subsurface zone. Inclusion of the soil moisture sub-model modified to reroute the infiltration, routed from the tank sub-model, into the return flow and the groundwater recharge features the model. The groundwater flow sub-model, numerically approximated by use of the finite volume method and the implicit time-marching scheme, considers a network of onfarm drainage canals as internal boundaries, which is an essential need for modeling the watershed including farmlands. Cascade-linking of the three sub-models in a cell and assembling of all the cells over the entire watershed domain provides the global equations system to be solved. Applicability of the model is demonstrated with its practical application to a real watershed in that paddy and upland crop fields take great part of the land-use practice. It is then indicated in a quantified manner that rice farming significantly contribute as a major groundwater recharger in an irrigation period to fostering and conservation of regional water resources. Along with appropriately profiling a cell, the model is so versatile and tough that it can be applied without difficulty to a watershed of diverse terrains and land-uses and the computations can stably be carried out. It is thus concluded that the model presently developed could be a powerful ''watershed simulator'' to investigate and assess the time-varying hydro-environmental properties of a watershed while separating and integrating the hydrological and hydraulic components of particular importance.

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Section: Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A distributed hydro-environmental watershed model with three-zoned cell profiling

Takeuchi

Kawachi

Unami

et al. 2009

Paddy Water Environ

Self Cite

View full text Add to dashboard Cite

show abstract

“…Though it seems short, Sorooshian et aL (1983) suggested that rather than length, the quality of infbrmation contained in the data is important. For calibra-tion of the model parameters, the GA-designed fuzzy system (Alam et al, 2007) is employed, which can allow for both qualitative and quantitative information of attributes in a watershed, but takes more time. Therefore, short length of data set would be favorable to avoid huge time requirement.…”

Section: Studyareamentioning

confidence: 99%

Identification and Uncertainty Assessment of Model Parameters of Distributed Runoff Model Using Bayesian Inference

Alam

Takeuchi

Kawachi

2007

Journal of Rainwater Catchment Systems

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A model parameter identification technique based on the Bayesian inference is presented and applied to a long-Lerm application of a distributed rainfall-nmoff mode]. Initial rnodel parameters are given from a probability distribution of values, which is called as "prior" distribution. This prior distribution is then updated by incorporating available observed data, producing a "posterior" probability distribution of model parameter values and the technique ef update is repeated. Seven model parameters of the disnibuted runoff model, which are directly or indirectly related to generate surface and subsurface runoffs, are identified using this technique when the model is applied to the Daido River watershed of Shiga prefecture, Japan. The identMed modet parameters are capable to represent the watershed characteristics for application of distributed model. Most of the model parameters, population variances are found to be low to medium, which supports the stability of identified medel paramcters. However, model parameters in some ceUs show relatively high variance which expresses considerable uncertainty. The estimated hydrograph using the sequentially updated model parameters are well suited with the observed hydrograph. The identified model parameters can produce a good model eCficiency criterion R2 of 84.82%. The preposed model parameter identification technique could be a too] t'or understanding the spatial properties of the model parameters of a distributed rainfaLl-runoff model. Kaywenty: Modelpatumeter identi.fication; Distributed runQffmodet; Bayesian injierence; llarameter uncertainty;

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GA-designed Fuzzy Rule-based System for Parameter Estimation of Distributed Rainfall-runoff Model Considering Various Cell Characteristics

Abstract: A fuzzy rule-based system, designed with genetic algorithm (GA)

Cited by 2 publications

References 21 publications

A distributed hydro-environmental watershed model with three-zoned cell profiling

A distributed hydro-environmental watershed model with three-zoned cell profiling

Identification and Uncertainty Assessment of Model Parameters of Distributed Runoff Model Using Bayesian Inference

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