• Advances in Computational Design
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• v.7 no.2
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• pp.113-128
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• 2022

Water consumption is strongly affected by numerous factors, such as population, climatic, geographic, and socio-economic factors. Therefore, the implementation of a reliable predictive model of water consumption pattern is challenging task. This study investigates the performance of predictive models based on multi-layer perceptron (MLP), multiple linear regression (MLR), and support vector regression (SVR). To understand the significant factors affecting water consumption, the stepwise regression (SW) procedure is used in MLR to obtain suitable variables. Then, this study also implements three predictive models based on these significant variables (e.g., SWMLR, SWMLP, and SWSVR). Annual data of water consumption in Thailand during 2006 - 2015 were compiled and categorized by provinces and distributors. By comparing the predictive performance of models with all variables, the results demonstrate that the MLP models outperformed the MLR and SVR models. As compared to the models with selected variables, the predictive capability of SWMLP was superior to SWMLR and SWSVR. Therefore, the SWMLP still provided satisfactory results with the minimum number of explanatory variables which in turn reduced the computation time and other resources required while performing the predictive task. It can be concluded that the MLP exhibited the best result and can be utilized as a reliable water demand predictive model for both of all variables and selected variables cases. These findings support important implications and serve as a feasible water consumption predictive model and can be used for water resources management to produce sufficient tap water to meet the demand in each province of Thailand.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.2
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• pp.87-95
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• 2016

Turbidity is a key indicator to the user that the 'Discolored Water' phenomenon known to be caused by corrosion of the pipeline in the water supply system. 'Discolored Water' is defined as a state with a turbidity of the degree to which the user visually be able to recognize water. Therefore, this study used data mining techniques in order to estimate turbidity changes in water supply system. Decision tree analysis was applied in data mining techniques to develop estimation models for turbidity changes in the water supply system. The pH and residual chlorine dataset was used as variables of the turbidity estimation model. As a result, the case of applying both variables(pH and residual chlorine) were shown more reasonable estimation results than models only using each variable. However, the estimation model developed in this study were shown to have underestimated predictions for the peak observed values. To overcome this disadvantage, a high-pass filter method was introduced as a pretreatment of estimation model. Modified model using high-pass filter method showed more exactly predictions for the peak observed values as well as improved prediction performance than the conventional model.

• Journal of Korea Water Resources Association
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• v.47 no.4
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• pp.343-356
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• 2014

The SWMM (storm water management model) has been widely used in the world and is a watershed runoff simulation model used for a single event or a continuous simulation of runoff quantity and quality. However, there are many uncertain parameters in the watershed runoff continuous simulation module and the water quality module, which make it difficult to use the SWMM. The purpose of the study is to develop an automatic calibration module of the SWMM not only for watershed runoff continuous simulation, but also water quality simulation. The automatic calibration module was developed by linking the SWMM with the SCE-UA (shuffled complex evolution-University of Arizona) that is a global optimization algorithm. Estimation parameters of the SWMM were selected and search ranges of them were reasonably configured. The module was validated by calibration and verification of the watershed runoff continuous simulation model and the water quality model for the Donghyang Stage Station Basin. The calibration results for watershed runoff continuous simulation model were excellent and those for water quality simulation model were generally satisfactory. The module could be used in various studies and designs for watershed runoff and water quality analyses.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.1241-1242
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• 2005

• Journal of Korean Society of Environmental Engineers
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• v.30 no.8
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• pp.760-767
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• 2008

A watershed model was constructed using HSPF(Hydrological Simulation Program - Fortran) for predicting flow and suspended solid in the Imha dam watershed. The whole watershed was divided into 33 sub-watersheds in the watershed model, which was calibrated for flow using measured data from 2001 to 2007. The accuracy of watershed model prediction was evaluated using statistical coefficients of R$_{eff}$(Nash-Sutcliffe), R$^2$(Correlation coefficient) and graphical comparison. Then, the model was calibrated for suspended solid using field data measured during 3 major rainfall events in July 2006, and then validated against data obtained in 2 rainfall events from July to August in 2007. Overall, the model showed good agreements with the field measurements for flow and suspended solid. The watershed model constructed in this study can provide flow and suspended solid entering the Imha reservoir and will be utilized for turbid water management in linkage with reservoir water quality models.

• Journal of Korea Water Resources Association
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• v.46 no.11
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• pp.1041-1052
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• 2013

In this study, we estimate parameters of a distributed hydrologic model, GRM (grid based rainfall-runoff model), using a model-independent parameter estimation tool, PEST. We implement auto calibration of model parameters such as initial soil moisture, multipliers of overland roughness and soil hydraulic conductivity in the Geumho River Catchment and the Gamcheon Catchment using radar rainfall estimates and ground-observed rainfall represented by Thiessen interpolation. Automatic calibration is performed by GRM-MP (multiple projects), a modified version of GRM without GUI (graphic user interface) implementation, and "Parallel PEST" to improve estimation efficiency. Although ground rainfall shows similar or higher cumulative amount compared to radar rainfall in the areal average, high spatial variation is found only in radar rainfall. In terms of accuracy of hydrologic simulations, radar rainfall is equivalent or superior to ground rainfall. In the case of radar rainfall, the estimated multiplier of soil hydraulic conductivity is lower than 1, which may be affected by high rainfall intensity of radar rainfall. Other parameters such as initial soil moisture and the multiplier of overland roughness do not show consistent trends in the calibration results. Overall, calibrated parameters show different patterns in radar and ground rainfall, which should be carefully considered in the rainfall-runoff modelling applications using radar rainfall.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1207-1216
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• 2018

Discharge or water level predictions at tidally affected river reaches are currently still a great challenge in hydrological practices. This research aims to predict water level of the tide dominated site, Jamsu bridge in the Han River downstream. Physics-based hydrodynamic approaches are sometimes not applicable for water level prediction in such a tidal river due to uncertainty sources like rainfall forecasting data. In this study, TensorFlow deep learning framework was used to build a deep neural network based LSTM model and its applications. The LSTM model was trained based on 3 data sets having 10-min temporal resolution: Paldang dam release, Jamsu bridge water level, predicted tidal level for 6 years (2011~2016) and then predict the water level time series given the six lead times: 1, 3, 6, 9, 12, 24 hours. The optimal hyper-parameters of LSTM model were set up as follows: 6 hidden layers number, 0.01 learning rate, 3000 iterations. In addition, we changed the key parameter of LSTM model, sequence length, ranging from 1 to 6 hours to test its affect to prediction results. The LSTM model with the 1 hr sequence length led to the best performing prediction results for the all cases. In particular, it resulted in very accurate prediction: RMSE (0.065 cm) and NSE (0.99) for the 1 hr lead time prediction case. However, as the lead time became longer, the RMSE increased from 0.08 m (1 hr lead time) to 0.28 m (24 hrs lead time) and the NSE decreased from 0.99 (1 hr lead time) to 0.74 (24 hrs lead time), respectively.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.3
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• pp.63-73
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• 2013

The objectives of this study were to develop a hydrologic simulation model to estimate surface drainage for irrigation districts consisting of paddy and protected cultivation, and to evaluate the applicability of the developed model. The model consists of three sub-models; agricultural supply, paddy block drainage, and protected cultivation runoff. The model simulates daily total drainage as the sum of paddy field drainage, irrigation canal drainage, and protected cultivation runoff at the outlets of the irrigation districts. The agricultural supply sub-model was formulated considering crop water requirement for growing seasons and agricultural water management loss. Agricultural supply was calculated for use as input data for the paddy block sub-model. The paddy block drainage sub-model simulates paddy field drainage based on water balance, and irrigation canal drainage as a fraction of agricultural supply. Protected cultivation runoff is calculated based on NRCS (Natural Resources Conservation Service) curve number method. The Idong reservoir irrigation district was selected for surface drainage monitoring and model verification. The parameters of model were calibrated using a trial and error technique, and validated with the measured data from the study site. The model can be a useful tool to estimate surface drainage for irrigated districts consisting of paddy and protected cultivation.

• Journal of Korean Society on Water Environment
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• v.31 no.5
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• pp.507-522
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• 2015

In this study, some of the model verification results of STREAM (Spatio-Temporal River-basin Ecohydrology Analysis Model), a newly-developed hybrid watershed model, are presented for the runoff processes of nonpoint source pollution. For verification study of STREAM, the model was applied to a test watershed and a sensitivity analysis was also carried out for selected parameters. STREAM was applied to the Mankyung River Watershed to review the applicability of the model in the course of model calibration and validation against the stream flow discharge, suspended sediment discharge and some water quality items (TOC, TN, TP) measured at the watershed outlet. The model setup, simulation and data I/O modules worked as designed and both of the calibration and validation results showed good agreement between the simulated and the measured data sets: NSE over 0.7 and $R^2$ greater than 0.8. The simulation results also include the spatial distribution of runoff processes and watershed mass balance at the watershed scale. Additionally, the irrigation process of the model was examined in detail at reservoirs and paddy fields.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.1274-1275
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• 2005