강우자료 형태에 따른 인공신경망의 일유입량 예측 정확도 평가
Influence of Rainfall observation Network on Daily Dam Inflow using Artificial Neural Networks
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초록
The objective of this study was to evaluate the influence of rainfall observation network on daily dam inflow using artificial neural networks(ANNs). Chungju Dam and Soyangriver Dam were selected for the study watershed. Rainfall and dam inflow data were collected as input data for constructionof ANNs models. Five ANNs models, represented by Model 1 (In watershed, point rainfall), Model 2 (All in the Thiessen network, point rainfall),Model 3 (Out of watershed in the Thiessen network, point rainfall), Model 1-T (In watershed, area mean rainfall), Model 2-T (All in the Thiessennetwork, area mean rainfall), were adopted to evaluate the influence of rainfall observation network. As a result of the study, the models that usedall station in the Thiessen network performed better than the models that used station only in the watershed or out of the watershed. The models thatused point rainfall data performed better than the models that used area mean rainfall. Model 2 achieved the highest level of performance. The modelperformance for the ANNs model 2 in Chungju dam resulted in the R2 value of 0.94, NSE of 0.94 NSEln of 0.88 and PBIAS of –0.04 respectively. The model-2 predictions of Soyangriver Dam with the R2 and NSE values greater than 0.94 were reasonably well agreed with the observations. Theresults of this study are expected to be used as a reference for rainfall data utilization in forecasting dam inflow using artificial neural networks.
Abstract
The objective of this study was to evaluate the influence of rainfall observation network on daily dam inflow using artificial neural networks(ANNs). Chungju Dam and Soyangriver Dam were selected for the study watershed. Rainfall and dam inflow data were collected as input data for constructionof ANNs models. Five ANNs models, represented by Model 1 (In watershed, point rainfall), Model 2 (All in the Thiessen network, point rainfall),Model 3 (Out of watershed in the Thiessen network, point rainfall), Model 1-T (In watershed, area mean rainfall), Model 2-T (All in the Thiessennetwork, area mean rainfall), were adopted to evaluate the influence of rainfall observation network. As a result of the study, the models that usedall station in the Thiessen network performed better than the models that used station only in the watershed or out of the watershed. The models thatused point rainfall data performed better than the models that used area mean rainfall. Model 2 achieved the highest level of performance. The modelperformance for the ANNs model 2 in Chungju dam resulted in the R2 value of 0.94, NSE of 0.94 NSEln of 0.88 and PBIAS of –0.04 respectively. The model-2 predictions of Soyangriver Dam with the R2 and NSE values greater than 0.94 were reasonably well agreed with the observations. Theresults of this study are expected to be used as a reference for rainfall data utilization in forecasting dam inflow using artificial neural networks.
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