Artifical neural network model for rainfall-runoff relationship

Artificial neural networks (ANNs) have emerged as an alternative approach in modeling the runoff process in which the explicit form of the relationship between the variables involved is unknown. This thesis focuses on the implementation of artificial neural network to forecast the three hourly discharge in the small watershed area with limited hydrologic data. Data for model calibration and validation are obtained from only one available hydrologic station (P.64) at the Mae Tuen River in Om Koi District, Chiang Mai Province located in the northern part of Thailand. The watershed is small with approximately 503 square kilometers. It has a distinct hydrologic feature with relatively little information on topography and runoff data. A feedforward backpropagation ANN is used to model and forecast the three hourly discharge. Rainfall-runoff relationship in this studied area was previously investigated by Pukdeboon (2001). He used the Tank model, originally proposed by Sugawara (1974), to forecast the three hourly discharge for the wet- and dry- periods. Based on the same set of hydrologic data, comparisons of the predicted discharge from the Tank model and ANN are presented. The results showed that the average relative error computed from the ANN (10.17%) substantially decreased when comparing with the average relative error computed Tank model (41.35%). The performance evaluation of these two models, based on various statistics, are presented and discussed.