03430nas|a2200397 i 4500 20260817162826.0 041006s2004 th u|m rtt 0| a1eng d .b11942022 AIT Thesis no.WM-03-01 Chau Nguyen Xuan Quang Application of a neuro - fuzzy technique in streamflow forecasting Pathum Thani, Thailand : Asian Institute of Technology, 2004 51, 12, 7, 3 leaves Thesis ; no. WM-03-01 A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering Thesis (M.Eng.) - Asian Institute of Technology, 2004 The neuro - fuzzy technique (NFT), called the Adaptive Neuro - Fuzzy Based Inference System (ANFIS) was employed to forecast daily and weekly streamflow for four gauging stations, namely Yl7, Y4, Y6 and Y20 of the Yom River Basin in Thailand. The model is proposed to forecast the streamflow of one, two and three days and one week in advance. Various inputs of different types and length of training data were tried, using observed daily and weekly discharges, water level and mean areal rainfall series from 1990 to 1999 for calibration (training) and from 2000 to 2001 for verification (testing), to obtain the most accurate results. The accuracy of flood forecast is evaluated by using statistical efficiency index (EI), and root mean square error (RMSE). The results obtained from NFT model were found to be very satisfactory in both daily and weekly streamflow forecasting. The model accuracy decreases when the time of forecasting ahead is increased. However, the accuracy of results of two and three days ahead forecasting are much better when using successive day to day forecast of previous day as the input of the next days forecast. The NFT model results are very close to the results obtained by using multilayer perceptron (MLP) model and are better than the results obtained from multi variable regression (MVR) model. This study presented the application of NFT for daily and weekly streamflow forecasting with promising results. The results also indicate that NFT perfo1m slightly better than MLP and much better than MVR in flood forecasting in te1ms of accuracy. Both NFT and MLP are strongly recommended for streamflow forecasting due to their capacity in nonlinear relationship modeling. Streamflow Forecasting Fuzzy logic Tawatchai Tingsanchali, Chairperson Clemente, Roberto S., Examination Committee Manukid Parnichkun, Examination Committee Government of the Netherlands, Scholarship donor Asian Institute of Technology. Thesis ; no. WM-03-01 Full-Text http://203.159.5.9/ait-thesis/detail.php?q=B09101 .b11942022 mnait u 240423 0 041006 m a u 0 mnait mnarc 22 40 Barcode : 30050160022017 CREATED : 2014-03-31 RECORD # : i1278364x LPATRON : 1028100 LCHKIN : 2019-12-04 # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 3 TOT CHKOUT : 4 TOT RENEW : 16 Barcode : 30050120235964 CREATED : 2016-03-25 RECORD # : i12909567 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 6437 6437 0 0 0 0 MAIN MAIN mnait 2026-08-17 50.00 0 AIT Thesis no.WM-03-01 30050160022017 2026-08-17 16:28:26 3 2026-08-17 22 0 0 0 0 MAIN MAIN mnarc 2026-08-17 0 AIT Thesis no.WM-03-01 30050120235964 2026-08-17 16:28:26 2026-08-17 40