000			03430nas|a2200397 i 4500
005			20260817162826.0
008			041006s2004 th u|m rtt 0| a1eng d
035			_a.b11942022
099		9	_aAIT Thesis no.WM-03-01
100	0		_aChau Nguyen Xuan Quang
245	1	0	_aApplication of a neuro - fuzzy technique in streamflow forecasting
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2004
300			_a51, 12, 7, 3 leaves
490	1		_aThesis ; _vno. WM-03-01
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering
502			_aThesis (M.Eng.) - Asian Institute of Technology, 2004
520			_aThe 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.
650		0	_aStreamflow _xForecasting
650		0	_aFuzzy logic
700	0		_aTawatchai Tingsanchali, _eChairperson
700	1		_aClemente, Roberto S., _eExamination Committee
700	0		_aManukid Parnichkun, _eExamination Committee
710	2		_aGovernment of the Netherlands, _eScholarship donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. WM-03-01
856			_3Full-Text _u http://203.159.5.9/ait-thesis/detail.php?q=B09101
907			_a.b11942022 _bmnait _cu
902			_a240423
998			_b0 _c041006 _dm _ea _fu _g0
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942			_c22
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