Premium
Application of tank, NAM, ARMA and neural network models to flood forecasting
Author(s) -
Tingsanchali Tawatchai,
Gautam Mahesh Raj
Publication year - 2000
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/1099-1085(20001015)14:14<2473::aid-hyp109>3.0.co;2-j
Subject(s) - flood forecasting , artificial neural network , flood myth , environmental science , meteorology , autoregressive–moving average model , hydrology (agriculture) , computer science , autoregressive model , geology , artificial intelligence , econometrics , geography , mathematics , geotechnical engineering , archaeology
Two lumped conceptual hydrological models, namely tank and NAM and a neural network model are applied to flood forecasting in two river basins in Thailand, the Wichianburi on the Pasak River and the Tha Wang Pha on the Nan River using the flood forecasting procedure developed in this study. The tank and NAM models were calibrated and verified and found to give similar results. The results were found to improve significantly by coupling stochastic and deterministic models (tank and NAM) for updating forecast output. The neural network (NN) model was compared with the tank and NAM models. The NN model does not require knowledge of catchment characteristics and internal hydrological processes. The training process or calibration is relatively simple and less time consuming compared with the extensive calibration effort required by the tank and NAM models. The NN model gives good forecasts based on available rainfall, evaporation and runoff data. The black‐box nature of the NN model and the need for selecting parameters based on trial and error or rule‐of‐thumb, however, characterizes its inherent weakness. The performance of the three models was evaluated statistically. Copyright © 2000 John Wiley & Sons, Ltd.