Forecast of daily rainfall, inflow to Sirikit reservoir and downstream discharges along Nan river | |
| Author | Nikhom Saterngram |
| Call Number | AIT Thesis no.WM-03-04 |
| Subject(s) | Neural networks (Computer science) Rain and rainfall--Sirikit reservoir--Forecasting |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of the Master of Engineering |
| Publisher | Asian Institute of Technology |
| Abstract | Accurate forecast of inflows to reservoir is of particular interest for reservoir operation and scheduling. For particular reservoir equipped with control gates, improved criteria for gates operation during flood period can be assessed if reliable forecasts of inflows to the reservoir are available. Prediction of rainfall should be considered for the extension of lead-time of forecast. This study presents the application of the Artificial Neural Networks (ANN) for daily rainfall time series forecasting in Nan river basin and prediction of inflows to Sirikit reservoir and daily streamflow at main gagging station along Nan river. The Nan river basin is situated in the northern part of Thailand and drains water from the upper basin to Sirikit reservoir. The characteristic of the upper part of Nan river basin is mountainous area which the width is relative small compared to the length of the basin. Due to the reason, rainfall from the catchment contributes to runoff rather quickly and sometimes cause flood. At present, real time operation of Sirikit reservoir in Nan river during flood periods frequently causes severe flooding in downstream areas especially in the vicinity of Uttradit and Phitsanulok provinces. The excessive release of Sirikit reservoir is due to lacking of information of local inflows from downstream tributaries of Nan river. The success of flood forecasting for more than three days in advance in the Nan river was found to depend heavily on the accuracy of rainfall forecast. Hence, rainfall forecast is necessary for an extension of lead-time of food forecast beyond 3 days in advance. The ANN models (WinNN32) have been applied for daily rainfall time series forecasting based on the Singular-Spectrum Analysis (SSA) with Principle Component Analysis technique. The ANN models cannot be directly applied to forecast discontinuous signals, like rainfall time series, as the universal function approximation theorems for neural networks system that requires the continuity of the function to be approximated. In order to avoid the effect of discontinuous of a signal, the SSA technique is applied to forecast the signal by decomposing the raw rainfall time series into reconstructed components. Then, reconstructed components are simulated by ANNs to obtain forecasted rainfall time series for several lead-times (one day, two days and three days in advance). After that, forecasted rainfall is obtained as inputs for prediction of inflows to the Sirikit reservoir and streamflow at main gauging stations along the Nan river downstream of the Sirikit dam. The results of the forecast of inflows to Sirikit reservoir and streamflow at downstream stations are found to be satisfactory for 1-5 days in advance. |
| Year | 2004 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Civil and Infrastucture Engineering (DCIE) |
| Academic Program/FoS | Water Engineering and Management (WM) |
| Chairperson(s) | Tawatchai Tingsanchali |
| Examination Committee(s) | Babel, Mukand Singh ;Dutta, Dushmanta |
| Scholarship Donor(s) | The Oil Refinery Contract Contribution Fund Committee, Energy Policy and Planning Office Bangkok, Thailand |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 2004 |