Applications of data mining in hydrology

Long-term range streamflow forecast plays an invaluable role in water resource planning and management. The potential applicability and limitations of the time series forecasting approach using neural network with the multiresolution learning paradigm (NNMLP) are investigated. The predicted longterm range streamflows using the NNMLP are compared with the observations. The results show that the time series forecasting approach of NNMLP has good predicting skill. The NNMLP requires only historical streamflow information. The time series forecasting approach of NNMLP has great potential for being used alone in regions with limited available information, and for being combined with other approaches to improve long-term range streamflow forecasts.