摘要
Effective runoff prediction is one of the main aspects of successful water resources management. One of the most important problems in the modeling of such hydrological processes is the non-stationarities in the data. Several data mining models have deficiencies in handling non-stationary data particularly when signal variations are highly non-stationary. The main objective of this study was to develop a robust model to estimate daily runoff quantities. Firstly, Group Method of Data Handling (GMDH) was used in its single form to model the rainfall-runoff process. Then, the discrete wavelet and wavelet packet transforms were used to decompose the original data to their corresponding components. Thereafter, hybrid models were developed using the wavelet-based analyzed data. Three different rivers were selected to perform these modeling approaches. Results showed that GMDH model had a moderate performance (R2 ≈ 0.84, RMSE ≈ 2.17 m3/s and Max. Error ≈ 24 m3/s for Ghale Chay River). Wavelet transform enhanced the ability of the GMDH model to some extent (R2 ≈ 0.90, RMSE ≈ 1.7 m3/s, and Max. Error ≈ 16 m3/s for Ghale-Chay River). However, it was shown that wavelet packet transform significantly enhanced the ability of the single GMDH model with R2 of 0.94, RMSE of 1.37m3/s, and Maximum Error of about 9.8m3/s for Ghale-Chay River. The results were similar in the other two rivers. It was confirmed that the wavelet packet transform can be effectively used to deal with the non- stationarities in the data and can efficiently enhance the performance of GMDH model.