In this study, the group method of data handling (GMDH)-based wavelet transform (WT) was developed to forecast significant wave height (SWH) in different lead times. The SWH dataset was collected from a buoy station located in the North Atlantic Ocean. For this purpose, the time series of SWH was decomposed into some subseries using WT and then decomposed time series were imported to the GMDH model to forecast the SWH. Performance of the wavelet group method of data handling (WGMDH) model was evaluated using an index of agreement (Ia), coefficient of efficiency and root mean square error. The analysis proved that the model accuracy is highly dependent on the decomposition levels. The results showed that the WGMDH model is able to forecast the SWH with a high reliability.
Forecasting of significant wave height (SWH) is necessary for most of ocean engineering activities. Different models have been applied to forecast SWH at various lead times. Here, group method of data handling as a data learning machine method is used to forecast the SWH for next 3, 6 and 12. The SWH data are collected from station 41036 located in the North Atlantic Ocean. The model performance was evaluated using three different index including root mean square error (RMSE), coefficient of correlation (R) and index of agreement (I a). The results shows that in short lead times, the predicted significant wave height mostly correlated to the observed significant wave height but in larger lead times this correlation decreased.
Streamflow forecasting, as one of the most important issues in hydrological studies, plays a vital role in several aspects of water resources management such as reservoir operation, water allocation, and flood forecasting. In this study, wavelet-gene expression programming (WGEP) and wavelet-M5 prime (WM5P) techniques, as two robust artificial intelligence (AI) models, were applied for forecasting the monthly streamflow in Khoshkroud and Polroud Rivers located in two basins with the same names. Results of hybrid AI techniques were compared with those achieved by two stand-alone models of GEP and M5P. Seven combinations of hydrological (H) and meteorological (M) variables were considered to investigate the effect of climatic variables on the performance of the proposed techniques. Moreover, the performance of both stand-alone and hybrid models were evaluated by statistical criteria of correlation of coefficient, root-mean-square error, index of agreement, the Nash–Sutcliffe model efficiency coefficient, and relative improvement. The statistical results revealed that there is a dependency between ‘the M5P and GEP performance’ and ‘the geometric properties of basins (e.g., area, shape, slope, and river network density)’. It was found that a preprocessed technique could increase the performance of M5P and GEP models. Compared to the stand-alone techniques, the hybrid AI models resulted in higher performance. For both basins, the performance of the WM5P model was higher than the WGEP model, especially for extreme events. Overall, the results demonstrated that the proposed hybrid AI approaches are reliable tools for forecasting the monthly streamflow, while the meteorological and hydrometric variables are taken into account.
In this study, we present a method to carry out flood frequency analysis when the assumption of stationary is not valid. A wavelet transform model is used to flood discharge estimation. A full series is applied to flood discharge estimation using two different wavelet functions. The energy function of wavelet was used to estimate flood discharge. The data were decomposed into some details and one approximation through different wavelet functions and decomposition levels. The approximation series was employed to estimate flood discharge. This was performed using daily maximum discharge data from on the Tamer hydrodynamic station in the north of Iran. In this way, the data from 1970 to 2009 were evaluated by wavelet analysis. Results illustrate that the decomposition levels in wavelet transform have a significant role in the flood discharge estimation. For instance, in 100years return period, the flood discharges are 13.06 and 110.92 by Haar (db1) mother wavelet in decomposition level of 1 and 8, respectively. It is shows a more than 8 time growth in flood discharge. The higher decomposition levels are closer to traditional statistical methods such as annual maximum and partial duration series.
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