No abstract

Because of their sensitive structure, earth dams might face failure due to seepage phenomenon. In order to prevent such failure, some equipment like piezometers are installed in the body or foundation of earth dams. This study investigated the importance of piezometer installation level in dam body or foundation using mutual information-wavelet-Gaussian process regression. 27 Piezometers in three section along with reservoir level were employed to predict onestep-ahead seepage discharge of Zonouz earth dam. The daily data of 1 year of piezometer level and reservoir level were collected for this purpose. In order to find the best possible input combination, three groups of modeling scenarios were defined using piezometers and reservoir level time series. As some input combinations had more than two variables, decomposed time series were imposed into mutual information (MI) tool in order to decrement input variables and find the most correlated input-output features. Afterward, mentioned features were imposed into optimized Gaussian process regression (GPR) to be predicted. Different kernels were selected as core tool of GPR, but results demonstrated the capability of radial basis function (RBF) kernel. GPR-RBF structure were optimized using cross-validation technique. Results indicated that input combination including piezometer level and reservoir level of section II, especially piezometer 203 time series led to the best result among all scenarios.

In this study, daily river stage–discharge relationship was predicted using different modeling scenarios. Ensemble empirical mode decomposition (EEMD) algorithm and wavelet transform (WT) were used as hybrid pre-processing approach. In the WT-EEMD approach, first temporal features were decomposed using WT. Furthermore, the decomposed sub-series were further broken down into intrinsic mode functions via EEMD to obtain features with higher stationary properties. Mutual information was used to select dominant sub-series and determine efficient input dataset. Relevance vector machine (RVM) was applied to forecast river discharge. Three scenarios were developed to predict river stage–discharge process. First, a successive-station form of forecasting was proposed by incorporating geomorphological features into the modeling process. Subsequently, an integrated RVM (I-RVM) was trained based on the concept of the cascade of reservoirs and the meta-learning approach. The proposed I-RVM had the semi-distributed characteristics of the river discharge model. Finally, a multivariate RVM was trained to predict discharge for different points of the river. For this reason Westhope station's features were used as input to predict discharge at downstream of the river. Results were compared with rating curve and capability of proposed models were approved in prediction of short-term river stage–discharge.

No abstract

The present study proposed a time-space framework using discrete wavelet transform-based multiscale entropy (DWE) approach to analyze and spatially categorize the precipitation variation in Iran. To this end, historical monthly precipitation time series during 1960–2010 from 31 rain gauges were used in this study. First, wavelet-based de-noising approach was applied to diminish the effect of noise in precipitation time series which may affect the entropy values. Next, Daubechies (db) mother wavelets (db5–db10) were used to decompose the precipitation time series. Subsequently, entropy concept was applied to the sub-series to measure the uncertainty and disorderliness at multiple scales. According to the pattern of entropy across scales, each cluster was assigned an entropy signature that provided an estimation of the entropy pattern of precipitation in each cluster. Spatial categorization of rain gauges was performed using DWE values as input data to k-means and self-organizing map (SOM) clustering techniques. According to evaluation criteria, it was proved that k-means with clustering number equal to 5 with Silhouette coefficient=0.33, Davis–Bouldin=1.18 and Dunn index=1.52 performed better in determining homogenous areas. Finally, investigating spatial structure of precipitation variation revealed that the DWE had a decreasing and increasing relationship with longitude and latitude, respectively, in Iran.