الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Water quality (WQ) data are essential for water resources management as well as WQ management. However, due to limited fiscal and/or technical resources, in many cases, historical WQ data may not be available for the watershed(s) of interest. In addition, gauged watersheds may suffer from missing records and or short gauged sites. Almost all WQ databases worldwide suffer from missing records and short gauged sites. Very limited research has been conducted for the estimation of WQ data or information at ungauged streams/watersheds. The main goal of this thesis was to propose simple-to-use statistical approaches towards a data-gap-free WQ database.
The thesis is divided into two main parts; in the first part a nonparametric statistical approach was proposed for the estimation of WQ characteristics at completely ungauged watersheds.
The second part comprises evaluations of newly proposed hybrid À Trous-Regression technique for the replacement of missing WQ records and the extension of short gauged WQ records.
In thesis part I, four nonparametric approaches were based on the region of influence (ROI) and Theil-Sen nonparametric multiple regression (TSMR). The ROI was used to identify watersheds similar to the ungauged watershed, and TSMR was calibrated and validated to estimate WQ quartiles in ungauged watersheds. This study is the first to estimate water quality parameters using the ROI approach under nonstationary conditions.
The main proposed approach combined the ROI and TSMR. In the second approach, the Spearman correlation analysis was introduced as a filter, to identify watershed attributes that are highly correlated with the WQ quartile, to improve the ROI performance, (CO)ROI-TSMR. In the third approach, a step forward selection technique was integrated with the TSMR to facilitate the selection of the model predictors (watershed attributes), ROI-(SF) TSMR. In the fourth approach, both the (CO)ROI and (SF)TSMR were used, (CO)ROI-(SF)TSMR. The four nonparametric statistical approaches were calibrated and validated to estimate the three main quartiles for three different WQ indicators using data from 50 watersheds in the Nile Delta, Egypt. A Jackknife validation procedure was applied to evaluate the performance of the four approaches. The results indicated that the (CO)ROI-(SF) TSMR approach outperformed the three other approaches.
In thesis Part II, several statistical regression techniques are employed to substitute missing values or to extend records at short-gauged sites, such as the Kendall-Theil robust line (KTRL), its modified version (KTRL2), ordinary least squares regression (OLS), four MOVE techniques, and the robust line of organic correlation (RLOC). In this study, in aspiring to achieve better accuracy and precision, the À Trous-Haar wavelet transform (WT) was adopted as a data denoising preprocessing step prior to applying record extension techniques. An empirical study was performed using real WQ data, from the National WQ monitoring network in the Nile Delta in Egypt, to evaluate the performance of these eight record-extension techniques with and without the WT data preprocessing step. Evaluations included the accuracy and precision of the techniques when used for the restoration of WQ missing values and for the extension of the WQ short-gauged variable. The results indicated that for the restoration of missing values, the KTRL and WT-KTRL outperformed other techniques. However, for the extension of short-gauged variables, WT-KTRL2, WT-MOVE3, and WT-MOVE4 techniques showed more accurate and precise results compared with both other techniques and their counterparts without the WT.