الفهرس 
Chapter One: IntroductionOnly 14 pages are availabe for public view
 |  | from | 140 |  |  |
| | | from | 140 | | |
Abstract
The main objective of this Thesis was to provide an advanced methodology using the latest technologies (GIS, Remote sensing, and Artificial Neural Network) to predict future hydrological valuable information that facilitates planning and decision-making for integrated management regarding mitigating the expected negative environmental impacts due to wastewater disposal. The study area was selected in North-East of Cairo due to the presence of disposal of the largest two conventional wastewater treatment plants in Greater Cairo, the first plant is Gabal EL Asfar WWTP with capacity 2.5 million m3/day and the second is El Berka WWTP with capacity 600,000 m3/day.
The methodology used is based on the identification of water quality characteristics in the study area for the aquifer and surface water by measuring the collected water samples (from the year 2016 to 2018) for eleven parameters include pH, CO3, BOD5, COD, TSS, TDS, SO4, Cl, NH4, NO3, and fecal coliform. The biggest challenge was to obtain acceptable accurate measurements from Landsat images for the canals and drains. The previous studies monitored and assessed the quality of water in rivers, reservoirs, and lakes, which gave clear resolution due to its convenient width, which cannot be achieve in the canal and drains.
Therefore, seven spectral bands were applied with a spatial resolution of 30 m, to obtain consistent results. After analyzing several factors and criteria such as land use/land cover change, geographic location, and elevation, the results obtained concluded that:
- The creation of spatial maps (using GIS technique) for all groundwater samples in the study area showed that the most polluted sites matched with field observation in the convergence area of the main two drains (Bilbays and Gabal El Asfar drains), in addition to the area surrounding the plant’s disposal.
- Spectral bands of Landsat-8 (OLI) showed a significant correlation between eight water quality parameters (CO3, BOD5, COD, TSS, TDS, Cl, NH4, and fecal coliform) and many of the band ratios which reached to 89%, 86%, 77%, 90%, 88%, 82%, 90%, and 81% respectively with band ratio b5/b2, while (pH, SO4, and NO3) parameters did not show any strong positive correlation.
- The verification of the correctness of the developed regression equations for (CO3, COD, TSS, TDS, and NH4), generally viewed as an acceptable, and the difference between the computed and measured values is satisfactory (ranged from 4% to 5.5 %) comparing with saving time and cost for measuring samples.
- The ANN modeling could effectively predict the removal efficiency of El Berka WWTP, due to the high correlation coefficient (R) reported between the measured and predicted output variables, reaching up to 0.98 with ANN structure developed by training and testing observed data of BOD5, COD, TSS, Ammonia and Sulfide measurements over a period of 7 years (2011 to 2017), and it is recommended to use as a rapidly, and not expensive method for plant operators and decision-makers.
The results of this Thesis would participate in supporting the local authorities and decision makers to develop groundwater management and protect our precious water resources, in addition to providing a valuable performance assessment tool for WWTP operators, by:
1- Using the produced spatial distribution maps to identify the location of water contamination sources.
2- Using the predictive models to expect the WWTP effluent.
3- Using the produced linear regression models to reduce field measurements of groundwater and surface water.