A GIS-based water quality management for Shatt Al-Arab River system, South of Iraq

Abduljaleel, Hind Yahya; Schüttrumpf, Holger; Azzam, Rafig

doi:10.18154/RWTH-2020-09237

A GIS-based water quality management for Shatt Al-Arab River system, South of Iraq

Abduljaleel, Hind Yahya^RWTH*

2020

Verantwortlichkeitsangabevorgelegt von Hind Yahya Abduljaleel, M.Sc.

ImpressumAachen 2020

Umfang1 Online-Ressource (xviii, 173 Seiten) : Illustrationen, Diagramme, Karten

Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2020

Veröffentlicht auf dem Publikationsserver der RWTH Aachen University

Genehmigende Fakultät
Fak05

Hauptberichter/Gutachter
Azzam, Rafig (Thesis advisor)^RWTH* ; Schüttrumpf, Holger (Thesis advisor)^RWTH*

Tag der mündlichen Prüfung/Habilitation
2020-08-03

Online
DOI: 10.18154/RWTH-2020-09237
URL: https://publications.rwth-aachen.de/record/802117/files/802117.pdf

Einrichtungen

Inhaltliche Beschreibung (Schlagwörter)
AHP-RNN (frei) ; Shatt al Arab River (frei) ; decision support system (frei) ; management (frei) ; predicative models (frei) ; water quality (frei)

Thematische Einordnung (Klassifikation)
DDC: 550

Kurzfassung
-

River water pollution and surface water pollution result when contaminants are released by untreated wastewater into natural water bodies. Even if this water is not used as drinking water, the polluted water can be a very acute problem, because it destroys the aquatic ecosystem and can cause epidemical water-borne diseases. The water quality for the coastal rivers deteriorates naturally by salinity intrusion due to its low elevation concerning sea level and when freshwater sources decrease. In order to solve some of these issues, the current research developed water quality assessment, prediction, forecasting, and management models for a complex river system (Shatt al Arab River, SAR) which is contaminated with all types of contamination from the point and non-point sources of pollution. Physical, chemical, and biochemical indicators (Pb, FC, pH, TDS, BOD, DO, and Temp.) analysed in a geographic information system (GIS) combined with the analytic hierarchy process (AHP) to assess and detect the most polluted region in Basra, the data was collected from Basra creeks during summer, 2013 in the case of the lowest low water level. Expert’s opinions were collected and analysed in GIS with the AHP, multi-criteria method allowing for the evaluation of interactive observation points. Daily time-series measurements for discharge, water level, total dissolved solids (TDS), and temperature were used to develop a predictive model based on RNN for predicting and forecasting TDS values at nine sites along the SAR course within the Basra region. Finally, a conceptual decision support system was suggested to integrate these models, databases, and tools into a flexible and interactive solution for the water quality problem in the study area. The analysis of the water quality data for (pH, Cl, O&G, TDS, and, DO), at four stations nearby Basra creeks (SH1, SH2, SH2B, E21) in the period between January 2014 and September 2011, showed that pH values (7.5-8.5) are alkaline and within the normal range of Iraqi rivers. The CL values were low in the upstream and before Basra city (SH1) than in other sites. The sites SH1 and SH2 had higher Q&G values than other sites because they are located within the industrial region of Basra. The TDS data suggested that upstream Basra (SH2) had less TDS concentration than other sites. And the amount of DO was almost within the acceptable range in all the sites (5-10 mg/lit). The calculations of AHP based on the expert’s opinions showed that Pb is the most influential parameter contributing to polluting the water in the Basra region. The importance of this parameter was about 32.73% (11.13) compared to other parameters. This analysis also showed that the parameter pH is the least important parameter for the detection of polluted areas in the Basra region. The relative importance of this parameter was 11.10% or 5.85. The RNN models showed that TDS can be predicted based on the parameters; water level (WL), discharge (Q), and Temperature. The model performed on average as following EVS = 0.64, MAE = 398.11, R2 = 0.63, RMSE = 787.24, and IA = 0.78. The results showed that the model best performed at Abu Flus and Al-Sweeb sites. In addition, the results also suggested that the proposed models can accurately forecast one and multistep ahead TDS values at the examined sites. The forecasting models best performed when 50 historical observations were used to make the predictions. For one step ahead, the model achieved EVS, MAE, R2, RMSE, and IA of 0.91, 239.25, 0.91, 377.09, and 0.98, respectively. For the multistep ahead, the model achieved 0.85, 324.72, 0.85, 493.98, and 0.96, respectively. The forecasting models were tested for 3, 5, and 10 time steps ahead prediction. The results showed that the performance of the model worsen as the longer time steps are required to be predicted. The model best performed when only 3-time steps ahead were predicted.The results from the proposed models suggest that the developed AHP and RNN models could help in better management of water resources in the study area. The results also confirm the significance of GIS for integrating geographical data and displaying interactive results. A list of multiple measures and management objectives related to water quality issues is suggested as a base for developing a decision support system for the SAR system. The protection of water bodies from untreated wastewater by blocking and eliminating point source of pollution, ongoing evaluation and revision of water resources policy, and an increase in the main channel flow from its fresh sources would be the best management practice in order to avoid pollution and to improve the water quality for SAR.

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