Development of urban growth and water management models in Nasiriyah City, Iraq using GIS and machine learning techniques

Urbanisation and water scarcity risk (WSR) have obstructed sustainable development goals globally. However, traditional approaches in this field do not include a listening concept to people who are vulnerable to water shortage risk and environmental vulnerability when making important decisions. In addition, techniques to measure the overlapping risks of water shortage and environmental vulnerability that threaten urban communities has yet to be developed. Therefore, the current study includes four objectives. The first objective is to develop a comprehensive vulnerability assessment (CVA) to determine vulnerable urban areas. This technique was created by combining three fuzzy logic functions, i.e. fuzzy analytic hierarchy process, fuzzy linear membership, and fuzzy overlay gamma. The second objective is to develop a new machine learning model to predict urban water shortage risk based on public participation (PP), namely WSRPP. The third objective is to develop a new approach to forecast urban boundary growth using ML and GIS, namely UGBF. The last objective is to develop a hybrid model prediction of environmental vulnerability (EV) and WSR, called WSREV model. The hybrid model includes the integration of the three techniques mentioned above (i.e. CVA, WSRPP and UGBF) coupled with Water Evaluation and Planning Model (WEAP). Nasiriyah City in southern Iraq was selected as the study area. Results revealed the following: A total of 11 sectors in the city, as well as over 175,000 people or 25% of the entire population, reside in regions with a high degree of vulnerability. Additionally, 26% live in 17 sectors with very high WSR. The urban area ratio was increased by about 10%, i.e. from 2.5% in the year 1992 to 12.2% in 2022. Moreover, the city will be expanded by 34%, 25% and 19% by the years 2032, 2042 and 2052, respectively. The prediction scenarios showed by the year 2052, under high population growth coupled with the RCP 8.5 path of climate change (HP-RCP 8.5), about 34% of the city’s area will be located under a very high to high degree of risk. By contrast, the situation under the manage measure scenario and RCP 4.5 path of climate change (MS-RCP 4.5) will be only 9% of the city’s area under a very high to high degree of risk.

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