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GIS-Based Decision Support System for Safe and Sustainable Building Construction Site in a Mountainous Region

Author

Listed:
  • Javed Mallick

    (Department of Civil Engineering, College of Engineering, King Khalid University, Abha 61411, Saudi Arabia)

  • Abdulaziz Awad Ibnatiq

    (Department of Civil Engineering, College of Engineering, King Khalid University, Abha 61411, Saudi Arabia)

  • Nabil Ben Kahla

    (Department of Civil Engineering, College of Engineering, King Khalid University, Abha 61411, Saudi Arabia)

  • Saeed Alqadhi

    (Department of Civil Engineering, College of Engineering, King Khalid University, Abha 61411, Saudi Arabia)

  • Vijay P. Singh

    (Department of Biological and Agricultural Engineering, Texas A & M University, College Station, TX 77843-2117, USA)

  • Pham Viet Hoa

    (Ho Chi Minh City Institute, Vietnam Academy of Science and Technology, Ho Chi Minh City 008428, Vietnam)

  • Hoang Thi Hang

    (Department of Urban Environmental & Remote Sensing, Natural Science, Jamia Millia Islamia, New Delhi 110025, India)

  • Nguyen Van Hong

    (Institute of Geography, Vietnam Academy of Science and Technology, Hanoi 100000, Vietnam)

  • Hoang Anh Le

    (Faculty of Environmental Sciences, VNU University of Science, Vietnam National University (VNU), 334 Nguyen Trai, Thanh Xuan, Hanoi 100000, Vietnam)

Abstract

The site selection process for a building entails evaluating a variety of factors with varying degrees of importance or percentage influence. In order to ensure that critical site selection factors are not overlooked, a methodology for calculating a building’s safe site selection must be developed. The study identified three broad aspects widely considered in site selection, namely environmental, physical, and socioeconomic criteria. To assess the safest site selection of residential building construction for sustainable urban growth, we used GIS-based multi-criteria decision-making approach that combined Fuzzy-AHP and weighted linear combination (WLC) aggregation method used to calculate the SSPZ. The final safe site suitability map was generated by aggregating all aspects such as geophysical, socio-economic and Geo-environmental thematic layers and their associated Fuzzy-AHP weights using the weighted linear combination method. The sites potential index’s mean value of 0.513 with standard deviation of 0.340, minimum and maximum GeoPhySSSI are 0.0 and 0.91, respectively, SSS index is classified into zones by histogram profile using natural breaks (jenks)” Subsequently, safe sites identified and divided into six classes namely no construction, very low suitable site low suitable site, moderate suitable site, high suitable site, and very high suitable site.“ According to the statistical analysis, 3.64% and 32.12% of the total area were under very high and high SSSZ, while 26.40% and 6.22% accounted to the moderate and low suitable potential, respectively ” Our findings suggest that integrating the fuzzy collection with AHP is highly desirable in terms of alternative and decision-making effectiveness. The study reveals that the areas of high and moderate suitability are located near existing habitant area, major roads, and educational and health services; they are not located in restricted/protected areas or are vulnerable to natural hazards. The findings indicate that unsuitable and less-suitable land uses such as vegetation, protected areas, and agriculture lands cover nearly one-third area of Abha-Khamis Mushyet regions, implying that using Fuzzy-AHP and GIS techniques will significantly aid in the conservation of the environment. This would significantly mitigate adverse effects on the ecosystem and climate.

Suggested Citation

  • Javed Mallick & Abdulaziz Awad Ibnatiq & Nabil Ben Kahla & Saeed Alqadhi & Vijay P. Singh & Pham Viet Hoa & Hoang Thi Hang & Nguyen Van Hong & Hoang Anh Le, 2022. "GIS-Based Decision Support System for Safe and Sustainable Building Construction Site in a Mountainous Region," Sustainability, MDPI, vol. 14(2), pages 1-33, January.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:2:p:888-:d:723968
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