Saudi Arabia’s arid and semi-arid regions suffer from water scarcity because of climatic constraints and rapid growth of domestic and industrial water uses. The growing demand for high-quality water supplies and to reduce the dependency on desalination creates an urgent need to explore groundwater resources as an alternative. The weighted overlay analysis method using the fuzzy-analytical hierarchy process (FAHP) multi-criteria decision making (MCDM) techniques combined with geoinformation technology was used in this study to explore the groundwater potential zones in the Itwad-Khamis watershed of Saudi Arabia. Twelve thematic layers were prepared and processed in a GIS setting to produce the groundwater potential zone map (GPZM). Subsequently, potential groundwater areas were delineated and drawn into five classes: very good potential, good potential, moderate potential, poor potential, and very poor potential. The estimated GWPZ (groundwater potential zones) was validated by analyzing the existing open wells distribution and the yield data of selected wells within the studied watershed. With this quality-based zoning, it was found that 82% of existing wells were located in a very good and good potential area. The statistical analysis showed that 14.6% and 28.8% of the total area were under very good and good, while 27.3% and 20.2% were accounted for the moderate and poor potential zone, respectively. To achieve sustainable groundwater management in the Aseer region, Saudi Arabia, this research provided a primary estimate and significant insights for local water managers and authorities by providing groundwater potential zone map.
Concrete manufacturing, a high energy and natural resources demanding process, can play a vital role in sustainable development by offering solutions to environmental and socio-economic issues. Concrete manufactured with siliceous materials can extend concrete life and reduce costs, and judicious management of siliceous utilization can make concrete manufacturing sustainable. A number of industrial and agro-based by-products, waste products, and new engineered materials are being use as siliceous material in concrete. The present research aims to implement the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach, a Multi-Criteria Decision Making (MCDM) technique, for the orderly management of siliceous materials based on sustainable criteria, namely, technical, environmental, social, and economic aspects. The present research adopts twenty indicators of sustainability to evolve a comprehensive model for a sustainability ranking of concrete siliceous materials and to provide siliceous materials management. The present research also provides a methodology for the systematic ranking of sustainable criteria and indicators along with a siliceous materials sustainability order for enhanced sustainable development and management. It can be concluded that the proper material management of siliceous concrete materials, especially nano-engineered materials in construction industry, will help in the conservation of basic concrete materials and environmental protection without direct impact on social development.
The construction sector is garnering increasing concern as an industry that generates significant pollution while suffering from a lack of adequate environmental performance evaluations. The purpose of this study is to evaluate the environmental performance of construction sector industries using the ISO 14031 standard in three dimensions: planning, using data and information, and reviewing and improving environmental performance evaluations. Using a descriptive and correlational approach, this study described and analyzed the level of implementation using a 7-point Likert scale questionnaire distributed to 1000 organizations in Saudi Arabia. The questionnaires were completed between May and December of 2018 and the results were measured against similar studies. The mean positive percentage responses were 31.36 and expressed the implementation levels of the three dimensions of environmental performance. The corresponding values for the three dimensions' items were 33.36, 29.83, and 30.95 respectively. The minimum Kendall correlation coefficients for each item in a dimension were 0.79, 0.79, and 0.83, respectively. The maximum correlation values between an item and a dimension to which the item did not belong were 0.25. This study answers a fundamental question regarding the implementation level values of environmental performance in construction organizations based on the ISO 14031 standard model and analyzes the pairwise correlation among the descriptor variables of the model. Further studies are needed to investigate the model structure using the principle component, factor analysis, and cluster analysis.
The Normalized Difference Vegetation Index (NDVI) and rainfall data were used to model the spatial relationship between vegetation and rainfall. Their correlation in previous studies was typically based on a global regression model, which assumed that the correlation was constant across space. The NDVI–rainfall association, on the other hand, is spatially non-stationary, non-linear, scale-dependent, and influenced by local factors (e.g., soil background). In this study, two statistical methods are used in the modeling, i.e., traditional ordinary least squares (OLS) regression and geographically weighted regression (GWR), to evaluate the NDVI–rainfall relationship. The GWR was implemented annually in the growing seasons of 2000 and 2016, using climate data (Normalized Vegetation Difference Index and rainfall). The NDVI–rainfall relationship in the studied Bisha watershed (an eco-sensitive zone with a complex landscape) was found to have a stable operating scale of around 12 km. The findings support the hypothesis that the OLS model’s average impression could not accurately represent local conditions. By addressing spatial non-stationarity, the GWR approach greatly improves the model’s accuracy and predictive ability. In analyzing the relationship between NDVI patterns and rainfall, our research has shown that GWR outperforms a global OLS model. This superiority stems primarily from the consideration of the relationship’s spatial variance across the study area. Global regression techniques such as OLS can overlook local details, implying that a large portion of the variance in NDVI is unexplained. It appears that rainfall is the most significant factor in deciding the distribution of vegetation in these regions. Furthermore, rainfall had weak relationships with areas predominantly located around wetlands, suggesting the need for additional factors to describe NDVI variations. The GWR method performed better in terms of accuracy, predictive power, and reduced residual autocorrelation. Thus, GWR is recommended as an explanatory and exploratory technique when relations between variables are subject to spatial variability. Since the GWR is a local form of spatial analysis that aligned to local conditions, it has the potential for more accurate prediction; however, a larger amount of data is needed to allow a reliable local fitting.
Decision-making and subjective evaluation are two important aspects that characterizes the involvement of an organization in tender process. Moreover, selection of the right project to bid for is a principal feature of business success. The study aims to investigate the behavioral differences of Saudi construction contractors toward internal and external factors based on the process of modelling the bidding decisions. A quantitative research design is used to investigate the behavioral differences of 97 contractors recruited from construction industry of Saudi Arabia. A questionnaire was distributed among the respondents that would help in identifying the significant level of factors affecting the bid or no bid decision. The impact of internal and external factors on the bidding decisions was evaluated using one-way ANOVA analysis. The results have shown a significant and positive effect of internal and external factors on the bid or no bid decision; including job start time, work capital requirement, availability of qualified human resources, bidding methods, bidding document price, project supervision procedure and etc. The study has helped in establishing a better understanding toward the behavioral differences of contractors with respect to the bidding decisions.
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