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    Assessment of haditha dam surface area and catchment volume and its capacity to mitigate flood risks for sustainable development
    (International Information and Engineering Technology Association, 2024) Hasan, Raad F.; Seyedi, Mohsen; Alsultani, Riyadh
    The purpose of this study is to assess Haditha Dam’s catchment area and accessible surface area in order to guarantee that these regions can hold water without being at risk of floods. Using topographic data, the study simulated the two-dimensional catchment area and flow area below the dam. The monthly increase in water storage was then computed using the water balance equation and HEC RAS software. These increments were used to determine the required flow that might be utilized to run the dam more efficiently. Significant outflows were found at the start of the operational year. These volumes will probably cause water to accumulate, water levels to increase quickly, and heights to climb. In order to make sure that these regions can store water without running the danger of flooding, the goal of this study is to assess the catchment area of a contemporary dam and its accessible surface area. The study generated a two-dimensional catchment region and flow area below the dam using topography data. The water balance equation and HEC RAS software were then used to determine the monthly increase in water storage. The necessary flow that could be utilized to run the dam as effectively as possible was calculated using these increments. This assessment provides a comprehensive analysis of the dam’s capacity to manage water storage efficiently and mitigate flood risks, contributing to sustainable water management practices.
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    Öğe
    Estimation and analysis of building costs using artificial intelligence support vector machine
    (International Information and Engineering Technology Association, 2023) Salahaldain, Zahra; Naimi, Sepanta; Alsultani, Riyadh
    An essential component of the project feasibility assessment is the conceptual cost estimate. In actuality, it is carried out based on the estimator's prior expertise. However, budgeting and cost control are planned and carried out ineffectively as a result of inaccurate cost estimates. The purpose of this article is to introduce an intelligent model to improve modeling approaches accuracy throughout early phases of a project's development in the construction sector. A support vector machine model, which is computationally effective, is created to calculate the conceptual costs of building projects. To get accurate estimates, the suggested neural network model is trained using a cross-validation method. Through the research of the literature and interviews with experts, the cost estimate's influencing elements are determined. As training instances, the cost information from 40 structures is used. Two potent intelligence methods-Nonlinear Regression (NR) and Evolutionary Fuzzy Neural Interface Model (EFNIM)- are offered to illustrate how well the suggested model performs. Based on the readily accessible dataset from the relevant literature in the construction business, their results are contrasted. The computational findings show that the intelligent model that is being provided outperforms the other two potent methods. During the planning and conceptual design phase, the inaccuracy is satisfied for a project's conceptual cost estimate. Case studies demonstrate how SVMs may help planners anticipate the cost of construction in an effective and precise manner.