دانلود مقاله انگلیسی رایگان:نقشه برداری بالقوه چشمه آب های زیرزمینی با استفاده از الگوریتم های تکاملی مبتنی بر جمعیت و روش های داده کاوی - 2019
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  • Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods
    Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods


    ترجمه فارسی عنوان مقاله:

    نقشه برداری بالقوه چشمه آب های زیرزمینی با استفاده از الگوریتم های تکاملی مبتنی بر جمعیت و روش های داده کاوی


    منبع:

    Sciencedirect - Elsevier - Science of the Total Environment 684 (2019) 31–49


    نویسنده:

    Wei Chen a,b, Paraskevas Tsangaratos c,⁎, Ioanna Ilia c, Zhao Duana, Xinjian Chen d


    چکیده انگلیسی:

    Water scarcity inmany regions of theworld has become an unpleasant reality. Groundwater appears to be one of the main natural resources capable to reverse this situation. Uncovering the spatial patterns of groundwater occurrence is a crucial factor that could assist in carrying out successful water resources management projects. The main objective of the current study was to provide a novel methodology approach which utilized Genetic Algorithm( GA) in order to performa feature selection procedure and data mining methods for generating a groundwater spring potential map. Three data mining methods, Naïve Bayes (NB), Support Vector Machine (SVM) and RandomForest (RF) were utilized to construct a groundwater spring potential map that had over 0.81 probability of occurrence for the Wuqi County, Shaanxi Province, China. Groundwater spring locations and sixteen related variables were analyzed, namely: lithology, soil cover, land use cover, normalized difference vegetation index (NDVI), elevation, slope angle, aspect, planform curvature, profile curvature, curvature, stream power index (SPI), stream transport index (STI), topographic wetness index (TWI), mean annual rainfall, distance from river network and distance from road network. The Frequency ratio method was used to weight the variables, whereas a multi-collinearity analysis was performed to identify the relation between the parameters and to decide about their usage. The optimal set of parameters, which was determined by the GA, reduced the number of parameters into twelve removing planformcurvature, profile curvature, curvature and STI. The Receiver Operating Characteristic curve and the area under the curve (AUROC) were estimated so as to evaluate the predictive power of eachmodel. The results indicated that the optimizedmodels were superior in accuracy than the original models. The optimized RF model produced the best results (0.9572), followed by the optimized SVM (0.9529) and the optimized NB (0.8235). Overall, the current study highlights the necessity of applying feature selection techniques in groundwater spring assessments and also that data miningmethods may be a highly powerful investigation approach for groundwater spring potential mapping.
    Keywords: Groundwater spring potential mapping | Genetic algorithm | Naïve Bayes | Support Vector Machine | Random Forest | China


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 19
    حجم فایل: 9263 کیلوبایت

    قیمت: رایگان


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