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شبیهسازی مکانی ظرفیت اندوختههای آب زیرزمینی آبخیز تلوار با مدلهای ماشین بردار پشتیبان و جنگل تصادفی | ||
| پژوهش های آبخیزداری | ||
| مقاله 7، دوره 35، شماره 1 - شماره پیاپی 134، فروردین 1401، صفحه 73-87 اصل مقاله (1.79 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmrj.2021.354418.1407 | ||
| نویسندگان | ||
| ادریس سیفی سلسله1؛ محمدصدیق قربانی* 2؛ نگین َعالی2 | ||
| 1دانشجوی کارشناسی ارشد ژئومورفولوژی، گروه جغرافیا، دانشگاه پیام نور، ایران | ||
| 2استادیار ژئومورفولوژی، گروه جغرافیا، دانشگاه پیام نور، ایران | ||
| چکیده | ||
| در این پژوهش ظرفیت اندوختههای آب زیرزمینی بخشهای گوناگون آبخیز تلوار با دو مدل یادگیری ماشینی بردار پشتیبان و جنگل تصادفی شناسایی شد. اطلاعات چاههای منطقه از شرکت آب منطقهیی کردستان گرفته شد. چاههای موجود در منطقه بهشیوهی تصادفی به دو گروه آموزش (70% از دادهها) و اعتبارسنجی (30% از دادهها) تقسیم شد. عاملهای ارتفاع، شیب زمین، جهت شیب، سنگشناسی، خاکشناسی، انحنای سطح، کاربری زمین، شاخص رطوبت پستیوبلندی و فاصله از رود متغیرهای پیشبینیکننده انتخاب، و نقشهی آنها در سامانهی اطلاعات جغرافیایی تهیه شد. دادههای گروه آموزش به همراه نقشههای متغیرهای پیشبینیکننده به مدل ماشین بردار پشتیبان و مدل جنگل تصادفی وارد کردهشد. متغیرهای مدل برپایهی دادههای گروه آموزش تنظیم شد، و برپایهی آن توان اندوختههای آب زیرزمینی پیشبینی شد. دقت پیشبینی مدلها با روش آماری منحنی مشخصهی عملکرد در دو مرحلهی آموزش و اعتبارسنجی تعیین شد. نتیجهها نشان داد که دقت مدل جنگل تصادفی (98/4%) بیشتر از ماشین بردار پشتیبان (98/1%) است. | ||
| کلیدواژهها | ||
| آبخیز تلوار؛ آب زیرزمینی؛ سامانهی اطلاعات جغرافیایی؛ شبیهسازی | ||
| عنوان مقاله [English] | ||
| Spatial Modeling of Groundwater Resources Potential in Telvar Watershed using Support Vector Machine and Random Forest Models | ||
| نویسندگان [English] | ||
| Edris Sayfi Selseleh1؛ Mohammad Sedigh Ghorbani2؛ Negin Aali2 | ||
| 1M.Sc. Student, Department of Geography, Payame Noor University, Iran | ||
| 2Assistant Professor, Department of Geography, Payame Noor University, Iran | ||
| چکیده [English] | ||
| In this research, it is tried to identify the potential status of groundwater resources in different parts of the Telvar watershed using two machine learning models including support vector machine and random forest models. Initially, information about the wells in the region was received from the Regional Water Company of Kurdistan. The wells in the area were randomly divided into two groups of training (including 70% of data) and validation (including 30% of data). Elevation, slope, slope direction, lithology, pedology, surface curvature, land use, topographic moisture index and distance from the river were selected as predictor variables and their map was prepared in the GIS environment. The data of the training group along with the maps related to the predictor variables were entered into the support vector machine model and the random forest model. Based on the data of the training group, the parameters of the model were calibrated and adjusted and the potential of groundwater resources was predicted. The prediction accuracy of the models was determined using the statistical method of performance characteristic curve in two stages of training and validation. The results showed that accuracy of the random forest model (98.4%) was more than the support vector machine model (98.1%). | ||
| کلیدواژهها [English] | ||
| GIS, groundwater, modeling, Telvar watershed | ||
| مراجع | ||
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