ارزیابی روش‌های داده‌کاوی و آمار دومتغیره در پهنه‌بندی خطر وقوع گسل‌ (مطالعه موردی: حوضه قره‌قوم)

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار گروه مهندسی طبیعت و گیاهان دارویی، دانشکده کشاورزی و منابع طبیعی، دانشگاه تربت‌حیدریه، خراسان رضوی، ایران

2 کارشناس ارشد آبخیزداری، شرکت سامانه آب کومش، خراسان رضوی، ایران

3 کارشناس گروه آموزشی مدیریت مناطق بیابانی، مرکز تحقیقات بین‌المللی بیابان، دانشگاه تهران، ایران

چکیده

گسل از فرآیندهای اصلی زمین­ریختی است که با پدیده‌هایی هم­چون زمین‌لرزه ارتباط دارد. لذا آگاهی از اثر عوامل محیطی در بروز گسل و شناخت مناطق پرخطر اهمیت زیادی دارد که این اهداف در پژوهش حاضر بررسی شده­اند. عرصه پژوهش حوضه قره­قوم است که پس از تعیین گسل­های آن، متغیرهای ارتفاع، شیب، جهت، اقلیم، کاربری، ژئومورفولوژی، سنگ­شناسی، فرسایش، بارش، پهنه­های سیلابی، خاک­شناسی، شاخص­های قدرت جریان، ناهمواری زمین و فاصله از جاده، آبراهه، تاقدیس و ناودیس آن استخراج گردید. سپس الگوریتم­های داده­کاوی شامل درخت­تصمیم، جنگل­تصادفی، تجمیعی­بوستینگ، ماشین­بردارپشتیبان، رگرسیون­لوجیستیک و شبکه­عصبی در نرم‌افزار R جهت شناسایی ارزش متغیرها و روش­های آماری دومتغیره شامل ارزش اطلاعات و تراکم سطح برای شناسایی ارزش کلاس­های هر متغیر در وقوع گسل برازش شد. ارزیابی دقت طبقه­بندی الگوریتم­ها با منحنی ROC نشان داد الگوریتم­های جنگل­تصادفی و ماشین­بردارپشتیبان به ترتیب با سطح زیر منحنی  88 و 86 درصد بهترین عملکرد را در طبقه­بندی وقوع گسل بر مبنای متغیرهای ورودی دارند. در نهایت با ضرایب جینی در الگوریتم جنگل تصادفی، نقشه­های پهنه­بندی به­دست آمده از ترکیب این الگوریتم با روش­های آماری دومتغیره،تهیه و اعتبارسنجی شد. طبق این الگوریتم، به­ترتیب متغیرهای ارتفاع، خاک­شناسی و شاخص ناهمواری زمین مهم‌ترین پارامترها در وقوع گسل شناخته شدند. طبق ارزیابی نقشه­های پهنه­بندی­، در روش ارزش اطلاعات و تراکم سطح به­ترتیب حدود 52 و 35 درصد گسل­ها در کلاس خطر خیلی­زیاد واقع شدند. لذا روش ارزش اطلاعات در تعیین پهنه­های حساس به وقوع گسل دقت بیش­تری داشت. با توجه به نتایج، روش­های داده­کاوی به عنوان ابزاری مفید در مدیریت ریسک گسل معرفی گردید. همچنین لزوم توجه به متغیرهای محیطی به­ویژه توپوگرافی در مراحل مدیریت و تغییر کاربری حوضه ضرورت می­یابد.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation of data mining and bivariate statistical methods in risk zoning of fault occurrence (Case study: Qara-Qum watershed)

نویسندگان [English]

  • Mehdi Bashiri 1
  • Seyedeh Maedeh Kavosi Davodi 2
  • Ali Afzali 3
1 Assistant Professor, Department of Nature Engineering and Medicinal Plants, Faculty of Agriculture and Natural Resources, University of Torbat Heydarieh, Razavi-Khorasan, Iran
2 M.Sc. in Watershed Management, Samane-Ab-Koomesh Company, Razavi-Khorasan, Iran
3 M.Sc. in Watershed Management, Department of Management of Desert Regions, International Desert Research Center, Tehran University, Iran
چکیده [English]

Fault is one of the main processes in tectonics which has relationship with phenomena such as earthquake. Therefore, awareness of the effect of environmental factors on fault occurrence and recognition of high risk areas is very important, that these goals in the present study have been investigated. The research area is Qara-Qum where after determining its faults, altitude, slope, direction, climate, land use, geomorphology, geology, erosion, precipitation, flood zones, pedology, stream power, topographic ruggedness index, distance from road, waterway, anticline and syncline were extracted. Then, data mining algorithms including of decision tree, random forest, cumulative, backing machine, logistic regression and neural network in R software are used to identify the value of variables and bivariate statistical methods including of information value and area density for identification of the values for each variable class fitted in fault occurrence. The accuracy of classification algorithms with ROC curve showed that based on input variables, random forest and support vector machine algorithms with 88% and 86% area under a curve had the best performance in classifying fault occurrence, respectively. Finally, according to the Gini coefficients in random forest algorithm, the zoning maps obtained by combining this algorithm were prepared and validated by bivariate statistical methods. According to this algorithm, the height, pedology and topographic ruggedness index variables, identified as the most important parameters in the fault occurrence respectively. Based on the zoning maps evaluation, information value and area density methods, around 52 and 35 percent of faults placed in very high risk class respectively. Therefore, the information value method was more accurate in identifying fault-sensitive zones. According to the results, data mining methods were introduced as a useful tool in fault risk management. It is also necessary to pay attention to environmental variables, especially topography, during the basin management and land use change stages.

کلیدواژه‌ها [English]

  • Density Area
  • Information Value
  • ROC curve
  • Classification Algorithm
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