Land subsidence susceptibility modeling using random forest approach (Case study: Tasuj plane catchment)

Subject Areas : Applications in natural hazard and disaster

Davoud Mokhtari ¹ (Prof. Department of Geomorphology, Faculty of Planning and Environmental Sciences, Tabriz University)
Hamid Ebrahimy ² (PhD Student of Remote Sensing and Geographical Information System, Shahid Beheshti University)
Saeed Salmani ³ (MSc. Graduated of Remote Sensing and Geographical Information System, University of Tabriz)

Received: 2017-10-19 Accepted : 2019-09-30 Published : 2019-09-23

Keywords: Land subsidence, Random forest algorithm, Groundwater level, Tasuj plane,

Abstract :

Land subsidence occurrence in the Tasuj plane might become more frequent and hazardous in the near future due to its relationship with the water crisis and drought periods. In order to mitigate the damage caused by land subsidence, it is necessary to determine the susceptible or prone areas. The purpose of this study is to produce land subsidence susceptibility map based on the random forest approach to land subsidence occurrence data and eleven environmental variables  that have significant influence on land subsidence occurrences (altitude, slope, aspect, distance to drainage line, drainage density, distance from the fault, topographic wetness index, land cover, lithology, groundwater level and decline in groundwater level) were used as inputs of the random forest model. The random forest approach was applied to produce the land subsidence susceptibility map. The performance of the model was assessed using the receiver operating characteristics (ROC) curve and the area under the curve (AUC). The model results indicate the accuracy of 0.86. Based on the result of the mean decrease accuracy method, the most important conditioning factors were groundwater level, distance from the fault, and a decline in groundwater level, respectively. According to the result, about 18% and 11% of the study area was located within high to very high susceptibility classes. The result of this study can be used by stakeholders and local authorities to mitigate related hazards of land subsidence occurrences in the study area. 

References:

امیراحمدی، ا.، ن. معالی اهری و ط. احمدی. 1392. تعیین مناطق فرونشست احتمالی دشت اردبیل با استفاده از GIS. جغرافیا و برنامه‌ریزی، 17(46): 1-23.

حبیب­زاده، ا.، ش. روستایی و م. ر. نیکجو. 1393. ارزیابی هیدرودینامیکی و ژئومورفولوژیکی نهشته ­های کواترنری تحلیل بحران آب­های زیرزمینی شمال دریاچه ارومیه (مطالعه موردی دشت تسوج). پژوهش­ های ژئومرفولوژی کمی، 3(2): 1-15.

رضایی بنفشه، م.، ط. جلالی عنصری، م. ضرغامی و ا. اصغری مقدم. 1394. بررسی تأثیر اقلیم بر تراز آب زیرزمینی حوزه آبریز تسوج به روش ریزمقیاس نمایی آماری. تحقیقات منابع آب ایران، 11(2): 106-116.

رهنما، ح. و س. میراثی. 1395. تحلیل و ارزیابی پارامتر­های مؤثر بر فرونشست زمین. عمران مدرس، 16(1): 45-53.

شادفر، ص.، ا. نصیری، س. چیتگر و ع. احمدی. 1394. پهنه‌بندی خطر فرونشست زمین با استفاده از روش تحلیل سلسله مراتبی (AHP)، ناحیه موردمطالعه (شهر بوئین‌زهرا). فصلنامه جغرافیایی سرزمین، 12(48): 101-116.

قاسمی، ا.، ا. فلاح و ش. شتایی جویباری. 1395. ارزیابی چهار الگوریتم پیش­بینی سطح تاج­پوشش جنگل­ های مانگرو با استفاده از تصاویر دوربین هوایی. سنجش‌ازدور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 7(2): 1-15.

قندی، ا. و ا. اصغری مقدم. 1384. بررسی عوامل مؤثر بر کیفیت آب زیرزمینی دشت تسوج. نهمین همایش انجمن زمین‌شناسی ایران. تهران - انجمن زمین‌شناسی ایران، دانشگاه تربیت‌معلم. 7 تا 9 شهریورماه، 1-6.

ملکی، ا. و پ. رضایی. 1395. پیش­بینی مکان­های در معرض خطر فرونشست دشت کرمانشاه با استفاده از مدل فازی. برنامه‌ریزی و آمایش فضا، 20(1): 235-251.

ندیری، ع.، ا. اصغری مقدم، ه. عبقری و ا. فیجانی. 1392. توسعه مدل­ های هوش مصنوعی مرکب در برآورد قابلیت انتقال آبخوان، مطالعه موردی: دشت تسوج. تحقیقات منابع آب ایران، 9(1): 1-14.

ندیری، ع.، ا. اصغری مقدم، ه. عقبری، ع. کلانتری اسکویی، ع. حسین پور و ا. حبیب زاده. 1393. مدل منطق فازی در تخمین قابلیت انتقال آبخوان‌ها مطالعه موردی: دشت تسوج. نشریه دانش آب‌وخاک، 24(1): 209-223.

Al-Halbouni D, Holohan EP, Saberi L, Alrshdan H, Sawarieh A, Closson D, Walter TR, Dahm T. 2017. Sinkholes, subsidence and subrosion on the eastern shore of the Dead Sea as revealed by a close-range photogrammetric survey. Geomorphology, 285: 305-324.

Arpaci A, Malowerschnig B, Sass O, Vacik H. 2014. Using multi variate data mining techniques for estimating fire susceptibility of Tyrolean forests. Applied Geography, 53: 258-270.

Billi A, Valle A, Brilli M, Faccenna C, Funiciello R. 2007. Fracture-controlled fluid circulation and dissolutional weathering in sinkhole-prone carbonate rocks from central Italy. Journal of Structural Geology, 29(3): 385-395.

Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32.

Caramanna G, Ciotoli G, Nisio S. 2008. A review of natural sinkhole phenomena in Italian plain areas. Natural Hazards, 45(2): 145-172.

Chen W, Xie X, Wang J, Pradhan B, Hong H, Bui DT, Duan Z, Ma J. 2017. A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena, 151: 147-160.

Choi J-K, Kim K-D, Lee S, Won J-S. 2010. Application of a fuzzy operator to susceptibility estimations of coal mine subsidence in Taebaek City, Korea. Environmental Earth Sciences, 59(5): 1009-1022.

Cutler DR, Edwards Jr TC, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ. 2007. Random forests for classification in ecology. Ecology, 88(11): 2783-2792.

Day M. 1983. Doline morphology and development in Barbados. Annals of the Association of American Geographers, 73(2): 206-219.

De Waele J, Gutiérrez F, Parise M, Plan L. 2011. Geomorphology and natural hazards in karst areas: a review. Geomorphology, 134(1-2): 1-8.

Deverel SJ, Rojstaczer S. 1996. Subsidence of agricultural lands in the Sacramento‐San Joaquin Delta, California: Role of aqueous and gaseous carbon fluxes. Water Resources Research, 32(8): 2359-2367.

Galloway DL, Jones DR, Ingebritsen SE. 1999. Land subsidence in the United States, vol 1182. US Geological Survey, 177 p.

Galve JP, Gutiérrez F, Remondo J, Bonachea J, Lucha P, Cendrero A. 2009. Evaluating and comparing methods of sinkhole susceptibility mapping in the Ebro Valley evaporite karst (NE Spain). Geomorphology, 111(3-4): 160-172.

Gao Y, Alexander E, Barnes RJ. 2005. Karst database implementation in Minnesota: analysis of sinkhole distribution. Environmental Geology, 47(8): 1083-1098.

Gutiérrez F, Parise M, De Waele J, Jourde H. 2014. A review on natural and human-induced geohazards and impacts in karst. Earth-Science Reviews, 138: 61-88.

Hosmer Jr DW, Lemeshow S, Sturdivant RX. 2013. Applied logistic regression, vol 398. John Wiley & Sons, 528 p.

Kim K-D, Lee S, Oh H-J. 2009. Prediction of ground subsidence in Samcheok City, Korea using artificial neural networks and GIS. Environmental Geology, 58(1): 61-70.

Lamelas M, Marinoni O, Hoppe A, De La Riva J. 2008. Doline probability map using logistic regression and GIS technology in the central Ebro Basin (Spain). Environmental Geology, 54(5): 963-977.

Lee S, Park I, Choi J-K. 2012. Spatial prediction of ground subsidence susceptibility using an artificial neural network. Environmental Management, 49(2): 347-358.

Liaw A, Wiener M. 2002. Classification and regression by randomForest. R news, 2(3): 18-22.

Moore ID, Grayson R, Ladson A. 1991. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5(1): 3-30.

Motagh M, Djamour Y, Walter TR, Wetzel H-U, Zschau J, Arabi S. 2007. Land subsidence in Mashhad Valley, northeast Iran: results from InSAR, levelling and GPS. Geophysical Journal International, 168(2): 518-526.

Nicodemus KK. 2011. Letter to the editor: On the stability and ranking of predictors from random forest variable importance measures. Briefings in bioinformatics, 12(4): 369-373.

Oh H-J, Lee S. 2010. Assessment of ground subsidence using GIS and the weights-of-evidence model. Engineering Geology, 115(1-2): 36-48.

Pal M. 2005. Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1): 217-222.

Park I, Lee J, Saro L. 2014. Ensemble of ground subsidence hazard maps using fuzzy logic. Open Geosciences, 6(2): 207-218.

Perrin J, Cartannaz C, Noury G, Vanoudheusden E. 2015. A multicriteria approach to karst subsidence hazard mapping supported by weights-of-evidence analysis. Engineering Geology, 197: 296-305.

Pourghasemi HR, Kerle N. 2016. Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environmental Earth Sciences, 75(3): 185.

Pourtaghi ZS, Pourghasemi HR, Aretano R, Semeraro T. 2016. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecological Indicators, 64: 72-84.

Pradhan B. 2013. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, 51: 350-365.

Pradhan B, Abokharima MH, Jebur MN, Tehrany MS. 2014. Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Natural Hazards, 73(2): 1019-1042.

Prasad AM, Iverson LR, Liaw A. 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, 9(2): 181-199.

Rahmati O, Pourghasemi HR, Melesse AM. 2016. Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: a case study at Mehran Region, Iran. Catena, 137: 360-372.

Regmi AD, Yoshida K, Nagata H, Pradhan AMS, Pradhan B, Pourghasemi HR. 2013. The relationship between geology and rock weathering on the rock instability along Mugling–Narayanghat road corridor, Central Nepal Himalaya. Natural Hazards, 66(2): 501-532.

Shao Y, Lunetta RS. 2012. Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points. ISPRS Journal of Photogrammetry and Remote Sensing, 70: 78-87.

Sharma P, Jones CE, Dudas J, Bawden GW, Deverel S. 2016. Monitoring of subsidence with UAVSAR on Sherman Island in California's Sacramento–San Joaquin Delta. Remote Sensing of Environment, 181: 218-236.

Taheri K, Gutiérrez F, Mohseni H, Raeisi E, Taheri M. 2015. Sinkhole susceptibility mapping using the analytical hierarchy process (AHP) and magnitude–frequency relationships: A case study in Hamadan province, Iran. Geomorphology, 234: 64-79.

Trigila A, Iadanza C, Esposito C, Scarascia-Mugnozza G. 2015. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology, 249: 119-136.