Forecasting the Trend of Landslide Changes in the Northern Region of Quchan with Regard to the Factors Affecting Landslide Using Neural Network, Cellular Automata-Markov, and Regression Logistics

Document Type : مقاله پژوهشی

Authors

• Mohsen Khosravi ¹
• Ali Akbar Jamali ²

¹ Islamic Azad University

² Maybod Branch, Islamic Azad University

10.22067/geo.v0i0.71520

Abstract

Introduction

The landslide is one of the types of mass movements. It consists of the fast or slow movement of stone, soil or the sum of both on the slope downward. Many studies have been carried out by geomorphologists to understand the factors affecting mass movements. However, due to the complexity of such movements and the impact of several factors on it, there is still no definitive and sufficient result in this regard. Generally, many factors can be considered effective in mass movements such as the genus of formation, type of surface materials, topographic conditions (e.g., gravity, slope steepness and aspect), aggregate formation, tectonic condition (proximity to fault), climatic conditions (e.g., rain, surface water, soil moisture), and type of land use (e.g., urban, rural, agriculture, road, and so on). The objective of this study is to forecast the trend of landslide changes with regard to the factors affecting the landslide by neural network, cellular automata-Markov, and regression logistics.

Area of study

The studied area is part of the Tabarkabad basin, one of the main sources of the Atrak river basin located in the north of the city of Quchan and between the east longitudes 58 30 to 59 and north latitudes 37 to 37 20. Geologically, Tabarakabad Basin is part of the Kope-Dagh basin. The basin's boundary is determined by the mountains of Allah Akbar and the anticline of Zobar. The average height of this basin is 1885m, and the average slope is about 23%. In addition, about 50% of the slope of the basin is between 20 to 40 degrees.
 

Materials and methods

Landslides in the area
Landslide dispersion map in all landslide studies from the identification, monitoring, zoning of sensitivity and especially risk assessment, and landslide risk is an integral part and as a basic and important layer.
Landslide susceptibility map using multilayer perceptron (MLP) neural network model
The MLP method in the land-change model is used because this model creates a network of neurons based on the input and output variables. The number of input neurons is equal to the number of variables (10 neurons) and the output neurons is equal to maps for each two classes (landslide and no landslide) as change trend probability. After a given number of repetitions, it is possible to get the lowest root mean square (RMS) error the network uses half of the data for training and with half the other data; it tests the network and gives the least error and most accuracy. After obtaining the highest accuracy of the training and testing of the network, the potential conversion map is provided. These maps determine the likelihood of converting applications into one another. It also describes the degree of effectiveness of each variable in the model. In this case, land use and geological type have the highest effect and the direction of gradient has the lowest role in the accuracy of 80% prediction. Then, a landslide prediction probability trend change map will be produced.
 
Landslide susceptibility map using the logistic regression model
By executing the model and using the probability map, we can identify areas with more potential, also with the proposed logistic function; the model can be used to measure the impact of each independent variable in the model. The positive coefficients have a greater effect and negative coefficients without effect in the model. Considering these land use coefficients, slope, elevation, and ultimately geological type of the area have the highest effect.
 
Prediction of landslide changes using the Cellular Automata, CA-Markov method
CA-Markov provides the images of classified landslides which are analyzed and output in the form of a probabilistic matrix of variations and an output image of the probability matrix of variations for the horizon. The probability change matrix indicates that the probability of each class of landed slip usage in the future will change to another

Discussion and Results

In the present study, landslide changes in a part of the Tabarkabad basin in northern Quchan in three periods of 2006, 2010, and 2016 with Google Earth satellite imagery, landslide-mapping using the multi-layer perceptron neural network, logistic regression, also the application of the Markov forecasting model and the modeling approach of land change modeler (LCM) of landslide changes were predicted for 2032.
Comparison of landslide maps in the mentioned periods indicating an increase in the level of landslide areas. Considering topographic and geological characteristics and climatic conditions governing the area. In addition, the issue of intensifying land use change over the past decade has expanded in scope fluctuations.
The results of geological maps of most of the area are located on the Sanganeh and Sarcheshmeh formations. The effect of land use on landslides has a direct effect.
Factors such as the spread of rainfed land, the degradation of grasslands, and the development of drainage systems from rivers to slopes have been one of the most important reasons for landslide due to land use.
The results showed that, due to the loosening of formations, land use change to poor pastures and agricultural lands, rainfalls increased. Also with Increasing, the altitude (above 1750 m) has reduced the risk of occurrence due to a decrease in gradient.

Conclusion

Finally, in order to evaluate and compare the results, a hybrid model of laminated perception neural network, logistic regression, CA-Markov model for modeling and predicting landslide changes. The predicted results of the three models indicate that the combined model of multi-layer perceptron neural network with a Kappa coefficient of 0.96 was better than the logical regression models and CA-Markov with Kappa coefficients of 0.86 and 0.72. Using a hybrid model of multilayer perceptron neural network for 2016, a prediction map was prepared and according to the acceptable accuracy of the model for the year 2032, a landslide prediction map was extracted.

Keywords

• GIS
• Landslide
• Neural Network
• Markov
• Forecast
• LCM

---

References

احمدی، حسن؛ 1385. ژئومورفولوژی کاربردی. جلد 1 (فرسایش آبی). انتشارات دانشگاه تهران، 577.

امیر احمدی، ابوالقاسم؛ ابراهیمی، مجید؛ زنگنه اسدی، محمدعلی؛ 1392، پهنه‌بندی خطر زمین‌لغزش با استفاده از مدل شبکه عصبی پرسپترون چندلایه از نوع پیش خور پس انتشار (BP) (مطالعه موردی: حوضه آبخیز بار نیشابور). دومین کنفرانس بین المللی مخاطرات محیطی. تهران: دانشگاه خوارزمی. 28.

بلواسی، ایمانعلی؛ رضائی مقدم، محمدحسین؛ نیکجو، محمدرضا؛ ولی زاده، کامران خلیل؛ 1394. مقایسۀ مدل شبکۀ عصبی مصنوعی با فرایند تحلیل سلسله‌مراتبی در ارزیابی خطر زمین‌لغزش. نشریه مدیریت مخاطرات محیطی. دوره 2. شماره 2. 225 - 250.

بهاروند، سیامک؛ سوری، سلمان؛ 1394. پهنه‌بندی خطر زمین‌لغزش با استفاده از روش شبکه عصبی مصنوعی (مطالعة موردی: حوزه سپیددشت، لرستان). مجله سنجش‌ازدور و سامانه اطلاعات جغرافیایی در منابع طبیعی. سال ششم. شماره 4، 15- 32.

پورسلطانی، مهدی رضا؛ قائمی مقدم، مهدی؛ 1392. فیزیوگرافی و زمین ریخت شناسی حوضه آبریز سد تبارک واقع در شمال قوچان و تأثیر آن بر نوع رسوبات. دو فصلنامه یافته‌های نوین زمین‌شناسی کاربردی. دوره 8. شماره 15. 83-97.

شاد فر، صمد؛ یمانی، مجتبی؛ قدوسی، جمال؛ غیومیان، جعفر: 1386. پهنه‌بندی خطر زمین‌لغزش با استفاده از روش تحلیل سلسله مراتبی(مطالعه موردی: حوزه آبخیز چالکرود تنکابن). پژوهش و سازندگی در منابع طبیعی. دوره 20. 126-118: 75.

شریعتی، شهرام؛ یزدانی چمزینی، عبدالرضا؛ سلسانی، آرمین؛1392. پیش‌بینی لرزش زمین با استفاده از مدل ترکیبی رگرسیون چند متغیره وشبکه عصبی. کنفرانس بین المللی عمران. معماری و توسعه پایدار شهری. تبریز: دانشگاه آزاد اسلامی واحد تبریز.

شیرانی، کورش؛ عرب عامری، علیرضا؛ 1394. پهنه‌بندی خطر وقوع زمین‌لغزش با استفاده از روش رگرسیون لجستیک (مطالعه موردی: حوضه دز علیا). مجله علوم و فنون کشاورزی و منابع طبیعی-علوم آب و خاک. جلد ۱۹. شماره ۷۲. ۳۲۱-۳۳۵

علیپور، حمید؛ ملکیان، آرش؛ 1378. پهنه‌بندی خطر زمین‌لغزش در حوضه آبخیز جهان اسفراین خراسان شمالی. فصلنامه جغرافیا و توسعه. 61،17: 641-615.

غفاری زرین، میررضا؛ محمدزاده، علی؛ 1393. مدل‌سازی منطقه‌ای TEC با استفاده از شبکه‏های عصبی مصنوعی و مدل چندجمله‌ای در ایران. نشریه علمی پژوهشی علوم و فنون نقشه برداری. دوره چهارم. شماره 3.

فیض نیا، سادات؛ احمدی، حسن؛ حسن زاده نفوتی، محمد؛1380. پهنه‌بندی خطر زمین‌لغزش حوزه آبخیز شلمانرود در استان گیلان. مجله منابع طبیعی ایران. جلد 54. شماره 3. 207 -219.

قنبرزاده، ‏هادی؛ بهنیافر، ابوالفضل؛ پزشکی، محمود؛ 1385. بررسی علل و عوامل ناپایداری دامنه‏ها در حوضۀ آبریز رودخانه تبارک آباد قوچان. نشریه علوم جغرافیایی 2(1): 102-121.

مقیمی، ابراهیم؛ علوی پناه، سیدکاظم؛ جعفری، تیمور؛ 1387. ارزیابی و پهنه‌بندی عوامل مؤثر در وقوع زمین‌لغزش دامنه‏های شمالی آلاداغ. پژوهش‏های جغرافیایی. 64، 53-75.

مقیمی، محمدحسن؛ 1394. پایش و پیش‌بینی روند تغییرات مکانی کاربری اراضی و توسعه شهری با استفاده از مدل LCM (مطالعه موردی شهر یزد). پایان نامه کارشناسی ارشد. دانشگاه آزاد اسلامی واحد یزد.

Abedi Gheshlaghi, Hassan & Feizizadeh, Bakhtiar., 2017. An integrated approach of analytical network process and fuzzy based spatial decision making systems applied to landslide risk mapping. Journal of African Earth Sciences. 133. 15-24. https:// doi.org/ 10.1016/ j.jafrearsci.2017.05.007

Aditian, A., Kubota, T., & Shinohara, Y., 2018. Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology, 318, 101-111.

Chen, W., Pourghasemi, H., Zhao, Z., 2017. "A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping." Geocarto International 32(4): 367-385. https:// doi.org/ 10.1080/ 10106049. 2016. 1140824

Crosta B.G., 2009. Dating, triggering, odeling and hazard assessment of large landslides. Geomorphology 103, 1 – 4. https://doi.org/10.1016/j.geomorph.2008.04.007

Dou, J., Yamagishi, H., Zhu, Z., Yunus, A. P., & Chen, C. W., 2018. TXT-tool 1.081-6.1 A Comparative Study of the Binary Logistic Regression (BLR) and Artificial Neural Network (ANN) Models for GIS-Based Spatial Predicting Landslides at a Regional Scale. In Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools (pp. 139-151). Springer, Cham.

Hong, H., Liu, J., Bui, D. T., Pradhan, B., Acharya, T. D., Pham, B. T., & Ahmad, B. B., 2018. Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). Catena, 163, 399-413.

Malek, Žiga & Boerboom, Luc & Glade, Thomas., 2015. Future Forest Cover Change Scenarios with Implications for Landslide Risk: An Example from Buzau Subcarpathians, Romania. Environmental Management. 56. https://doi.org/10.1007/s00267-015-0577-y

Mondal, S. and S. Mandal., 2017. "RS & GIS-based landslide susceptibility mapping of the Balason River basin, Darjeeling Himalaya, using logistic regression (LR) model." Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards: 1-16.

Park, S., Choi, C., Kim, B., Kim, J., 2013. "Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea." Environmental Earth Sciences 68(5): 1443-1464. https:// doi.org/ 10.1007/ s12665-012-1842-5

Pham, B. T., Bui, D. T., Pourghasemi, H. R., Indra, P., & Dholakia, M. B., 2017. Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theoretical and Applied Climatology, 128(1-2), 255-273.

Rodrigo F Leandro., 2007. A New Technique to TEC Regional Modeling using a Neural Network. Geodetic Research Laboratory, Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, Canada

Sang L, Zhang C, Yang J, Zhu D, Yun W., 2011. Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Mathematical and Computer Modelling, 54(3): 938-943. https://doi.org/10.1016/j.mcm.2010.11.019

Wang, Qiqing & Li, Wenping & Xing, Maolin & Wu, Yanli & Pei, Yabing & Yang, Dongdong & Bai, Hanying., 2016. Landslide susceptibility mapping at Gongliu county, China using artificial neural network and weight of evidence models. Geosciences Journal. 20. https://doi.org/10.1007/s12303-015-0026-1