Remote Sensing and GIS Applications in Environmental Sciences

Locating construction waste landfills using fuzzy logic and hierarchical analysis process (case study: Ilam city)

Document Type : Original Article

Authors

1 student of Faculty of Planning and Environmental Sciences, Tabriz University

2 Professor of Remote Sensing and GIS Department, Faculty of Planning and Environmental Sciences, Tabriz University

Abstract

The aim of the current research is to locate construction waste disposal sites in Ilam city. Identifying the optimal sites for the disposal of construction waste is one of the basic steps in the field of urban waste management. In the present study, the process of locating construction waste disposal sites in Ilam city was carried out in four basic steps. In the first step, variables affecting the location of construction waste disposal sites were prepared in the form of thematic layers in the framework of the Geographical Information System (GIS) and were spatially evaluated. In the second step, the subject layers were dimensioned and their values were revalued using fuzzy functions. In the third step, the weight of each of the research criteria was calculated using the Analytical Hierarchy Process (AHP) model. In the last step, the subject layers were combined with each other based on the coefficients obtained from the AHP model and the land suitability layer was obtained in order to dispose of construction waste. The results show that the three variables of distance from the city, slope and distance from the main roads, with weights of 0.292, 0.208 and 0.145 respectively, are the most important variables affecting the optimal location of construction waste in Ilam city. are counted Also, two areas located in the southwest and northwest of Ilam city were proposed to create construction waste disposal sites.

Keywords

در تحقیق حاضر به ارزیابی و پهنهبندی خطر زمینلغزش در محدوده مهاباد تا سردشت با کاربرد مدلهای تحلیلی مختلف از قبیل ماشین بردار پشتیبان، جنگل تصادفی و رگرسیون لجستیک پرداخته شده است. روش انجام تحقیق حاضر مبتنی بر روش توصیفی – تحلیلی و کاربرد مقایسهای صحت الگوریتمهای ماشین بردار پشتیبان، جنگل تصادفی و رگرسیون لجستیک بوده است. بر این اساس از دادهها و معیارهای مختلف محیطی در فرآیند تجزیه و تحلیل استفاده گردیده است .ابتدا بر اساس تعیین نقاط نمونه، سه مدل مذکور به منظور تهیه نقشه پهنهبندی زمینلغزش به اجرا درآمده است و سپس بر اساس نتایج، اقدام به ارزیابی و اعتبارسنجی نتایج مدلهای مورد استفاده شده است. نتایج پهنهبندی زمینلغزش محدوده مورد مطالعه حاکی از این بوده است که بهطورکلی نیمه جنوبی منطقه به دلیل تأثیر عواملی از قبیل ساختارهای متراکم گسلی، شیب بالاتر و تراکم بیشتر آبراهه از پتانسیل بالاتری نسبت به نیمه شمالی آن برخوردار است و بر اساس ماشین بردار پشتیبان 90/12 درصد، بر اساس جنگل تصادفی 00/35 درصد و بر اساس رگرسیون لجستیک 50/11 درصد از مجموع وسعت منطقه دارای حساسیت لغزشی متوسط به بالا بوده است. ارزیابی دقت حاصله برای الگوریتمها بر اساس منحنی ROC چنین مشخص نموده است که ماشین بردار پشتیبان، جنگل تصادفی و رگرسیون لجستیک به ترتیب مقدار صحت 17/9، 71/9 و 70/9 را به خود اختصاص دادهاند و از این نظر الگوریتم جنگل تصادفی بهترین دقت را ارائه کرده است. همچنین شاخص Precision – Recall نیز به ترتیب برابر با 790/9، 715/9 و 700/9 به دست آمده است که بیانگر دقت بالاتر الگوریتم جنگل تصادفی نسبت به دو الگوریتم دیگر در زمینه پهنهبندی پتانسیل خطر وقوع زمینلغزش در مسیر مهاباد – سردشت میباشد .

  • Achour, Y. Pourghasemi, H.R. (2020). How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geoscience Frontiers, 11(3); 871-883.
  • Ahmad, MN. Shao, Z. Aslam, RW. Ahmad, I. Liao, M. Li, X. Song, Y. (2022). Landslide hazard, susceptibility and risk assessment (HSRA) based on remote sensing and GIS data models: a case study of Muzaffarabad Pakistan. Stochastic Environmental Research and Risk Assessment. May 27; 1-6.
  • Akinci, H. Zeybek, M. (2021). Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey. Natural Hazards, 108(2); 1515-1543.
  • Anastasiy, Motrenko. Strijov, Vadim. Weber, Gerhard-Wilhelm. (2014). Sample size determination for logistic regression. Journal of Computational and Applied Mathematics 255; 743-752.
  • Bennett, N.D. Croke, B.F. Guariso, G. Guillaume, J.H. Hamilton, S.H. Jakeman, A.J. MarsiliLibelli, S. Newham, L.T. Norton, J.P. Perrin, C. (2013). Characterizing performance of environmental models. Model, 4; 1–20.
  • Chen, W. Peng, J. Hong, H. Shahabi, H. Pradhan, B. Liu, J. Zhu, A.X. Pei, X. Duan, Z. (2018). Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren county, Jiangxi province, China. Total Environ, 626; 1121–1135.
  • Hoang, N.D. Nguyen, Q.L. Bui, D.T. Image Processing-Based Classification of Asphalt Pavement Cracks Using Support Vector Machine Optimized by Artificial Bee Colony. (2018). Comput. Civ. Eng, 32; 04018037.
  • Hong, H. Shahabi, H. Shirzadi, A. Chen, W. Chapi, K. Ahmad, B.B. Roodposhti, M.S. Hesar, A.Y. Tian, Y. Bui, D.T. (2019). Landslide susceptibility assessment at the Wuning area, China: A comparison between multi-criteria decision making, bivariate statistical and machine learning methods. Hazards, 96; 173–212.
  • Hota, H. S. Handa, R Shrivas, A. K. (2017). Time series data prediction using sliding window based RBF neural network. International Journal of Computational Intelligence Research, )5(31; 1145-.6511
  • Kiani, S. Karimkhani, A. Mazidi, A. (2021). Landslide Risk Zoning using Logistic Regression and ANFIS Models in Hashtjin Catchment Area in Ardabil Province. Geography and Environmental Sustainability. 11(2); 55-73. doi: 10.22126/ges.2021.6461.2391. (In Persian).
  • Kornejady, A. Pourghasemi, H. R. (2019). 'Landslide Susceptibility Assessment Using Data Mining Models, A case study: Chehel-Chai Basin', Watershed Engineering and Management, )1(11; 28-42. doi: 10.22092/ijwmse.2019.118436. (In Persian).
  • Mahdavifard, M, Jafari, S. Valizadeh Kamran, K, Karimzadeh, S. (2022). Rapid monitoring of mangrove cover changes using support vector machine algorithm in Google Earth Engine

 

مقایسه الگوریتمهای ماشین بردار پشتیبان، جنگل تصادفی و رگرسیون لجستیک ...   جواد صدیقی ، رضا مالکی    88

computing platform (Case study: Qeshm mangrove forests). Remote Sensing and GIS Applications in Environmental Sciences, 2(2); 36-23. (In Persian).

  • Mersha, T. Meten, M. (2020). GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia. Geoenvironmental disasters, 7(1); 1-22.
  • Miao, T. Wang, M. Susceptibility analysis of earthquake-induced landslide using random forest method. In proceedings of the international conference on computer information systems and industrial applications, Bangkok, Thailand, 28–29 June 2015; 1–1006. 136.
  • Ngo, T. Q. Dam, N. D. Al-Ansari, N. Amiri, M. Phong, T. V. Prakash, I. Pham, B. T. (2021). Landslide susceptibility mapping using single machine learning models: a case study from Pithoragarh District, India. Advances in civil engineering.
  • Niazi, Y. Mendoza, ME. Talebi, A. Bidaki, H. (2021). GIS-based support vector machine model in shallow landslide hazards prediction: A case study on Ilam dam watershed, Iran. Journal of Nature and Spatial Sciences (JONASS). 1(1); 59-84.
  • Pardeshi, SD. Autade, SE. Pardeshi, SS. (2013). Landslide hazard assessment: recent trends and techniques. SpringerPlus. )1(2; 1-.1
  • Su, Q. Zhang, J. Zhao, S. Wang, L. Liu, J. Guo, J. (2017). Comparative assessment of three nonlinear approaches for landslide susceptibility mapping in a coal mine area. ISPRS International Journal of Geo-Information. 6(7); 228.
  • Sukristiyanti, S. Wikantika, K. Sadisun, IA. Yayusman, LF. Soebowo, E. (2021). Preliminary Study of Landslide Susceptibility Modeling with Random Forest Algorithm Using R (Case Study: The Cisangkuy Sub-watershed). InIOP Conference Series: Earth and Environmental Science. 1(1); 012015. IOP Publishing.
  • Taheri, V. Karam, A. Safari, A. Shataii joibari, S. (2020). 'Landslides zoning of Golestan province using combination algorithm least squares support vector machines and artificial bee colony', Geographical Planning of Space, 10(37); 213-230. doi:

10.30488/gps.2021.177771.3022. (In Persian).

  • Tehrani, FS. Calvello, M. Liu, Z. Zhang, L. Lacasse, S. (2022). Machine learning and landslide studies: Recent advances and applications. Natural Hazards. 20; 1-.94
  • Tien Bui, D. Shahabi, H. Shirzadi, A. Chapi, K. Alizadeh, M. Chen, W. Mohammadi, A. Ahmad, B. Panahi, M. Hong, H. (2018). Landslide detection and susceptibility mapping by airsar data using support vector machine and index of entropy models in Cameron highlands, Malaysia. Remote Sens. 10; 1527.
  • Tsangaratos, P. Ilia, I. (2017). Applying machine learning algorithms in landslide susceptibility assessments. In Handbook of Neural Computation. 1: 433-457. Academic Press.

 

 

کاربرد سنجش از دور و GIS در علوم محیطی، شماره 0، سال اول ،پاییز 2092، صص 299-72

Application of remote sensing and GIS in environmental sciences, Vol. 1, No. 4, Fall 2022, pp. 81-100   999

  • Umar, Z. Pradhan, B. Ahmad, A. Jebur, M.N. Tehrany, M.S. (2014). Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena. 118; 124–135.
  • Wang, LJ. Guo, M. Sawada, K. Lin, J. Zhang, J. (2016). A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network. Geosciences Journal. 20(1); 117-36.
  • Wubalem, A. (2021). Landslide susceptibility mapping using statistical methods in Uatzau catchment area, northwestern Ethiopia. Geoenvironmental Disasters. 8(1); 1-.12
  • Yi, Y. Zhang, Z. Zhang, W. Xu, C. (2019). Comparison of different machine learning models for landslide susceptibility mapping. In IGARSS 2019. IEEE International Geoscience and Remote Sensing Symposium. 9318-9321. IEEE.
  • Zhou, X. Wu, W. Lin, Z. Zhang, G. Chen, R Song, Y. Wang, Z. Lang, T. Qin, Y. Ou, P. Huangfu, W. (2020). Landslide risk zoning in Ruijin, Jiangxi, China. Natural Hazards and Earth System Sciences Discussions. 5; 1-.12
Remote Sensing and GIS Applications in Environmental Sciences
Volume 2, Issue 4 - Serial Number 4
October 2023
Pages 79-57
  • Receive Date: 14 December 2022
  • Revise Date: 22 December 2022
  • Accept Date: 30 December 2022