Development of Prediction - Area charts to improve the output of landslide potential models

Document Type : Original Research

Authors
V. Ghiasi 1
S. Mirzaei 2
M. Yousefi 3
1 Assistant Professor  of Geotechnical EngineeringDepartment of Civil EngineeringFaculty of Civil and Architecture EngineeringMalayer University - IranH/P: (0098)912-537-4295
2 Master of Science, Faculty of engineering, University of Malayer
3 associate PROFESSOR OF MINING ENGINEERING gis
Abstract
Landslide is one of the types of mass movements involving the jogging of rock, earth or aggregate both on the downward slope affected by gravity. This is one of the natural disasters that causes huge loss of life and financial loss in different countries every year. Therefore, it is important to investigate the factors affecting the occurrence of this phenomenon and to determine landslides. The purpose of the present study is to develop a forecast-area rate chart for landslide hazard zoning. Landslide zoning is one of the ways in which the critical areas can be identified in terms of slope stability and the zoning maps obtained in sustainable development planning. Zoning is the division of land into several zones and the classification of these zones according to the degree of actual or potential risk of a phenomenon. Landslide hazard zoning maps can be used as a useful tool for managers and decision makers to determine suitable locations for the development of residential areas and critical arteries. Landslide studies in the past have mostly been done to stabilize instability in natural slopes and to carry out specific projects, and engineers have developed techniques in this field to design structures to control slope instability. Extensive variations in geotechnical properties of materials, internal friction angle (Փ) and adhesion coefficient (C), heterogeneity of natural environments at regional and regional scales compared to homogeneity in local and expensive models, and time-consuming exploration techniques. The desert makes old ways inappropriate. Because this seems unreasonable given the cost to profit ratio, especially in the early stages of decision making in engineering projects. To address this problem, taking into account that landslide risk assessment should be based on a careful study of the natural conditions of an area and all possible parameters involved in slope instability should be analyzed seriously in the area of ​​landslide hazard zoning. Was Initial measures in this area should be based on field studies and analysis of instability relationships in slopes and natural and geographical conditions. The results of engineering geological and geomorphological surveys that pay particular attention to minor landslide issues are generalized to the entire study area with no instability observed in its natural slopes and to sites that are naturally occurring. And geographies of landslide conditions are being searched. For this purpose, after defining a descriptive and conceptual model including landslide hazard zoning, all the features that could be used as suitable criteria were identified and collected in a target model. Then, by analyzing different information layers and weighting those using logistic function, control maps and weighted prediction were obtained. In this regard, for the first time to weighted landslide hazard maps, weighted maps were produced continuously, without classifying and simplifying the data into different parts, as well as minimizing expert judgment. It was also identified using the Prediction - Area chart of each layer and identifying the most important factor affecting landslide occurrence for the first time without expert judgment. Finally, all the weighted maps were integrated using the data-driven overlay index method.It was also able to identify 55% of the landslide points in 45% of the total area. Application of the developed model in the Oshvand-Nahavand watershed proved that modeling the above method can optimally reduce the studied areas and reduce the objectives. Identify for further field surveys.

Keywords

Subjects

[1] Fell, R. (1994). Landslide risk assessment and acceptable risk. Canadian Geotechnical Journal, 31(2), 261-272.
[2] Mafian, S., Huat, B. B. K., and Ghiasi, V. (2009). Evaluation on root theories and root strength properties in slope stability. European Journal of Scientific Research, 30(4), 594-607.
[3] Safaei, M., Omar, H., Huat, B. K., Yousof, Z. B., and Ghiasi, V. (2011). Deterministic rainfall induced landslide approaches, advantage and limitation. Electronic Journal of Geotechnical Engineering, 16, 1619-1650.

[4] Hadji, R., errahmane Boumazbeur, A., Limani, Y., Baghem, M., el Madjid Chouabi, A., & Demdoum, A. (2013). Geologic, topographic and climatic controls in landslide hazard assessment using GIS modeling: a case study of Souk Ahras region, NE Algeria. Quaternary International, 302, 224-237. “(In Persian)".
[5] Ilanloo, M. (2011). A comparative study of fuzzy logic approach for landslide susceptibility mapping using GIS: An experience of Karaj dam basin in Iran. Procedia-Social and Behavioral Sciences, 19, 668-676. "(In Persian)".
[6] Bui, D. T., Pradhan, B., Lofman, O., Revhaug, I., & Dick, O. B. (2012). Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Computers & Geosciences, 45, 199-211.
[7] Yousefi, M., & Carranza, E. J. M. (2015a). Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping. Computers & Geosciences, 74, 97-109.
[8] Yousefi, M., & Carranza, E. J. M. (2015). Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Computers & Geosciences, 79, 69-81.
[9] Pradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 25(6), 747-759.
[10] Mokarram, M., & Zarei, A. R. (2018). Landslide Susceptibility Mapping Using Fuzzy-AHP. Geotechnical and Geological Engineering, 36(6), 3931-3943. "(In Persian)".
[11] Chen, W., Panahi, M., Tsangaratos, P., Shahabi, H., Ilia, I., Panahi, S., & Ahmad, B. B. (2019). Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility. Catena, 172, 212-231.
[12] Kayastha, P., Dhital, M. R., & De Smedt, F. (2013). Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Computers & Geosciences, 52, 398-408.
[13] Adhikari, M. (2011). Bivariate Statistical Analysis of Landslide Susceptibility in Western Nepal (Master's thesis).
[14] Yousefi, M., & Nykänen, V. (2016). Data-driven logistic-based weighting of geochemical and geological evidence layers in mineral prospectivity mapping. Journal of Geochemical Exploration, 164, 94-106.
[15] Yousefi, M., & Carranza, E. J. M. (2015b). Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Computers & Geosciences, 79, 69-81.
[16] Mihalasky, M. J., & Bonham-Carter, G. F. (2001). Lithodiversity and its spatial association with metallic mineral sites, Great Basin of Nevada. Natural Resources Research, 10(3), 209-226.
[17] Yousefi, M., Kamkar-Rouhani, A., & Carranza, E. J. M. (2012). Geochemical mineralization probability index (GMPI): a new approach to generate enhanced stream sediment geochemical evidential map for increasing probability of success in mineral potential mapping. Journal of Geochemical Exploration, 115, 24-35.
Modares Civil Engineering journal
Volume 20, Issue 6
November and December 2020
Pages 127-131