Determination of landslide risk areas in Giresun province using Google Earth Engine (GEE) and Best Words Methods (BWM)erinin belirlenmesi
Keywords:
Disaster Management Landslide, Landslide Sensitivity Map, Landslide Susceptibility Parameters, Google Earth Engine, Best Worst MethodAbstract
Our planet has been facing natural hazards since the human population has existed. Numerous events have caused millions of casualties and significant destruction. On the one hand, natural hazards are a major threat to human communities all over the world. However, on the other hand, these events help us learn from our mistakes, help scientists to better understand nature, protect society and benefit from this knowledge.
When analyzed in terms of the number of events in our country, landslides are one of the natural disasters that we frequently encounter. In this article, the concept of disaster and landslide is mentioned in general. It is aimed to produce landslide susceptibility map of Giresun city Centre. In the production of the susceptibility map, GIS stratified best-worst method (BWM) method, which is found to be widely used in the literature, was used. In the evaluations, eleven factors that are mainly effective by reviewing the literature on the occurrence of landslides in Giresun province were taken into consideration: Topographic index, elevation, slope, aspect, land cover, proximity to the river, surface water, NDVI, surface temperature. During the study, these factors were downloaded using Google Earth Engine software language and then mapped in ArcGIS and a new landslide susceptibility assessment model was proposed by integrating GIS stratified best-worst method (BWM).
References
Akgün, A., & Türk, N. (2010). Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multivariate statistical techniques. Environmental Earth Sciences, 59(7), 1639–1653.
Aleotti, P., & Chowdhury, R. (1999). Landslide hazard assessment: Summary review and new perspectives. Bulletin of Engineering Geology and the Environment, 58(1), 21–44.
Corominas, J., van Westen, C., Frattini, P., Cascini, L., Malet, J. P., Fotopoulou, S., ... & Smith, J. T. (2014). Recommendations for the quantitative analysis of landslide risk. Bulletin of Engineering Geology and the Environment, 73(2), 209–263.
Carrara, A., Cardinali, M., Guzzetti, F., & Reichenbach, P. (1991). GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms, 16(5), 427–445.
Carrara, A., Cardinali, M., Guzzetti, F., & Reichenbach, P. (1991). GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms, 16(5), 427–445.
Clerici, A., Perego, S., Tellini, C., & Vescovi, P. (2006). A GIS-based automated procedure for landslide susceptibility mapping by the conditional analysis method: The Baganza Valley case study (Italian Northern Apennines). Environmental Geology, 50(7), 941–961
Çevik, E., & Tolon, M. (2003). Landslide susceptibility mapping for a part of Antalya city, Turkey. Environmental Geology, 44(6), 654–664.
Demir, G., Gökçeoğlu, C., & Sönmez, H. (2013). Landslide susceptibility mapping by using logistic regression in the medium scale: Seydişehir region (Konya, Turkey). Environmental Earth Sciences, 69(3), 1005–1017.
Howard, A. D., Dietrich, W. E., & Seidl, M. A. (1988). Modeling fluvial erosion on regional to continental scales. Journal of Geophysical Research: Solid Earth, 93(B7), 7185–7192.
Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A. X., & Chen, W. (2018). Application of artificial neural networks in landslide susceptibility assessment: A comparative analysis of supervised learning algorithms. Natural Hazards, 93, 31–61.
Kheybari, S. (2021). An integrated approach of BWM and TOPSIS for sustainable supplier selection considering behavioral criteria. Journal of Cleaner Production, 281, 125278.
Lee, S., & Dan, D. (2005). Probabilistic landslide susceptibility mapping using GIS and remote sensing data at Boun, Korea. International Journal of Remote Sensing, 26(7), 1477–1491.
Lee, S., & Min, K. (2001). Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology, 40(9), 1095–1113.
Liu, P., Mao, L., & Zhang, Y. (2017). An extended BWM method for decision making with uncertain preferences. Applied Soft Computing, 60, 432–442.
Mi, X., You, X., & Liu, Y. (2019). An extended best–worst method and its application in emergency management. Technological Forecasting and Social Change, 143, 208–216.
Montrasio, L., & Valentino, R. (2008). A model for triggering mechanisms of shallow landslides. Natural Hazards and Earth System Sciences, 8(5), 1149–1159.
Papathanassiou, G., Pavlides, S., & Ganas, A. (2013). Landslide susceptibility mapping in a rapidly urbanized area of Greece using statistical methods and GIS. Environmental Earth Sciences, 70(2), 749–764.
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57.
Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126–130.
Rezaei, J. (2021). Anchoring bias in best-worst method: A countermeasure model. Computers & Industrial Engineering, 153, 107060.
Rozos, D., Bathrellos, G. D., & Skilodimou, H. D. (2011). Comparison of the implementation of rock engineering system and analytic hierarchy process methods, upon landslide susceptibility mapping. Environmental Earth Sciences, 63(1), 49–63.
Umar, Z., Pradhan, B., Ahmad, A., Jebur, M. N., & Tehrany, M. S. (2014). Earthquake induced landslide susceptibility mapping using an ensemble statistical index (SI) and logistic regression (LR) model applied to the Kashmir region. Natural Hazards, 73, 291–308.
Zizioli, D., Meisina, C., Valentino, R., & Montrasio, L. (2013). Comparison between different approaches for stability assessment of shallow landslides in fine soils. Landslides, 10(3), 407–420.
Wu, Y., Liu, Y., & Zhao, Q. (2016). Landslide susceptibility mapping using an integrated model with information value and logistic regression methods in the Three Gorges Reservoir Area (TGRA), China. Geomatics, Natural Hazards and Risk, 7(6), 2064–2083.
