بررسی کارایی مدل های هوش مصنوعی و آماری دو متغیره در تعیین مناطق حساس به وقوع زمین لغزش در استان آذربایجان غربی

Al-Hinai, H.Y. & Abdalla, R.I.F.A.A.T. (2020). Spatial Prediction of Coastal Flood Susceptible Areas in Muscat Governorate Using an Entropy Weighted Method. WIT Transactions on Engineering Sciences; WIT Press: Southampton, UK, 129: 121-133.

Arca, D., Hacısalihoğlu, M. & Kutoğlu, Ş. H. (2020). Producing forest fire susceptibility map via multi-criteria decision analysis and frequency ratio methods. Natural Hazards, 104(1): 73-89.

Arora, A., Arabameri, A., Pandey, M., Siddiqui, M. A., Shukla, U.K., Bui, D.T., & Bhardwaj, A. (2021). Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India. Science of The Total Environment, 750: 141565.

Bui, D. T., Pradhan, B., Revhaug, I., Nguyen, D. B., Pham, H. V., & Bui, Q. N. (2015). A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). Geomatics, Natural Hazards and Risk, 6(3), 243-271.

Chen, W., Shirzadi, A., Shahabi, H., Ahmad, B. B., Zhang, S., Hong, H., & Zhang, N. (2017a). A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China. Geomatics, Natural Hazards and Risk, 8(2), 1955-1977.

Chen, W., Chen, X., Peng, J., Panahi, M. & Lee, S. (2021). Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer. Geoscience Frontiers, 12(1): 93-107.

Fang, Z., Wang, Y., Peng, L. & Hong, H. (2020). Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping. Computers & Geosciences, 139: 104470.

Gaidzik, K. & Ramírez-Herrera, M. T. (2021). The importance of input data on landslide susceptibility mapping. Scientific reports, 11(1): 1-14.

Khosravi, K., Pham, B.T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., & Bui, D.T. (2018). A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of the Total Environment, 627: 744-755.

Khosravi, K., Pourghasemi, H. R., Chapi, K. & Bahri, M. (2016). Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon’s entropy, statistical index, and weighting factor models. Environmental monitoring and assessment, 188(12): 1-21.

Khosravi, K., Shahabi. H., Pham, B.T., Adamowski, J., Shirzadi, A., Pradhan, B., Dou, J., Ly, H. B., Grof, G., Ho, H. L., Hong, H., Chapi, K. & Prakash, I., (2019). A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods, Journal of Hydrology, 573: 311-323.

Liang, G., Zhu, X. & Zhang, C. (2011). An empirical study of bagging predictors for different learning algorithms. In Twenty-Fifth AAAI Conference on Artificial Intelligence.

Liu, L., Chin, S.P. & Tran, T.D. (2019). Reducing sampling ratios and increasing number of estimates improve bagging in sparse regression. In 2019 53rd Annual Conference on Information Sciences and Systems (CISS) (pp. 1-5). IEEE.

Malekinezhad, H., Sepehri, M., Pham, Q.B., Hosseini, S.Z., Meshram, S.G., Vojtek, M. & Vojteková, J. (2021). Application of entropy weighting method for urban flood hazard mapping. Acta Geophysica: 1-14.

Nampak, H., Pradhan, B., Manap, M.A. (2014). Application of GIS based data driven evidential belief function model to predict groundwater potential zonation, Journal of Hydrology, 513: 283-300.

Ngo, P.T.T., Panahi, M., Khosravi, K., Ghorbanzadeh, O., Kariminejad, N., Cerda, A. & Lee, S. (2021). Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geoscience Frontiers, 12(2): 505-519.

Pham, B.T., Bui, D.T., Pourghasemi, H.R., Indra, P. and Dholakia, M.B. (2017b). 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), pp.255-273.

Pham, B.T., Bui, D.T., Prakash, I. & Dholakia, M.B. (2016b). Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS. Natural Hazards, 83(1), pp.97-127.

Pham, B.T., Tien Bui, D., Indra, P., & Dholakia, M.B. (2015b). Landslide susceptibility assessment at a part of Uttarakhand Himalaya, India using GIS–based statistical approach of frequency ratio method. Int J Eng Res Technol, 4, 338-344.

Piao, Y., Piao, M., Jin, C.H., Shon, H.S., Chung, J.-M., Hwang, B., Ryu, K.H. (2015). A new ensemble method with feature space partitioning for high-dimensional data classification. Mathematical Problems in Engineering

Piryonesi, S.M. & El-Diraby, T. E. (2020). Data analytics in asset management: Cost-effective prediction of the pavement condition index. Journal of Infrastructure Systems, 26(1): 04019036.

Pourghasemi, H.R., Pradhan, B., Gokceoglu, C., Mohammadi, M. & Moradi, H.R. (2013). Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arabian Journal of Geosciences, 6(7): 2351-2365.

Pourghasemi, H.R. & Rahmati, O. (2018). Prediction of the landslide susceptibility: Which algorithm, which precision? CATENA, 162: 177-192.

   Raghuvanshi, T.K., Ibrahim, J., Ayalew, D. (2014). Slope stability susceptibility evaluation parameter (SSEP) rating scheme—an approach for landslide hazard zonation. J. Afr Earth. Sci., 99:595-612

Rodriguez, J.J., Kuncheva, L.I., & Alonso, C.J. (2006). Rotation Forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(10), 1619-1630.

Sarkar, D., Saha, S. and Mondal, P. 2021. GIS-based frequency ratio and Shannon's entropy techniques for flood vulnerability assessment in Patna district, Central Bihar, India. International Journal of Environmental Science and Technology: 1-22.

Sassa, K. & Canuti, P. eds., (2008). Landslides-disaster risk reduction. Springer Science & Business Media.

Shirzadi, A., Bui, D.T., Pham, B.T., Solaimani, K., Chapi, K., Kavian, A., Shahabi, H. and Revhaug, I. (2017a). Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environmental Earth Sciences, 76(2), 60.

Sun, D., Shi, S., Wen, H., Xu, J., Zhou, X. & Wu, J. (2021). A hybrid optimization method of factor screening predicated on GeoDetector and Random Forest for Landslide Susceptibility Mapping. Geomorphology, 379: 107623.

Terlien, M.T., van Westen, C.J., & van Asch, T.W. (1995). Deterministic modelling in GIS-based landslide hazard assessment. In A. Carrara & F. Guzzetti (Eds.), Geographical Information Systems in Assessing Natural Hazards, PP: 57-77. Netherlands: Springer.

Youssef, A.M. & Pourghasemi, H.R. (2021). Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia. Geoscience Frontiers, 12(2): 639-655.