1 Khorozyan, I., Soofi, M., Ghoddousi, A., Hamidi, A. K. & Waltert, M. The relationship between climate, diseases of domestic animals and human-carnivore conflicts. Basic and Applied Ecology 16, 703-713 (2015).
2 Sarikhani, N. & Majnonian, B. Guideline for production of forest roads project. J. Plan. Bud. Org. Pub 148, 178-192 (1999).
3 Shokri, M., Safaian, N. & Atrakchali, A. Investigation of the Effects of Fire on Vegetation Variations in Takhti-Golestan National Park. Journal of Natural Enviornment (In persian) 55, 273-281 (2002).
4 Faramarzi, H., Hosseini, S. M., pourghasemi, H. R. & Farnaghi, M. Assessment and Zoning of Flood Risk in Golestan National Park. Eco Hydrology (In persian) 6, 1055-1068 (2020).
5 Lan, H. et al. A web-based GIS for managing and assessing landslide data for the town of Peace River, Canada. Natural Hazards and Earth System Sciences 9, 1433-1443 (2009).
6 Pourghasemi, H. R., Gayen, A., Panahi, M., Rezaie, F. & Blaschke, T. Multi-hazard probability assessment and mapping in Iran. Science of the total environment 692, 556-571 (2019).
7 Mioc, D. et al. Web-GIS application for flood prediction and monitoring. WIT Transactions on Ecology and the Environment 118, 145-154 (2008).
8 Guhathakurta, P., Sreejith, O. & Menon, P. Impact of climate change on extreme rainfall events and flood risk in India. Journal of earth system science 120, 359-373 (2011).
9 Hennequin, T., Sørup, H. J. D., Dong, Y. & Arnbjerg-Nielsen, K. A framework for performing comparative LCA between repairing flooded houses and construction of dikes in non-stationary climate with changing risk of flooding. Science of the total environment 642, 473-484 (2018).
10 Hao, W., Hao, Z., Yuan, F., Ju, Q. & Hao, J. Regional frequency analysis of precipitation extremes and its spatio-temporal patterns in the Hanjiang River Basin, China. Atmosphere 10, 130 (2019).
11 Li, Y. et al. In situ reconstruction of long-term extreme flooding magnitudes and frequencies based on geological archives. Science of the total environment 670, 8-17 (2019).
12 Lyubchich, V., Newlands, N. K., Ghahari, A., Mahdi, T. & Gel, Y. R. Insurance risk assessment in the face of climate change: Integrating data science and statistics. Wiley Interdisciplinary Reviews: Computational Statistics 11, e1462 (2019).
13 Toda, L. L., Yokingco, J. C. E., Paringit, E. C. & Lasco, R. D. A LiDAR-based flood modelling approach for mapping rice cultivation areas in Apalit, Pampanga. Applied Geography 80, 34-47 (2017).
14 Pham, B. T. et al. Flood risk assessment using deep learning integrated with multi-criteria decision analysis. Knowledge-Based Systems 219, 106899 (2021).
15 Norouzi, G. & Taslimi, M. The impact of flood damages on production of Iran’s Agricultural Sector. Middle East J Sci Res 12, 921-926 (2012).
16 Ahmadalipour, A., Moradkhani, H., Castelletti, A. & Magliocca, N. Future drought risk in Africa: Integrating vulnerability, climate change, and population growth. Science of the Total Environment 662, 672-686 (2019).
17 Dai, F. & Lee, C. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42, 213-228 (2002).
18 Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M. & Ardizzone, F. Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72, 272-299 (2005).
19 Turetsky, M. R. et al. Recent acceleration of biomass burning and carbon losses in Alaskan forests and peatlands. Nature Geoscience 4, 27-31 (2011).
20 Zaitsev, A. S., Gongalsky, K. B., Malmström, A., Persson, T. & Bengtsson, J. Why are forest fires generally neglected in soil fauna research? A mini-review. Applied soil ecology 98, 261-271 (2016).
21 Shearman, T. M., Varner, J. M., Hood, S. M., Cansler, C. A. & Hiers, J. K. Modelling post-fire tree mortality: Can random forest improve discrimination of imbalanced data? Ecological Modelling 414, 108855 (2019).
22 Gayen, A., Pourghasemi, H. R., Saha, S., Keesstra, S. & Bai, S. Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms. Science of the total environment 668, 124-138 (2019).
23 Karimi, A., Abdollahi, S., Ostad-Ali-Askari, K., Eslamian, S. & Singh, V. P. Predicting fire hazard areas using vegetation indexes, case study: forests of Golestan province, Iran. Journal of Geography and Cartography 2 (2019).
24 Wang, C. et al. Machine learning-based regional scale intelligent modeling of building information for natural hazard risk management. Automation in Construction 122, 103474 (2021).
25 Keesstra, S. et al. Soil-related sustainable development goals: Four concepts to make land degradation neutrality and restoration work. Land 7, 133 (2018).
26 Ding, Q., Chen, W. & Hong, H. Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping. Geocarto international 32, 619-639 (2017).
27 Allard-Duchêne, A., Pothier, D., Dupuch, A. & Fortin, D. Temporal changes in habitat use by snowshoe hares and red squirrels during post-fire and post-logging forest succession. Forest Ecology and Management 313, 17-25 (2014).
28 Jaiswal, R. K., Mukherjee, S., Raju, K. D. & Saxena, R. Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geoinformation 4, 1-10 (2002).
29 Whittier, T. R. & Gray, A. N. Tree mortality based fire severity classification for forest inventories: a Pacific Northwest national forests example. Forest Ecology and Management 359, 199-209 (2016).
30 Djamali, M. et al. Modern pollen rain–vegetation relationships along a forest–steppe transect in the Golestan National Park, NE Iran. Review of Palaeobotany and Palynology 153, 272-281 (2009).
31 Rad, E. B., Mesdaghi, M., Ahmad, N. & Abdullah, M. Nutritional quality and quantity of available forages relative to demand: a case study of the goitered gazelles of the Golestan National Park, Iran. Rangelands 37, 68-80 (2015).
32 Ghoddousi, S., Pintassilgo, P., Mendes, J., Ghoddousi, A. & Sequeira, B. Tourism and nature conservation: A case study in Golestan National Park, Iran. Tourism management perspectives 26, 20-27 (2018).
33 Van Westen, C., Van Asch, T. W. & Soeters, R. Landslide hazard and risk zonation—why is it still so difficult? Bulletin of Engineering geology and the Environment 65, 167-184 (2006).
34 Schlögl, M. & Matulla, C. Potential future exposure of European land transport infrastructure to rainfall-induced landslides throughout the 21st century. Natural Hazards and Earth System Sciences 18, 1121-1132 (2018).
35 Hu, Q., Zhou, Y., Wang, S. & Wang, F. Machine learning and fractal theory models for landslide susceptibility mapping: Case study from the Jinsha River Basin. Geomorphology 351, 106975 (2020).
36 Zhu, A.-X. et al. An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic. Geomorphology 214, 128-138 (2014).
37 Flannigan, M. D., Stocks, B. J. & Wotton, B. M. Climate change and forest fires. Science of the total environment 262, 221-229 (2000).
38 Syphard, A. D. et al. Predicting spatial patterns of fire on a southern California landscape. International Journal of Wildland Fire 17, 602-613 (2008).
39 Chen, L. et al. Integrating expert opinion with modelling for quantitative multi-hazard risk assessment in the Eastern Italian Alps. Geomorphology 273, 150-167 (2016).
40 Chandra, S. in Geo-information for Disaster management 1239-1254 (Springer, 2005).
41 Fernandez, D. & Lutz, M. Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Engineering Geology 111, 90-98 (2010).
42 Grabs, T., Seibert, J., Bishop, K. & Laudon, H. Modeling spatial patterns of saturated areas: A comparison of the topographic wetness index and a dynamic distributed model. Journal of Hydrology 373, 15-23 (2009).
43 Moore, I. D., Grayson, R. & Ladson, A. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological processes 5, 3-30 (1991).
44 Tehrany, M. S., Jones, S. & Shabani, F. Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques. Catena 175, 174-192 (2019).
45 Thach, N. N. et al. Spatial pattern assessment of tropical forest fire danger at Thuan Chau area (Vietnam) using GIS-based advanced machine learning algorithms: A comparative study. Ecological informatics 46, 74-85 (2018).
46 Hong, H., Naghibi, S. A., Dashtpagerdi, M. M., Pourghasemi, H. R. & Chen, W. A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China. Arabian Journal of Geosciences 10, 167 (2017).
47 Sevinc, V., Kucuk, O. & Goltas, M. A Bayesian network model for prediction and analysis of possible forest fire causes. Forest Ecology and Management 457, 117723 (2020).
48 Nourani, V., Ejlali, R. G. & Alami, M. T. Spatiotemporal groundwater level forecasting in coastal aquifers by hybrid artificial neural network-geostatistics model: a case study. Environmental Engineering Science 28, 217-228 (2011).
49 Ghobadi, G. J., Gholizadeh, B. & Dashliburun, O. M. Forest fire risk zone mapping from geographic information system in Northern Forests of Iran (Case study, Golestan province). International Journal of Agriculture and Crop Sciences 4, 818-824 (2012).
50 Schicker, R. & Moon, V. Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale. Geomorphology 161, 40-57 (2012).
51 Dai, F., Lee, C., Li, J. & Xu, Z. Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology 40, 381-391 (2001).
52 Yerro, A., Soga, K. & Bray, J. Runout evaluation of Oso landslide with the material point method. Canadian Geotechnical Journal 56, 1304-1317 (2019).
53 Sofia, G. & Nikolopoulos, E. Floods and rivers: a circular causality perspective. Scientific reports 10, 1-17 (2020).
54 Agee, J. K. et al. The use of shaded fuelbreaks in landscape fire management. Forest ecology and management 127, 55-66 (2000).
55 Sullivan-Wiley, K. A. & Gianotti, A. G. S. Risk perception in a multi-hazard environment. World Development 97, 138-152 (2017).
56 Gariano, S. L. & Guzzetti, F. Landslides in a changing climate. Earth-Science Reviews 162, 227-252 (2016).
57 Cortez, P. & Morais, A. d. J. R. A data mining approach to predict forest fires using meteorological data. (2007).
58 Hoffmann, W. A., Orthen, B. & Nascimento, P. K. V. d. Comparative fire ecology of tropical savanna and forest trees. Functional Ecology 17, 720-726 (2003).
59 Jaafari, A., Gholami, D. M. & Zenner, E. K. A Bayesian modeling of wildfire probability in the Zagros Mountains, Iran. Ecological informatics 39, 32-44 (2017).
60 Kamranzad, F., MohselAfshar, E., Mojarab, M. & Memarian, H. Landslide Hazard Zonation in Tehran Province Using Data-Driven and AHP Methods. Geoscience (In persian) 25, 101-114 (2016).
61 Duman, T., Can, T., Gokceoglu, C., Nefeslioglu, H. & Sonmez, H. Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environmental Geology 51, 241-256 (2006).
62 Cerri, R. I., Reis, F. A., Gramani, M. F., Giordano, L. C. & Zaine, J. E. Landslides Zonation Hazard: relation between geological structures and landslides occurrence in hilly tropical regions of Brazil. Anais da Academia Brasileira de Ciências 89, 2609-2623 (2017).
63 Zhuo, L. et al. Evaluation of remotely sensed soil moisture for landslide hazard assessment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, 162-173 (2019).
64 Chuvieco, E. et al. Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment 92, 322-331 (2004).
65 Nampak, H., Pradhan, B. & Abd Manap, M. Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. Journal of Hydrology 513, 283-300 (2014).
66 Vilar, L., Woolford, D. G., Martell, D. L. & Martín, M. P. A model for predicting human-caused wildfire occurrence in the region of Madrid, Spain. International Journal of Wildland Fire 19, 325-337 (2010).
67 Cantarello, E. et al. Simulating the potential for ecological restoration of dryland forests in Mexico under different disturbance regimes. Ecological Modelling 222, 1112-1128 (2011).
68 Breiman, L. Random forests. Machine learning 45, 5-32 (2001).
69 Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. Journal of Animal Ecology 77, 802-813 (2008).
70 Pouteau, R., Meyer, J.-Y. & Stoll, B. A SVM-based model for predicting distribution of the invasive tree Miconia calvescens in tropical rainforests. Ecological modelling 222, 2631-2641 (2011).
71 Guisan, A., Edwards Jr, T. C. & Hastie, T. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological modelling 157, 89-100 (2002).
72 Convertino, M., Troccoli, A. & Catani, F. Detecting fingerprints of landslide drivers: a MaxEnt model. Journal of Geophysical Research: Earth Surface 118, 1367-1386 (2013).
73 Rossiter, D. & Loza, A. Analyzing land cover change with logistic regression in R. University of Twente, Faculty of Geo-Information Science & Earth Observation (ITC), Enschede (NL) (2012).
74 Schneider, L. C. & Pontius Jr, R. G. Modeling land-use change in the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment 85, 83-94 (2001).
75 Tingsanchali, T. & Karim, F. Flood-hazard assessment and risk-based zoning of a tropical flood plain: case study of the Yom River, Thailand. Hydrological Sciences Journal–Journal des Sciences Hydrologiques 55, 145-161 (2010).
76 Baldwin, R. A. Use of maximum entropy modeling in wildlife research. Entropy 11, 854-866 (2009).
77 Rahimi, D. & Khadem, S. Analysis Synoptic Patterns for Forest Fires Risk in Northern of
Iran. Natural Environmental Hazards (in persian) 7, 19-36 (2018).
78 Faramarzi, H., Hosseini, S. M., Pourghasemi, H. R. & Farnaghi, M. Forest fire spatial modelling using ordered weighted averaging multi-criteria evaluation. Journal of Forest Science 67, 87-100 (2021).
79 Collins, L., Griffioen, P., Newell, G. & Mellor, A. The utility of Random Forests for wildfire severity mapping. Remote Sensing of Environment 216, 374-384 (2018).
80 Termeh, S. V. R., Kornejady, A., Pourghasemi, H. R. & Keesstra, S. Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Science of the Total Environment 615, 438-451 (2018).
81 Khosravi, K., Pourghasemi, H. R., Chapi, K. & Bahri, M. 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, 1-21 (2016).
82 Abedini, M. & Tulabi, S. Assessing LNRF, FR, and AHP models in landslide susceptibility mapping index: a comparative study of Nojian watershed in Lorestan province, Iran. Environmental Earth Sciences 77, 1-13 (2018).
83 Costanzo, D., Rotigliano, E., Irigaray, C., Jiménez-Perálvarez, J. D. & Chacón, J. Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain). Natural Hazards and Earth System Sciences 12, 327-340 (2012).
84 Pradhan, B. & Lee, S. 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, 747-759 (2010).
85 Faramarzi, H., Hosseini, S. M., Pourghasemi, H. R. & Farnaghi, M. Evaluation of the Asian Highway Role on Fire Golestan National Park in GIS Environment. Wood and Forest science and technology (In persian) 25, 33-48 (2019).
86 Pourghasemi, H. R. & Kerle, N. Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environmental earth sciences 75, 185 (2016).
87 Cutler, D. R. et al. Random forests for classification in ecology. Ecology 88, 2783-2792 (2007).
88 Rahmati, O. et al. Multi-hazard exposure mapping using machine learning techniques: A case study from Iran. Remote Sensing 11, 1943 (2019).
89 Thüring, T., Schoch, M., van Herwijnen, A. & Schweizer, J. Robust snow avalanche detection using supervised machine learning with infrasonic sensor arrays. Cold Regions Science and Technology 111, 60-66 (2015).
90 Pozdnoukhov, A., Purves, R. S. & Kanevski, M. Applying machine learning methods to avalanche forecasting. Annals of Glaciology 49, 107-113 (2008).
91 Arabameri, A. et al. Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility. Science of the Total Environment 688, 903-916 (2019).
92 Shabani, S., Pourghasemi, H. R. & Blaschke, T. Forest stand susceptibility mapping during harvesting using logistic regression and boosted regression tree machine learning models. Global Ecology and Conservation 22, e00974 (2020).
93 Bui, D. T., Tuan, T. A., Klempe, H., Pradhan, B. & Revhaug, I. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13, 361-378 (2016).
94 Pourghasemi, H. R., Jirandeh, A. G., Pradhan, B., Xu, C. & Gokceoglu, C. Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. Journal of Earth System Science 122, 349-369 (2013).
95 Goetz, J. N., Guthrie, R. H. & Brenning, A. Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology 129, 376-386 (2011).
96 Hanspach, J., Kühn, I., Pompe, S. & Klotz, S. Predictive performance of plant species distribution models depends on species traits. Perspectives in Plant Ecology, Evolution and Systematics 12, 219-225 (2010).
97 Abeare, S. Comparisons of boosted regression tree, GLM and GAM performance in the standardization of yellowfin tuna catch-rate data from the Gulf of Mexico lonline [sic] fishery. (2009).