this study sought to produce an accurate multi-hazard risk map for a mountainous region of iran. the study area is in southwestern iran. the region has experienced numerous extreme natural events in recent decades. This study models the probabilities of snow avalanches, landslides, wildfires, land subsidence, and floods using machine learning models that include support vector machine (SVM), boosted regression tree (BRT), and generalized linear model (GLM). Climatic, topographic, geological, social, and morphological factors were the main input variables used. the data were obtained from several sources. The accuracies of GLM, SVM, and functional discriminant analysis (FDA) models indicate that SVM is the most accurate for predicting landslides, land subsidence, and flood hazards in the study area. GLM is the best algorithm for wildfire mapping, and FDA is the most accurate model for predicting snow avalanche risk. The values of AUC (area under curve) for all five hazards using the best models are greater than 0.8, demonstrating that the model's predictive abilities are acceptable. A machine learning approach can prove to be very useful tool for hazard management and disaster mitigation, particularly for multi-hazard modeling. the predictive maps produce valuable baselines for risk management in the study area, providing evidence to manage future human interaction with hazards. Human interactions with natural extreme events, or hazards, are increasing globally 1. Natural disasters have affected people and natural environments generating vast economic losses around the world. However, in some developed counties disasters have been decreasing since 1900 2,3. Hazard is the probability of occurrence in a specified period and within a given area of a potentially damaging of a given magnitude 4,5. The definition incorporates the concepts of location (where?), time (when, or how frequently?) and magnitude (how large?). Total risk (R) means the expected number of lives lost, person injured, damage to property, or disruption of economic activity due to a particular natural phenomenon, and is therefore the product of specific risk (RS) and elements at risk (E) 6. In addition, RS is the expected degree of loss due to a natural phenomenon. Landscapes around the world are reflections of diverse natural processes. The probabilities of extreme natural events are typically greater in more natural areas and are, in fact, extensions of natural systems. Exposure of people to these extreme natural processes could be reduced and limited if predictive models based on new approaches and deeper knowledge of effective factors were employed 7. Mountainous areas are commonly sites of snow avalanches 8,9 , landslides 4,10 , floods 11,12 , mudflows 13 , ice avalanches 14 , soil erosion 15-17 , rock falls 18 , and wildfires 19-24. Most studies focus on a single hazard, even when there are multiple hazardous processes affecting the same landscapes 8,25-30. However, hazards sometimes interact with each other. Sometimes, the mitigation of one

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Catastrophic floods cause deaths, injuries, and property damages in communities around the world. The losses can be worse among those who are more vulnerable to exposure and this can be enhanced by communities’ vulnerabilities. People in undeveloped and developing countries, like Iran, are more vulnerable and may be more exposed to flood hazards. In this study we investigate the vulnerabilities of 1622 schools to flood hazard in Chaharmahal and Bakhtiari Province, Iran. We used four machine learning models to produce flood susceptibility maps. The analytic hierarchy process method was enhanced with distance from schools to create a school-focused flood-risk map. The results indicate that 492 rural schools and 147 urban schools are in very high-risk locations. Furthermore, 54% of rural students and 8% of urban students study schools in locations of very high flood risk. The situation should be examined very closely and mitigating actions are urgently needed.

Early Cretaceous parts of the western Median Batholith (Western Fiordland Orthogneiss) represent the exposed root of a magmatic arc of dioritic to monzodioritic composition (SiO 2 051Á55 wt%; Na 2 O/K 2 O 03.7Á8.8 in this study). We characterise for the first time the field relationships, petrography, mineralogy and geochemistry of ultramafic and mafic cumulates at Hawes Head, the largest exposure of ultramafic rocks in western Fiordland. We distinguish three related rock types at Hawes Head: hornblende peridotite (MgO 021Á35 wt%); hornblendite (MgO 015Á16 wt%); and pyroxenite (MgO 021 wt%). Petrogenetic relationships between the ultramafic rocks and the surrounding Misty Pluton of the Western Fiordland Orthogneiss are demonstrated by: (i) mutually cross-cutting relationships; (ii) similar mafic phases (e.g. pyroxene and amphibole) with elevated Mg-numbers (e.g. olivine Mg/(Mg'Fe) 00.77Á0.82); (iii) fractionation trends in mineral geochemistry; and (iv) shared depleted heavy rare earth element patterns. In addition, the application of solid/liquid partition coefficients indicates that olivine in the ultramafic rocks at Hawes Head crystallised from a magma with Mg/(Mg'Fe) 00.54Á0.57. The olivine grains therefore represent a plausible early crystallising phase of the adjacent Western Fiordland Orthogneiss (Mg/(Mg'Fe) 0 0.51Á0.55).

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