Geotechnical engineering

Ecology

Hazard map

Elevation (ballistics)

Engineering

Natural hazard

Structural engineering

Geology

Meteorology

Hazard

Biology

Drainage basin

Geography

Drainage density

Hydrology (agriculture)

Landslide

Cartography

Physical geography

Environmental science

Geoscience Frontiers

Natural hazards are often studied in isolation. However, there is a great need to examine hazards holistically to better manage the complex of threats found in any region. Many regions of the world have complex hazard landscapes wherein risk from individual and/or multiple extreme events is omnipresent. Extensive parts of Iran experience a complex array of natural hazards – floods, earthquakes, landslides, forest fires, subsidence, and drought. The effectiveness of risk mitigation is in part a function of whether the complex of hazards can be collectively considered, visualized, and evaluated. This study develops and tests individual and collective multi-hazard risk maps for floods, landslides, and forest fires to visualize the spatial distribution of risk in Fars Province, southern Iran. To do this, two well-known machine-learning algorithms – SVM and MARS – are used to predict the distribution of these events. Past floods, landslides, and forest fires were surveyed and mapped. The locations of occurrence of these events (individually and collectively) were randomly separated into training (70%) and testing (30%) data sets. The conditioning factors (for floods, landslides, and forest fires) employed to model the risk distributions are aspect, elevation, drainage density, distance from faults, geology, LULC, profile curvature, annual mean rainfall, plan curvature, distance from man-made residential structures, distance from nearest river, distance from nearest road, slope gradient, soil types, mean annual temperature, and TWI. The outputs of the two models were assessed using receiver-operating-characteristic (ROC) curves, true-skill statistics (TSS), and the correlation and deviance values from each models for each hazard. The areas-under-the-curves (AUC) for the MARS model prediction were 76.0%, 91.2%, and 90.1% for floods, landslides, and forest fires, respectively. Similarly, the AUCs for the SVM model were 75.5%, 89.0%, and 91.5%. The TSS reveals that the MARS model was better able to predict landslide risk, but was less able to predict flood-risk patterns and forest-fire risk. Finally, the combination of flood, forest fire, and landslide risk maps yielded a multi-hazard susceptibility map for the province. The better predictive model indicated that 52.3% of the province was at-risk for at least one of these hazards. This multi-hazard map may yield valuable insight for land-use planning, sustainable development of infrastructure, and also integrated watershed management in Fars Province.

Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?