## 3 Results
### Main Outcomes of the Top 20 Highly Cited Articles from Each Thematic Area
#### 3.2.5 Disaster Risk Reduction  
Disaster risk reduction is the thematic area with the highest average number of citations per thematic area for the top 20 cited papers (Figure 7). The focus of these papers is on landslides [112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122] and on floods [123; 124; 125]. Landslides are often catastrophic natural hazards leading to loss of life, damage to properties, and economic disruption [113]. All the studies on landslides have in common the coupling of machine learning (or artificial neural network) algorithms with spatial data types. The data used in these studies can be categorized into five main categories: topographical (e.g., aspect, slope, elevation), hydrological (e.g., rainfall, river density), lithological (e.g., lithology, faults), land cover (e.g., NDVI), and anthropogenic (e.g., road density). Topographical and land cover data are often derived from satellite images. However, the source of these data was not provided in most studies. According to [PERSON] et al. [113], tree-based ensemble algorithms achieve excellent results compared to other machine learning algorithms for landslide susceptibility mapping. Specifically, the random forest algorithm offers robust performance for accurate landslide susceptibility mapping with only a small number of adjustments required before training the model. The studies of [PERSON] and [PERSON] [117], [PERSON] and [PERSON] [122], [PERSON] et al. [121],and [PERSON] and [PERSON] [119] reported similar robust performance for the random forest algorithm for landslide susceptibility assessment. [PERSON] et al. [126] applied the metaheuristic algorithm Harris hawks optimization (HHO) in order to improve the performance of the artificial neural network (ANN) for landslide susceptibility assessment in western Iran. The authors found that the HHO algorithm improved the performance of the ANN in both recognizing and predicting the landslide patterns. [PERSON] et al. [127] showed that the use of a hybrid approach of alternative tree decision and ensemble algorithms (rotation forest and multi-boost) will improve the predictions of landslide susceptibility due to decreasing noise and over-fitting problems. According to [PERSON] et al. [112], for the preparation of landslide susceptibility assessment there are nine steps that need to be considered, which include obtaining relevant landslide and thematic information, selecting the appropriate mapping unit and model, evaluating model fitting and predictive performance, estimating model uncertainty, ranking model quality, and designing the landslide protocol. Future studies should consider these steps and focus on the development of landslide early warning systems at different geographical scales. Floods are considered as a major natural disaster because of their devastating effects that lead to human casualties and socioeconomic losses [128]. [PERSON] et al. [125] tested four decision tree-based machine learning models (logistic model trees (LMTs), reduced error pruning trees (REPTs), naive Bayes trees (NBTs), and alternating decision trees (ADTs)) for flash flood susceptibility mapping at the Haraz watershed in the northern part of Iran. The physical factors needed for the models, such as ground slope, altitude, and curvature, were derived from the Aster digital elevation model (DEM) of 30m resolution and the land use and NDVI maps were extracted from Landsat 8 Operational Land Image (OLI). Their results showed that all models have good performances in flood susceptibility assessment, with the ADT model having the highest predictive capability. [PERSON] et al. [126] combined the adaptive neuro-fuzzy inference system (ANFIS) with two GIS-based ensemble artificial intelligence approaches, the imperialistic competitive algorithm (ICA) and the firefly algorithm (FA), for flood spatial modeling and mapping applications in the Haraz watershed (Iran).