Geo-environmental Factors Research Articles

Geoinformation-based landslide susceptibility mapping in subtropical area

Mapping susceptibility of landslide disaster is essential in subtropical area, where abundant rainfall may trigger landslide and mudflow, causing damages to human society. The purpose of this paper is to propose an integrated methodology to achieve such a mapping work with improved prediction results using hybrid modeling taking Chongren, Jiangxi as an example. The methodology is composed of the optimal discretization of the continuous geo-environmental factors based on entropy, weight of evidence (WoE) calculation and application of the known machine learning (ML) models, e.g., Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR). The results show the effectiveness of the proposed hybrid modeling for landslide hazard mapping in which the prediction accuracy vs the validation set reach 82.35–91.02% with an AUC [area under the receiver operating characteristic (ROC) curve] of 0.912–0.970. The RF algorithm performs best among the observed three ML algorithms and WoE-based RF modeling will be recommended for the similar landslide risk prediction elsewhere. We believe that our research can provide an operational reference for predicting the landslide hazard in the subtropical area and serve for disaster reduction and prevention action of the local governments.

Geo-environmental Factors in Three Persian Folk Stories: A Preliminary Study

Stories mirror the essential function of human cognitive activity. In the present preliminary study, we hypothesized that the mental spaces that make up the stories can be influenced by the environmental conditions of their creators. For this purpose, three stories from three different climatic zones in Iran were selected and a content analysis method was used to analyze their components. Results showed significant differences between the mental spaces of different geographic regions in these three stories. This finding could be considered as a step taken to show the extent to which an individual’s cognition and cognitive processing in general and the creation of meaning in particular, is an extension and representation of embodied experience. We have discussed the subject in the framework of narrative analysis as well as cognitive semantics.

Geospatial modelling of landslide susceptibility in Cross River State of Nigeria

Landslides have continued to wreck its havoc in many parts of the globe; comprehensive studies of landslide susceptibilities of many of these areas are either lacking or inadequate. Hence, this study was aimed at modelling landslide susceptibility in Cross River State of Nigeria, using geospatial approach. Frequency ratio (FR) model was adopted in this study. In adopting this approach, a landslide inventory map was developed using 72 landslide locations identified during fieldwork combined with other relevant data sources. Using appropriate geostatistical analyst tools within a geographical information environment, the landslide locations were randomly divided into two parts in the ratio of 7:3 for the training and validation processes respectively. A total of 12 landslide causing factors, such as; elevation, slope, aspect, profile curvature, plan curvature, topographic position index, topographic wetness index, stream power index, land use/land cover, geology, distance to waterbody and distance to major roads, were selected and used in the spatial relationship analysis of the factors influencing landslide occurrences in the study area. FR model was then developed using the training sample of the landslide to investigate landslide susceptibility in Cross River State which was subsequently validated. It was found out that the distribution of landslides in Cross River State of Nigeria was largely controlled by a combined effect of geo-environmental factors such as elevation of 250 – 500 m, slope gradient of >35°, slopes facing the southwest direction, decreasing degree of both positive and negative curvatures, increasing values of topographic position index, fragile sands, sparse vegetation, especially in settlement and bare surfaces areas, distance to waterbody and major road of < 500 m. About 46% of the mapped area was found to be at landslide susceptibility risk zones, ranging from moderate – very high levels. The susceptibility model was validated with 90.90% accuracy. This study has shown a comprehensive investigation of landslide susceptibility in Cross River State which will be useful in land use planning and mitigation measures against landslide induced vulnerability in the study area including extrapolation of the findings to proffer solutions to other areas with similar environmental conditions.

Consideration of geo-environmental factors in formulation of territorial sustainability strategy

The paper examines the role of geo-environmental impacts in developing a territorial sustainability strategy. It reviews the literature concerned with the definition of geo-environmental impacts and a globally accepted definition of “sustainable development”. The paper deals with the keys to local sustainable development and principles necessary for developing territorial sustainability strategy. It studies the challenges that hinder sustainable development of Russian regions in modern geopolitical and environmental conditions; preconditions for stable geo-environmental bonds in order to minimize territorial imbalances. The objectives and aims of territorial development are set. The levels of territorial sustainability for development are proposed. The paper addresses a geo-environmental approach to territorial development strategy in the context of accelerating global interaction, sensitive to a great number of impacts. The authors are concerned with different interpretations of the concept and the relationship between geo-environmental and geo-economic research by various domestic scientists. They identify prerequisites for sustainable geo-environmental bonds in order to minimize territorial imbalances. The priorities of geo-environmental territorial sustainability strategy imply various levels, hierarchies in the target socio-environmental-economic system, each of which has corresponding objectives set and implementation tools developed. The strategy for territorial development based on geo-environmental impacts is proposed, containing the priorities, aims and objectives.

Bone histology of human remains from the Late Holocene of Northwestern Patagonia, Argentina: a multidimensional taphonomic approach

This study presents the first histological results on human remains recovered from Late Holocene archaeological sites from Northwestern Patagonia, Argentina, from a multidimensional taphonomic perspective. The burial sites come from different geoenvironmental contexts. The main purpose of this work is to assess the influence of geoenvironmental factors on taphonomic history of human bones. The bone microstructure of eleven human femora was analyzed through thin sections and four of them also with scanning electron microscopy. The relationship of these parameters with a set of geoenvironmental variables was assessed by means of different uni-, bi-, and multivariate statistical analysis. Results indicate that the geoenvironmental contexts led to different preservation of the microstructure of skeletal remains, represented by two different patterns. Bones with the best histological preservation were associated with semi-arid to arid geoenvironmental characteristics and the worst preserved ones with subhumid to humid features. Interestingly, samples from the better-preserved pattern showed staining and microcracks, whereas bioerosion is associated with the poorly preserved ones. A weak incidence of sex and chronology was found on microstructure preservation. Finally, the effects of weathering on naturally unearthed bones were deeply destructive in a short period of time. This work highlights the power of a multidisciplinary approach to discern the taphonomical pathways in archaeological sites.

Evaluation of Land Use Suitability for Wheat Cultivation Considering Geo-Environmental Factors by Data Dependent Approaches

Two techniques were investigated to be standard deviation based weighting Multi Criteria Assessment (MCA), and Artificial Neural Network (ANN) considering base environmental factors to define wheat cultivation suitability in Van region. Climate data (long term annual, maximum and minimum temperature, total mean precipitation and solar radiation), physical factors such as elevation, hillshade, slope, soil depth, accessibility to the fields and land use cover were used to produce wheat suitability map. All inputs were weighted with reference to existing wheat areas. MCA and ANN approaches were applied using same dataset to compare the performance of the two techniques. In total, 228 wheat parcels were used as training (171 parcels) and testing (57 parcels) data. Relative Operational Characteristic (ROC) was applied for accuracy assessment. ROC values of the MCA technique which was depended on existing wheat lands, and ANN techniques were derived to be 0.875 and 0.71 respectively. Results showed that 15% of the research area was very suitable for wheat farm, and today, only 67% of very suitable areas were used to be agriculture. Other areas were currently used as grassland (28%), bare ground (4%), and other (1%).

Heterogeneity of the population of Lonicera caerulea L. in the tectonically active zone (Altai Mountains, Kurai ridge)

Depending on specific combinations of geoenvironmental factors, the micropopulations of the bluehoneysuckle in the valley of the Kuraika River (Gorny Altai) produced fruits of increased or decreased size and seednumber. The observed 3-fold higher accumulation of biologically active phenols in fruits, accompanied by the respectivedecreased accumulation in leaves, and narrower ratios of certain polyphenol classes in leaves and fruits are considered tobe the plant response to the stressing impact of environmental factors, resulting from the active tectonic processes in thestudied area of the Russian Altai.

Effects of urbanisation in a shallow coastal aquifer: An integrated GIS-based case study in Cochin, India

Urbanisation impacts the availability and quality of renewable natural resources, particularly water resources. In coastal regions, the magnitude of urbanisation and its consequences is more prevalent. However, there are limited quantitative assessments on how urbanisation affects the groundwater system, especially the groundwater potential. Therefore, in the present study, we analysed the variability in groundwater potential zones due to the temporal changes in land use land cover (LULC) for Cochin, Kerala, India. For this, a GIS-based weighted overlay analysis method was performed with six geo-environmental factors to delineate groundwater potential zones (GWPZ). GWPZ at three different decadal time points from 1992 to 2019 was derived using the representative LULC maps. Our analysis showed a significant reduction in the high potential zone, decreasing from 75.34 % to 17.53 %. Contrastingly, the low potential zone rose from 0 to 11.8 % from 1992 to 2019. The temporal change in the spatial pattern in GWPZ named Groundwater Potential Change Index (GWPCI) was derived through pixel-level change detection. We performed a long-term (1985–2017) trend analysis on groundwater levels obtained for a network of twenty-three observation wells from the study area to validate the results. The trend analysis showed that 47 % of the wells display a statistically significant declining water level trend. Subsequently, twelve wells were selected to perform a bivariate statistical analysis between the long-term trend in water level and GWPCI. Ten wells showed a reasonable agreement to the negative change in GWPCI, while two displayed no relation to GWPCI. The study illustrates the effect of LULC change patterns in the groundwater system since significant alterations were observed on GWPZ. The systematic approach adopted in the present study can contribute to sustainable land and water resource management aimed at reducing the urbanisation impact on the groundwater system in the future.

How to environmental, demographic, and geographical measure the environmental factors influence the spread of Covid-19

Abstract. Italy was the first European country to experience a rapid increase in confirmed cases and deaths of the novel Coronavirus disease (COVID-19). This chapter explains how COVID-19 transmitted so rapidly in Northern Italy, analysing the underlying relationships between infected people and environmental, demographic, and geographical factors that influenced its spread. This study analyses data on COVID-19 cases alongside environmental data. This study finds out that cities with little wind, high humidity and frequently high levels of air pollution — exceeding safe levels of ozone or particulate matter — had higher numbers of COVID-19 related infected individuals and deaths. Overall, then, results here suggest that geo-environmental factors may have accelerated the spread of COVID-19 in Northern Italian cities, leading to a higher number of infected individuals and deaths. Keywords. Air pollution, Environment and health, Natural hazards, Risk assessment, Urban environment, Sustainable development and policy assessment, Sustainable Growth. JEL. Q12, Q13, Q15, Q18 .

Risk Factors for Cerebral Aneurysm Rupture in Mongolia

PurposeIdentification of country-specific demographic, medical, lifestyle, and geoenvironmental risk factors for cerebral aneurysm rupture in the developing Asian country of Mongolia. First-time estimation of the crude national incidence of aneurysmal subarachnoid hemorrhage (aSAH).MethodsA retrospective analysis of all intracranial digital subtraction angiographies (DSA) acquired in Mongolia during the 2‑year period 2016–2017 (1714 examinations) was performed. During this period, DSA was used as primary diagnostic imaging modality for acute severe neurological symptoms in the sole hospital nationwide dedicated to neurological patients. The catchment area of the hospital included the whole country. Patients with incidental and ruptured aneurysms were reviewed with respect to their medical history and living conditions. The data was used to install a Mongolian aneurysm registry.ResultsThe estimated annual crude incidence of cerebral aneurysm rupture was 6.71 for the country of Mongolia and 14.53 per 100,000 persons for the capital region of Ulaanbaatar. Risk factors common in developed countries also applied for the Mongolian population: A medical history of hypertension, smoking or the presence of multiple aneurysms led to a higher relative risk of rupture. In contrast, female gender was not associated with a higher risk in this national cohort. Males pursuing a traditional nomadic living may exhibit a specifically high risk of rupture.ConclusionDisease management of over 200 individuals/year with aSAH constitutes a socioeconomic burden in Mongolia. Efforts to raise awareness of the risk factors hypertension and smoking among the Mongolian population are desirable. Measures to improve the nationwide availability of modern neurovascular treatment options are currently under consideration.

Identification of Groundwater Potential Zones Using GIS and Multi-Criteria Decision-Making Techniques: A Case Study Upper Coruh River Basin (NE Turkey)

In this study, geographic information system (GIS)-based, analytic hierarchy process (AHP) techniques were used to identify groundwater potential zones to provide insight to decisionmakers and local authorities for present and future planning. Ten different geo-environmental factors, such as slope, topographic wetness index, geomorphology, drainage density, lithology, lineament density, rainfall, soil type, soil thickness, and land-use classes were selected as the decision criteria, and related GIS tools were used for creating, analysing and standardising the layers. The final groundwater potential zones map was delineated, using the weighted linear combination (WLC) aggregation method. The map was spatially classified into very high potential, high potential, moderate potential, low potential, and very low potential. The results showed that 21.5% of the basin area is characterised by high to very high groundwater potential. In comparison, the very low to low groundwater potential occupies 57.15%, and the moderate groundwater potential covers 21.4% of the basin area. Finally, the GWPZs map was investigated to validate the model, using discharges and depth to groundwater data related to 22 wells scattered over the basin. The validation results showed that GWPZs classes strongly overlap with the well discharges and groundwater depth located in the given area.

Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China.

Landslides are one of the major geohazards threatening human society. The objective of this study was to conduct a landslide hazard susceptibility assessment for Ruijin, Jiangxi, China, and to provide technical support to the local government for implementing disaster reduction and prevention measures. Machine learning approaches, e.g., random forests (RFs) and support vector machines (SVMs) were employed and multiple geo-environmental factors such as land cover, NDVI, landform, rainfall, lithology, and proximity to faults, roads, and rivers, etc., were utilized to achieve our purposes. For categorical factors, three processing approaches were proposed: simple numerical labeling (SNL), weight assignment (WA)-based and frequency ratio (FR)-based. Then 19 geo-environmental factors were respectively converted into raster to constitute three 19-band datasets, i.e., DS1, DS2, and DS3 from three different processes. Then, 155 observed landslides that occurred in the past decades were vectorized, among which 70% were randomly selected to compose a training set (TS1) and the remaining 30% to form a validation set (VS1). A number of non-landslide (no-risk) samples distributed in the whole study area were identified in low slope (<1–3°) zones such as urban areas and croplands, and also added to the TS1 and VS1 in the same ratio. For comparison, we used the FR approach to identify the no-risk samples in both flat and non-flat areas, and merged them into the field-observed landslides to constitute another pair of training and validation sets (TS2 and VS2) using the same ratio of 7:3. The RF algorithm was applied to model the probability of the landslide occurrence using DS1, DS2, and DS3 as predictive variables and TS1 and TS2 for training to obtain the SNL-based, WA-based, and FR-based RF models, respectively. Verified against VS1 and VS2, the three models have similar overall accuracy (OA) and Kappa coefficient (KC), which are 89.61%, 91.47%, and 94.54%, and 0.7926, 0.8299, and 0.8908, respectively. All of them are much better than the three models obtained by SVM algorithm with OA of 81.79%, 82.86%, and 83%, and KC of 0.6337, 0.655, and 0.660. New case verification with the recent 26 landslide events of 2017–2020 revealed that the landslide susceptibility map from WA-based RF modeling was able to properly identify the high and very high susceptibility zones where 23 new landslides had occurred, and performed better than the SNL-based and FR-based RF modeling, though the latter has a slightly higher OA and KC. Hence, we concluded that all three RF models achieve reasonable risk prediction, but WA-based and FR-based RF modeling deserves a recommendation for application elsewhere. The results of this study may serve as reference for the local authorities in prevention and early warning of landslide hazards.

Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM

The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model (DEM) data. The unique terrain characteristics of a particular landscape are derived from DEM, which are responsible for initiation and development of ephemeral gullies. As the topographic features of an area significantly influences on the erosive power of the water flow, it is an important task the extraction of terrain features from DEM to properly research gully erosion. Alongside, topography is highly correlated with other geo-environmental factors i.e. geology, climate, soil types, vegetation density and floristic composition, runoff generation, which ultimately influences on gully occurrences. Therefore, terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility (GES) mapping. In this study, remote sensing-Geographic information system (GIS) techniques coupled with machine learning (ML) methods has been used for GES mapping in the parts of Semnan province, Iran. Current research focuses on the comparison of predicted GES result by using three types of DEM i.e. Advanced Land Observation satellite (ALOS), ALOS World 3D-30 m (AW3D30) and Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) in different resolutions. For further progress of our research work, here we have used thirteen suitable geo-environmental gully erosion conditioning factors (GECFs) based on the multi-collinearity analysis. ML methods of conditional inference forests (Cforest), Cubist model and Elastic net model have been chosen for modelling GES accordingly. Variable’s importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods (Cforest = 21.4, Cubist = 19.65 and Elastic net = 17.08), followed by lithology and slope. Validation of the model’s result was performed through area under curve (AUC) and other statistical indices. The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs (AUC value of Cforest in ALOS DEM is 0.994, AW3D30 DEM is 0.989 and ASTER DEM is 0.982) used in this study, followed by elastic net and cubist model. The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.

Drought risk assessment: integrating meteorological, hydrological, agricultural and socio-economic factors using ensemble models and geospatial techniques

Among natural disasters, drought hits almost half of the world every year, regardless of the climatic zones. Identifying drought vulnerability regions is fundamental to plan and adopt mitigation measures. Here we apply a multi-criteria-based machine learning technique that integrates spatial data for preparing drought vulnerability map of different categories. We adopted remote sensing tools with three machine learning models namely support vector machine (SVM), random forest (RF) and support vector regression (SVR) and their ensembles (i.e. Bagging, Boosting and Stacking), as applied to the northwestern part of Iran as a case study. Various types of geo-environmental factors were considered including meteorological, hydrological, agricultural and socio-economic. The result of the model was evaluated through arithmetic logic values (area under the curve [AUC]) under the receiver operating curve (ROC). Through multi-collinearity test, the prominent causative factors for the occurrences of drought are defined. The AUC value from ROC of SVR-Stacking, RF-Stacking and SVM-Stacking model for training datasets are 0.942, 0.918 and 0.896, respectively. The SVR-Stacking yielded the best result (AUC = 0.94) confirming that SVR serves as a robust model for the preparation of drought susceptibility maps that can be used by governmental and other administrative agencies.

Assessment of Landslide Hazard in Jiangxi Using Geo-information Technology

Landslides constitute a severe environmental problem in Jiangxi, China. This research was aimed at conducting landslide hazard assessment to provide technical support for disaster reduction and prevention action in the province. Fourteen geo-environmental factors, e.g., slope, elevation, road, river, fault, lithology, rainfall, and land cover types, were selected for this study. A test was made in two cases: (1) only based on the main linear features, e.g., main rivers and roads, and (2) with detailed complete linear features including all levels of roads and rivers. After buffering of the linear features, an information value (IV) analysis was applied to quantify the distribution of the observed landslides for each subset of the 14 factors. The results were inputted into the binary logistic regression model (LRM) for landslide risk modeling, taking the known landslide points as a training set (70% of the total 9,525 points). The calculated probability of a landslide was further classified into five grades with an interval of 0.2 for hazard mapping: very high (3.70%), high (4.05%), moderate (18.72%), low (27.17%), and stable zones (46.36%). The accuracy was evaluated by AUC [the area under the receiver operating characteristic (ROC) curve] vs. the validation set (30%, the remaining landslides). The final results show that with increasing the completeness of the linear features, the modeling reliability also significantly increased. We hence concluded that the tested methodology is capable of achieving the landslide hazard prediction at regional scale, and the results may provide technical support for geohazard reduction and prevention in the studied province.

Geoenvironmental controls on the formation of human urinary calculi: a case study from Jordan

The purpose of this study is to determine the factors responsible for the prevalence of urinary stones disease among the inhabitants of selected regions in northern Jordan and to trace the extent of their link with the hydrogeochemical properties of groundwater in the area in addition to geoenvironmental factors and some certain dietary habits. By comparing the disease spread in the studied areas, results showed that the elevated areas (Irbid and Amman) are the most affected regions with the disease. One hundred and thirty water samples collected from 15 wells were distributed across the three studied regions to identify urinary stone patients among individuals. All samples were analyzed for percentages of cations and anions to assess water quality. Anions are distributed in the following order: HCO3− >Cl− >SO42− >NO3− >F− >PO43−, while the cations are distributed as follows: Ca2+ >Mg2+ >Na+ >K+ >Fe2+. The hydrogeochemical analysis results for the three regions compared using the Wilcoxon rank-sum test. The significant difference in the values of TDS, TH, Cl−, NO3−, Ca2+, and Mg2+ is p < 0.05 compared with water quality in the regions of Amman–Mafraq and Irbid–Mafraq. Depending on Gibbs’ diagram and the factor analysis, elements ratio plots strongly suggest that the source of chemical ions in the groundwater is the interaction of water with carbonate rocks. Therefore, this study confirms the strong relationship between urinary stones formation and the geochemistry of drinking water during the high calcium content and increase of the water hardness used by the residents of the study areas. The effect of temperature on the formation of urinary stones was observed through the direct correlation with the amount of water consumed by individuals. In winter, the daily water intake decreases, which leads to an increase in the concentration of Ca, Na, and P ions in the bladder. The high temperature in the summer increases the dehydration process in the body, which increases the concentration of calcium and other ions to create an opportunity for urine stones to form.

Landslide Geo-Hazard Risk Mapping Using Logistic Regression Modeling in Guixi, Jiangxi, China

Reliable prediction of landslide occurrence is important for hazard risk reduction and prevention. Taking Guixi in northeast Jiangxi as an example, this research aimed to conduct such a landslide risk assessment using a multiple logistic regression (MLR) algorithm. Field-investigated landslides and non-landslide sites were converted into polygons. We randomly generated 50,000 sampling points to intersect these polygons and the intersected points were divided into two parts, a training set (TS) and a validation set (VT) in a ratio of 7 to 3. Thirteen geo-environmental factors, including elevation, slope, and distance from roads were employed as hazard-causative factors, which were intersected by the TS to create the random point (RP)-based dataset. The next step was to compute the certainty factor (CF) of each factor to constitute a CF-based dataset. MLR was applied to the two datasets for landslide risk modeling. The probability of landslides was then calculated in each pixel, and risk maps were produced. The overall accuracy of these two models versus VS was 91.5% and 90.4% with a Kappa coefficient of 0.814 and 0.782, respectively. The RP-based MLR modeling achieved more reliable predictions and its risk map seems more plausible for providing technical support for implementing disaster prevention measures in Guixi.

Understanding the Relationship between Dominant Geo-Environmental Factors and Rural Poverty in Guizhou, China

Geo-environmental factors are believed to be major determinants of rural poverty. However, few studies have quantified the effects of these factors on rural poverty in China. In this paper, we used county-level poverty incidence data and geo-environmental factors to explore spatial patterns of the incidence of poverty using global and local spatial autocorrelation analysis and to investigate the effect of geo-environment factors on rural poverty using a geo-detector model. Our results demonstrated that there was spatial clustering of the incidence of poverty in the study area. The incidence of poverty decreased from south to north and from the east and west to the central area. The incidence of high–high poverty areas was mainly distributed in the southeast of Guizhou Province and the incidence of low–low poverty areas was distributed in the northeast. The results also demonstrated that percentage of effective irrigation on arable land, slope, elevation and vegetation cover were the dominant factors explaining the spatial pattern of poverty. Interaction analysis demonstrated that the slope non-linearly enhanced the percentage of effective irrigation on arable land. Our findings suggested that geo-environment is the fundamental control factor explaining the spatial pattern of rural poverty in China. Through analysis of the impact of the geo-environment on the spatial pattern of poverty, this study provides a reference for effectively implementing targeted alleviation of poverty.

Debris flows modeling using geo-environmental factors: developing hybridized deep-learning algorithms

Although the prediction of debris flow-prone areas represents a key step towards reducing damages, modeling debris flow susceptibility is complicated. In addition, the role of debris flow causal drivers in forested mountain landscapes is still poorly understood. To gain a holistic view of the causes of debris flows in the Umyeonsan, Seoul, South Korea region, we coupled the convolutional neural network (CNN) with two evolutionary optimization algorithms – grey wolf optimization (GWO) and cuckoo optimization algorithm (COA). Applying geoinformatics to debris flow factors, debris-flow susceptibility maps were generated and their validities were assessed with receiver operating characteristic (ROC) curves. The results reveal that three causative factors seem to contribute most to debris flows in the study area. The evolutionary optimization algorithms achieved higher goodness-of-fit and predictive power than the standalone CNN model. The goodness-of-fit and predictive skill measures of the CNN susceptibility map were 0.76 and 0.73. The values of CNN hybridized with GWO were 0.81 and 0.81 and hybridized with COA were 0.83 and 0.82. Slope degree, tree age, stream power index, geographical class, and soil drainage were the factors most affecting debris flow likelihood. The CNN-COA is the superior model and it predicted that 40.6% of the study area (i.e., 1844.96 km2) is highly and very highly susceptible to debris flows. The methodology can be applied for analysis of other region to improve risk management and guide development and land use planning.

Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm

Fire is among the most dangerous and devastating natural hazards in forest ecosystems around the world. The development of computational ensemble models for improving the predictive accuracy of forest fire susceptibilities could save time and cost in firefighting efforts. Here, we combined a locally weighted learning (LWL) algorithm with the Cascade Generalization (CG), Bagging, Decorate, and Dagging ensemble learning techniques for the prediction of forest fire susceptibility in the Pu Mat National Park, Nghe An Province, Vietnam. A geospatial database that contained records from 56 historical fires and nine explanatory variables was employed to train the standalone LWL model and its derived ensemble models. The models were validated for their goodness-of-fit and predictive capability using the area under the receiver operating characteristic curve (AUC) and several other statistical performance criteria. The CG-LWL and Bagging-LWL models with AUC = 0.993 showed the highest training performance, whereas the Dagging-LWL ensemble model with AUC = 0.983 performed better than Decorate-LWL (AUC = 0.976), CG-LWL and Bagging-LWL (AUC = 0.972), and LWL (AUC = 0.965) for predicting the spatial pattern of fire susceptibilities across the study area. Our study promotes the application of ensemble models in forest fire prediction and enhances the researchers' understanding of the processes of model building. Although these four ensemble models were originally developed for the estimation of forest fire susceptibility, the models are sufficiently general to be used for predicting other types of natural hazards, such as landslides, floods, and dust storms, by considering local geo-environmental factors.