Geo-environmental Factors Research Articles

Geoenvironmental determinants influencing chronic kidney disease of unknown etiology in the dry zone of Sri Lanka: a study based on a cohort of at-risk communities.

Chronic kidney disease of unknown origin (CKDu) has emerged as a serious health risk for farming communities in remote semi-arid lowland regions of Sri Lanka, with geoenvironmental factors widely believed to be associated with the etiology. Although numerous case-control studies have been carried out to determine the causes of CKDu, none have been conducted in at-risk communities. The main objective of this study is to identify the potential geo-environmental risk factors associated with the at-risk community in the Wilgamuwa region, a CKDu endemic area in Sri Lanka. The study collected and analyzed 92 water sources currently or previously used in the study area for major cations, anions, and trace elements, mainly As, Cd, Pd, Mn, Zn, Ba and Sr. The major cations and anions in CKDu-susceptible groundwater varied Si4+ > Ca2+ > Na+ > Mg2+ > K+ and HCO3- > Cl- > SO42- > NO3- > F- > PO43- > Br-. The higher average values of Si4+, F- and total hardness (TH) were reported in groundwater consumed by communities at risk of CKDu, with average values (in mg/L) of Si4+-46, F--0.62, and TH-178. Considering the Water Quality Index (WQI) values, 73% of groundwater sources are classified as poor water quality, and the groundwater quality mainly depends on the fluoride distribution in the region. According to the Hazard Quotient (HQ), the risk of adverse health effects from exposure to F- and SiO2 increased in magnitude in the order of male < female < children. Health hazards due to As, Cd and Pb exposure fell between the 'no-risk' and 'low-risk' categories. As outlined in this study, continuous monitoring of vulnerable communities for environmental exposures to key groundwater constituents is important in the dry zone of Sri Lanka.

Community identification and carbon storage monitoring of Heritiera littoralis with UAV hyperspectral imaging

The Heritiera littoralis, an important rare semi-mangrove species found in coastal protective forests, plays a crucial role in carbon storage and cycling within mangrove wetland ecosystems. Despite its importance, previous studies have overlooked remote monitoring of its carbon storage. Taking the world’s oldest and largest natural community of H. littoralis in Shenzhen, China, as the study area, this research pioneers the use of UAV hyperspectral imaging to identify the H. littoralis community and estimate its above-ground, below-ground, and total carbon storage. The impacts of five geo-environmental factors (elevation, slope, aspect, vegetation communities, and inland distance from the coastline) on the spatial variability of carbon storage using SHapley Additive exPlanations (SHAP) and multiscale geographically weighted regression (MGWR) methods were also investigated. The results demonstrate that the first derivative bands within the red edge and near-infrared regions, the anthocyanin reflectance index 2 (ARI2) and newly-developed three-band VIs, were sensitive features for community identification and carbon storage estimation within the H. littoralis community. Among the four machine learning models (eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR)), the best identification accuracy for the H. littoralis community was achieved by XGBoost. Among the four machine learning models (XGBoost, RF, SVM and kernel ridge regression (KRR)), RF model achieved the best performance in estimating above-ground (R2 = 0.749, RMSE=1.723 kg/m2, EV (explained variance) = 0.704), below-ground (R2 = 0.636, RMSE=0.6 kg/m2, EV=0.606) and total carbon storage (R2 = 0.613, RMSE=2.592 kg/m2, EV=0.597). SHAP and MGWR analysis showed that elevation and inland distance from the coastline were key factors influencing the spatial variability of carbon storage. In conclusion, this study showcases significant advantages in community identification and carbon storage monitoring of rare mangrove communities, providing valuable insights for biodiversity conservation efforts and management within the H. littoralis ecosystem.

Spatial variation of gully erosion determinants across subtropical regions of China

Gully erosion, with an enormous threat to global economic development and ecological security, results from the comprehensive effect of soil inherent properties and external geo-environmental factors. Previous studies on gully causative factors were mainly conducted on a specific site, case, or region, and limited systematic investigation has been performed on their spatial variation. Based on gully inventory in the seven provinces of southern China, this study investigated the spatial variation of thresholds for fifteen potential causative factors by the frequency ratio model, and analyzed the contribution of these factors to gully erosion by the Boruta algorithm. The results showed that gully density generally increased from north to south, characterized by a clustered distribution within a specific range, but the threshold of each potential factor exhibited a slight difference. Particularly, gully formation was predominantly influenced by multi-year average rainfall erosivity, multi-year average rainfall, and population density. However, the relative importance of these factors to gully erosion showed significant spatial heterogeneity. The relative importance increased from north to south for the factors of rainfall erosivity (8.25 %-13.56 %), rainfall (8.62 %-12.01 %), and slope aspect (0.35 %-1.22 %), but decreased for the factors of slope gradient (2.93 %-6.24 %), temperature (5.23 %-8.89 %), normalized difference vegetation index (NDVI) (5.25 %-7.80 %), and fraction vegetation coverage (FVC) (4.11 %-7.23 %). Overall, climatic factors exhibited an increasing contribution to gully erosion from north to south, whereas the influence of topography and vegetation coverage decreased along the same gradient. These findings will facilitate a better gully erosion control in southern China.

Tackling global biogeography and drivers of soil microbial dehalogenation traits and taxa: Insights from metagenomic profiling based on a curated dehalogenase database

To identify microbial resources for dehalogenation, develop effective remediation strategies, and reveal their significance in “One Health”, it is crucial to understand the occurrence, distribution, and drivers of soil dehalogenation functional traits and taxonomy groups at a broad scale, which is currently not well understood. To address the gaps, we characterized the biogeography of both dehalogenation traits and taxa assigned to six dehalogenation pathways, by metagenomic profiling global 4821 soils from eight habitats, based on a manually curated dehalogenase database (DhgaseDB). We found dehalogenation genes and microbes assigned to different pathways are everywhere, but varied consistently across habitats. The similarity of dehalogention traits and taxa composition declines with geographic distance, and that patterns are strongly correlated with geo-environmental factors. We identified anthropogenic organohalide pesticide inputs as the most influential factor on dehalogenation gene abundance, while soil properties, particularly pH, exert a larger impact on dehalogenation taxa diversity. Ultimately, we generated predictive maps of soil dehalogenation gene abundance and taxa diversity for the first time, highlighting the microbial dehalogenation hotpots in East Asia, Australia, Southern Africa, and coastal regions. Collectively, our study highlights the significant role of various microbial dehalogenation processes in organohalide biotransformation and environmental microecology, providing the necessary methodological basis for a deeper comprehension of the underlying mechanisms, thereby contributing to the advancement of tailored strategies for organohalide remediation.

Utilizing geodetectors to identify conditioning factors for gully erosion risk in the black soil region of northeast China

In the black soil region of Northeast China, the issue of gully erosion persists as a significant threat, resulting in extensive damage to farmland, severe degradation of the black soil, and decreased productivity. It is therefore of utmost importance to accurately identify areas that are susceptible to gully erosion to effectively prevent and control its negative impact. This study tried to utilize geographical detectors (geodetectors) as a means to identify the factors that contribute to the distribution of gullies and assess the risk of gully erosion (GER) in five catchments within the region, with areas ranging from approximately 80 km2–200 km2. By employing the geodetectors method, fourteen geo-environmental factors were analyzed, including topographic attributes (such as aspect, catchment area, convergence index, elevation, plan curvature, profile curvature, slope length, slope, stream power index, and topographic wetness index), channel network distance, vegetation index (NDVI and EVI), as well as land use/land cover (LULC). The modeling of GER was conducted using the random forest algorithm (RFA). Out of the fourteen examined geo-environmental factors, only a subset, comprising less than or equal to 50%, demonstrated a significant (p < 0.05) influence on the spatial distribution of gullies. These selected factors were sufficient in assessing GER, with LULC (mean q-value = 0.270) and elevation (mean q-value = 0.113) identified as the two most important factors. Furthermore, the RFA exhibited satisfactory performance across all catchments, achieving AUC values ranging from 0.712 to 0.933 (mean = 0.863) in predicting GER. Overall, the catchment areas were classified into high, moderate, low, and very low-risk levels, representing 9.67%–15.95%, 19.28%–26.08%, 24.59%–30.55%, and 30.54%–39.08% of the total area, respectively. Importantly, a significant positive linear relationship (r2 = 0.722, p < 0.05) was observed between the proportion of cropland area and the occurrence of high-level GER. Although the primary risk levels were categorized as low and very low, the proportion of high-risk levels exceeded the existing gully coverage (0.34%–3.69%). These findings highlight the substantial potential for gully erosion and underscore the necessity for intensified efforts in the prevention and control of gully erosion within the black soil region of Northeast China.

A Unique Conditions Model for Landslide Susceptibility Mapping

Several methods and approaches have been proposed to assess landslide susceptibility. The likelihood of landslides occurring can be determined by applying statistical models to historical landslides, taking into account controlling factors. Popular methods for predicting the probability of landslides are weights-of-evidence and logistic regression. We discuss the assumptions and interpretations of these methods, the relationships between them, and their strengths and weaknesses in case of categorical factors. Of particular interest is the conditional independence of the controlling factors and its effect on model bias. To avoid lack of conditional independence of factors and model bias, we present a unique conditions model that is always unbiased. To illustrate the theoretical developments, a practical application is given using observed landslides and geo-environmental factors from a previous study. The unique conditions model appears superior to the other models.

Integrated frequency ratio-analytical hierarchy and geospatial techniques-based earthquake risk assessment in mountainous cities: a case from the Northwestern Himalayas

ABSTRACT Earthquake risk management (ERM) needs proper planning and mitigation measures to minimise adverse impacts. In this context, we quantify the earthquake risk based on hazard, exposure, and vulnerability in the Northwestern Himalayas, Pakistan. A complete earthquake catalogue along with topographic, geo-environmental, seismic, and social causative factors were integrated into a geospatial environment for earthquake risk profiling. The integrated frequency ratio-analytical hierarchy process (FR-AHP) techniques were employed to measure the causative factor weights and historical earthquake distribution within the causative factor classes for hazard assessment. Simultaneously, AHP was utilised to calculate the vulnerability. The exposure was obtained by integrating the hazard and the land use map to estimate damage and loss. Finally, the risk was estimated and mapped across the study area at high resolution. The developed AHP model for vulnerability and hazard showed high accuracy for training and validation data sets (i.e. 98% and 93%, respectively). While there is an evident geographic disparity in the estimated risk, the results show that ∼25% of areas fall under the “very high-risk” zones where population and building density are high near active fault zones. The current study offers actionable insights for risk-reduction initiatives in the mapped high-risk zones.

Leaf trait variations and correlations across four forests with similar mean annual precipitation in northern China

Leaf functional traits (LFTs) are highly plastic and essential to plant resource acquisition and utilization. However, the collinearity of environmental factors and differences in the response of distinct functional types generate challenges in clarifying the contributions of environment variables to LFTs. This study selected 28 species from four forests in northern China with similar mean annual precipitation (MAP) to explore how other dissimilar environmental factors influence LFTs after controlling for the most influential factor. Specifically, we examined the effect of geographical (latitude, longitude, and altitude) and non-precipitation environmental factors (mean annual temperature (MAT), aridity index (AI), and soil pH) on leaf dry matter content (LDMC), and leaf carbon (LCC), nitrogen (LNC), and phosphorus (LPC) concentration, and pH. The collinearity of environmental factors on LFTs can be attributed to the regulation of moisture and temperature by geographical factors. Therefore, the remaining geo-environmental factors still played a significant role in LFT variation after excluding the most influential factor, which assist LFTs to maintain the original pattern of change across the environmental gradient. As latitude, longitude, and AI decreased, MAT increased, LCC and LPC decreased, LNC first increased, then decreased, and LDMC and leaf pH did not change significantly. The trends and intensity of LFTs variation varied between functional types. Significant differences in correlation were observed in LPC-to-LDMC and LPC-to-LNC across all regions for each species. Trait-trait relationships were inconsistent between life forms (herbs and woody plants) for leaf pH-to-nutrient concentration (C, N, P), LCC-to-LPC, and LCC-to-LDMC. Environmental variables were larger contributors to LFTs (especially LNC, LPC, and LDMC) variation than plant functional types (PFTs), while LCC and leaf pH were largely dependent of PFTs. These patterns result from plant adaptations to multiple variables, and a single environmental factor (even the core element that generated the collinearity of environmental factors such as MAP) in northern China cannot dominant geographic patterns among LFTs. This study sheds light on plant adaptation strategies to environmental change, deepening our understanding of ecosystem function and functional coordination.

Novel Ensemble Models Based on the Split‐Point Sampling and Node Attribute Subsampling Classifier for Groundwater Potential Mapping

AbstractGroundwater potential maps are crucial tools for effectively managing water resources, particularly in agriculturally focused countries such as Vietnam. However, creating these maps is a challenging task that requires reliable data and methods. In this study, we integrated the Split‐Point Sampling and Node Attribute Subsampling Classifier (SPAARC) with the Bagging (B), MultiBoostAB (MBAB), and Random Subspace (RSS) ensemble learning techniques and developed three ensemble models: B‐SPAARC, MBAB‐SPAARC, and RSS‐SPAARC. We selected 13 geoenvironmental factors based on their availability, relevance, and association with groundwater potential in the Sesan River basin of Vietnam. We assessed the models' performance using various metrics such as area under the curve (AUC), accuracy, sensitivity, specificity, and RMSE. The findings indicated that the ensemble models performed better than the single SPAARC model in mapping groundwater potential. The MBAB‐SPAARC model demonstrated the highest accuracy with an AUC value of 0.891, followed by B‐SPAARC (AUC = 0.844), RSS‐SPAARC (AUC = 0.871), and the single SPAARC (AUC = 0.853) models. The results also highlighted that elevation, rainfall, land use/cover, and altitude were the most significant factors for mapping groundwater potential in the Sesan River basin. The innovative ensemble models and reliable potential maps developed in this study assist water resource managers in planning water usage based on the benefits and costs for various users and in devising sustainable strategies for using, protecting, and managing groundwater.

Spatial distribution of Japanese encephalitis cases and correlated geo-environmental factors in southern and northern counties of China

Japanese encephalitis (JE) is a mosquito-borne disease with a spatial distribution that is linked to geo-environmental factors. The spatial distribution of JE cases and correlated geo-environmental factors were investigated in two critical counties in southern and northern China. Based on maps, enhanced thematic mapper (ETM) remote sensing datasets from Landsat and spatial datasets of JE cases, spatial distribution and spatial cluster analyses of JE cases at the village scale were performed by using the standard deviational ellipse and Ripleys K-function. Global and regional spatial cluster analyses of JE cases were also performed by using Moran's index. Regression analysis was used to analyze the relationships between geo-environmental characteristics and the risk of JE cases. At the study sites, the JE cases were not spatially clustered at the village or district (global) level, whereas there was a spatial cluster at the district (local) level. Diversity-related features for JE patients at the district and village levels were detected at two sites. In the southern counties, the distance of a village from a road was related to the village-level JE risk (OR: 0.530, 95 CI: 0.297–0.947, P = 0.032), and the number of township-level JE cases was linked to the distance of the district center from the road (R =-0.467, P = 0.025) and road length (R = 0.516, P = 0.012) in the administrative area. In northern China, the modified normalized difference water index (MNDWI) in the 5 km buffer around the village was related to village-level JE risk (OR: 0.702, 95% CI: 0.524–0.940, P = 0.018), and the number of township-level JE cases was related to the MNDWI in the administrative region (R =-0.522, P = 0.038). This study elucidates the spatial distribution patterns of JE cases and risk, as well as correlated geo-environmental features, at various spatial scales. This study will significantly assist the JE control efforts of the local Centers for Disease Control and Prevention (CDC), which is the base-level CDC, particularly concerning the allocation of medicine and medical staff, the development of immunological plans, and the allocation of pesticides and other control measures for the mosquito vectors of JE.

Correlation between climatic environment and characteristic components of 14 kinds of huajiao by thermal analysis techniques, GC-MS and HS-IMS.

Huajiao (Zanthoxylum bungeanum Maxim.) is extensively cultivated in various countries, including China, Korea, and India, owing to its adaptability to diverse environments. This study comprehensively analyzed the volatile substance composition of 14 varieties of red huajiao with distinct geographical origins. Thermal analysis methods, gas chromatography-mass spectrometry (GC-MS), and headspace-ion mobility chromatography (HS-IMS) were employed to evaluate the total volatile substance composition and content. The study revealed minor variations in water content, volatile matter content, and fractions among the geographically sourced huajiao samples. Utilizing correlation analysis based on GC-MS and orthogonal partial least squares discriminant analysis (OPLS-DA) with HS-IMS, a robust classification method for the 14 types of huajiao was developed. Applying the variable importance in the projection (VIP) method, seven distinctive components were identified as potential markers for distinguishing the geographical origins of red huajiao. By integrating climatic and topographical factors of the 14 huajiao varieties, the correlation analysis of GC-MS, and OPLS-DA classification outcomes from HS-IMS elucidated the influence of geo-environmental factors on huajiao components and contents. This research provides insights into the impact of diverse geographic environments on the constituents and characteristics of huajiao. It offers valuable guidance for selecting optimal cultivation locations to enhance huajiao quality, aiding consumers in making informed choices.

Current State of Canine Heartworm in Portugal.

The favourable geo-climatic conditions in Portugal have made it highly conducive to the development of Dirofilaria immitis in dogs, leading to its identification as an endemic region. This nematode is rapidly spreading across Europe, particularly in northeastern countries. The objective of this study was to provide an updated assessment of the prevalence of this disease in Portuguese dogs, analysing the results in relation to epidemiological and geo-environmental factors, and to identify potential risk factors. A total of 1367 dogs from all continental and insular districts were included in the study and tested for D. immitis antigens. The overall prevalence was found to be 5.9%. It was observed that the disease is spreading northward, with previously unaffected districts now reporting cases, and that the prevalence in coastal districts exceeded that of inland ones. Notably, the Aveiro district exhibited a significant increase in D. immitis prevalence, while in certain districts such as Setúbal, Santarém, Madeira, or Faro, a stabilisation or decrease in prevalence was noted. Furthermore, outdoor and older dogs were found to be at a higher risk of infection. No positive cases were detected in the Azores. Most of the infected dogs were located in areas of high and medium risk of infection. This study underscores the importance of implementing pharmacological prophylaxis, vector control strategies, and public awareness programs to control the spread of this zoonotic disease.

Optimizing landslide susceptibility mapping using machine learning and geospatial techniques

Landslides present a substantial risk to human lives, the environment, and infrastructure. Consequently, it is crucial to highlight the regions prone to future landslides by examining the correlation between past landslides and various geo-environmental factors. This study aims to investigate the optimal data selection and machine learning model, or ensemble technique, for evaluating the vulnerability of areas to landslides and determining the most accurate approach. To attain our objectives, we considered two different scenarios for selecting landslide-free random points (a slope threshold and a buffer-based approach) and performed a comparative analysis of five machine learning models for landslide susceptibility mapping, namely: Support Vector Machine (SVM), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The study area for this research is an area in Polk County in Western North Carolina that has experienced fatal landslides, leading to casualties and significant damage to infrastructure, properties, and road networks. The model construction process involves the utilization of a dataset comprising 1215 historical landslide occurrences and 1215 non-landslide points. We integrated a total of fourteen geospatial data layers, consisting of topographic variables, soil data, geological data, and land cover attributes. We use various metrics to assess the models' performance, including accuracy, F1-score, Kappa score, and AUC-ROC. In addition, we used the seeded-cell area index (SCAI) to evaluate map consistency. The ensemble of the five models using Weighted Average produces outstanding results, with an AUC-ROC of 99.4% for the slope threshold scenario and 91.8% for the buffer-based scenario. Our findings emphasize the significant impact of non-landslide random sampling on model performance in landslide susceptibility mapping. Furthermore, by optimally identifying landslide-prone regions and hotspots that need urgent risk management and land use planning, our study demonstrates the effectiveness of machine learning models in analyzing landslide susceptibility and providing valuable insights for informed decision-making and disaster risk reduction initiatives.

Urban Erosion in The Amazon Region: Mitigation and Control Strategies in The Municipality of Buriticupu (MA)

Purpose: this work aims to analyze the factors that contributed to the intensification of erosion in the Amazon region of Buriticupu (MA) and discuss possible forms of mitigation and control. Methods: urban area of the municipality was characterized and data on soil, pedology, relief, slope, use and occupation, economic activities developed, as well as satellite images, were collected. This information was used to analyze indicators of natural erosion processes, as well as the influence of human activities. Possible mitigation and control solutions were discussed. Results and discussion: it was found that several aspects contribute to the development of erosion phenomena in the region, such as soil type, slope and hydrology. In the case of the municipality under study, geoenvironmental factors favor the development of gullies. However, the main force has been the action of man in modifying the landscape and weakening the soil. It is understood that it is necessary to implement articulated biogeoengineering strategies, such as controlling the slope and stability of slopes, the water flow regime, plant restoration and environmental education strategies. With this, we can be successful in mitigating the phenomenon and preserving the landscape. Implications of the research: it is urgent to develop shared management measures between federated entities, organized civil society and the local population to map and regulate sustainable development, especially in Amazonian regions. Originality/value: in some Amazonian regions, as in the case of the municipality of Buriticupu (MA), the advancement of agricultural activities and urban development, associated with climate change, have significantly influenced the emergence and advancement of gullies, which can promote the disappearance of the built environment. These gullies are treated as more advanced soil erosion phenomena, serious and difficult to control and remediate. Although erosion is a problem that affects many regions in Brazil, there are still few studies that aim to investigate the causes of its appearance and the discussion about possible solutions applied to gullies in the Amazon region is unprecedented. Agricultural development has accelerated soil degradation and is an urgent problem to be debated.

Climate change and geo-environmental factors influencing desertification: a critical review.

The problem of desertification (DSF) is one of the most severe environmental disasters which influence the overall condition of the environment. In Rio de Janeiro Earth Summit on Environment and Development (1922), DSF is defined as arid, semi-arid, and dry sub-humid induced LD and that is adopted at the UNEP's Nairobi ad hoc meeting in 1977. It has been seen that there is no variability in the trend of long-term rainfall, but the change has been found in the variability of temperature (avg. temp. 0-5°C). There is no proof that the air pollution brought on by CO2 and other warming gases is the cause of this rise, which seems to be partially caused by urbanization. The two types of driving factors in DSF-CC (climate change) along with anthropogenic influences-must be compared in order to work and take action to stop DSF from spreading. The proportional contributions of human activity and CC to DSF have been extensively evaluated in this work from "qualitative, semi-quantitative, and quantitative" perspectives. In this study, we have tried to connect the drives of desertification to desertification-induced migration due to loss of biodiversity and agriculture failure. The authors discovered that several of the issues from the earlier studies persisted. The policy-makers should follow the proper SLM (soil and land management) through using the land. The afforestation with social forestry and consciousness among the people can reduce the spreading of the desertification (Badapalli et al. 2023). The green wall is also playing an important role to reduce the desertification. For instance, it was clear that assessments were subjective; they could not be readily replicated, and they always relied on administrative areas rather than being taken and displayed in a continuous space. This research is trying to fulfill the mentioned research gap with the help of the existing literatures related to this field.

An integrated statistical-geospatial approach for the delineation of flood-vulnerable sub-basins and identification of suitable areas for flood shelters in a tropical river basin, Kerala

Flood vulnerability assessment is crucial for developing effective flood management and mitigation strategies. The present study aims to understand the flood vulnerability of the Kallada River Basin (KRB) and identify suitable locations for safe flood sheltering facilities in the basin. As part of this, morphometric analysis of KRB was carried out by dividing the basin into fifty-eight 4th-order sub-basins to understand the sub-basin-wise terrain characteristics and the degree of vulnerability to flooding. GIS tools were used to assess various morphometrical parameters, such as drainage frequency, texture ratio, ruggedness number, basin relief, bifurcation ratio, length of overland flow, drainage density, circularity ratio and area, and geo-environmental factors such as sand percent, rainfall, and mean slope of these basins. The morphometric parameters exhibited distinct spatial trends, with higher values primarily concentrated in the east and northeast parts for certain parameters and in the western parts for others. Using hierarchical cluster analysis, the sub-basins were categorized into six clusters, revealing that 51% were vulnerable to floods, 26% moderately vulnerable, and 22% not vulnerable. Sub-basins in the central and western KRB were found to be highly vulnerable to flooding, while those in the eastern parts showed moderate vulnerability or were not vulnerable. Flood vulnerability mapping was validated using flood data of 2018 and 2019. Additionally, the weighted overlay method identified suitable areas for flood shelters in moderately vulnerable and vulnerable sub-basins and the areas were categorized into highly suitable, suitable, moderately suitable, and not suitable areas. Most areas of SB53 and SB55 were found highly suitable, emphasizing their potential for flood shelter locations. The findings of this study can be used by competent authorities to initiate flood mitigation and to develop targeted flood preparedness measures in similar river basins, particularly in the context of increasing flood events.

Contribution of predictive factors of land degradation occurrence applying maximum entropy model

Land degradation (LD) is a significant environmental issue that needs to be spatially mapped in different areas. This research provides a framework to prioritize the geo-environmental and anthropogenic factors on LD in a semi-arid region, Qazvin Plain, in Iran based on the maximum entropy model for the first time and also to produce the LD hazard map. The LD inventory map that shows the occurrence points of LD was prepared based on the decreasing trend of net primary productivity (NPP) over 20 years. 120 random points were selected as degraded sites where NPP showed a decreasing trend. Several environmental and anthropogenic factors including temperature, rainfall, land use, normalized difference vegetation index (NDVI), vegetation soil salinity index (VSSI), visible and shortwave infrared drought index (VSDI), electrical conductivity (EC) and sodium adsorption ratio (SAR) of groundwater, groundwater table, annual depletion of groundwater resources, slope, aspect, and altitude were considered as predictive factors of LD and their relative contributions were determined using the maximum entropy model. According to the findings, 49.74% of the region was in the class of low degradation. Also, 22.57%, 17.87%, and 9.82% of the plain were related to the moderate, high, and very high degradation classes, respectively. The model specified altitude as the most significant variable influencing LD followed by land use, slope, and VSSI. The least important variable affecting LD in the study area was the aspect. The results of modeling LD also showed that the areas most sensitive to LD were frequently rangelands which are mostly found on steep slopes and highlands. Based on the UAC value, the model illustrated a very good efficiency with a value of 0.8. According to the results, the maximum entropy model successfully mapped the LD hazard in the Qazvin plain.

MODELAGEM PREDITIVA APLICADA AO MAPEAMENTO DE RISCO POTENCIAL DE EROSÃO DE SOLOS NA AMAZÔNIA OCIDENTAL

The modeling of areas susceptible to soil loss due to hydro-erosive processes consists of methods that simplify reality to predict future behavior based on the observation and interaction of a set of geoenvironmental factors. Thus, the objective of the current analysis is to predict susceptibility to soil loss and map areas with the potential risk of erosion using the principles of Binary Logistic Regression (BLR) and Artificial Neural Networks (ANN). The hydrographic sub-basin of the Sete Voltas River (330 km2), Rondônia, Brazil, was defined as the experimental area. Models were obtained using 100 sample units and 14 predictor parameters. Susceptibility was mapped based on five reference classes: very low, low, moderate, high, and very high. ANN obtained an area under the curve (AUC) of 0.808 and global precision of 79.2%, and the BLR model showed an AUC of 0.888 and global precision of 77%. Potentially susceptible areas represent 57.71% and 54.80% of the area for BLR and ANN models, respectively. The greatest potential risks are verified in places with no vegetation cover associated with agricultural practices. The technique proved to be effective, with adequate precision and the advantage of being less time-consuming and expensive than other methods. Keywords: Binary Logistic Regression; Artificial Neural Network; Erosion Susceptibility.

Risk assessment of flood disasters in the Poyang lake area

Floods are one type of common and catastrophic disaster and cause extensive destruction areas, enormous casualties, and huge economic and environmental losses worldwide. Flood risk assessment is crucial to disaster mitigation and prevention, social stability and prosperity, and economic and environmental sustainability. This work establishes comprehensive risk indices of geoenvironmental, disaster-triggering, disaster-vulnerable, and disaster-preventive factors to improve the rationality of risk assessment. Moreover, a novel combined method of context-perception long short-term memory (LSTM), analytic hierarchy process, and entropy weight is suggested to perform relative accurate risk assessment in the Poyang lake region, one of the worst-hit areas in China. The new context-perception LSTM algorithm can adequately learn comprehensive and significant context characteristics and outperforms the context-based convolutional neural network and conventional LSTM algorithms. The test precision reaches 95.7%, 99.1%, 0.958, and 0.981 in Accuracy, TPR, F1-score, and AUC, respectively. Some significant insights into flood risk are suggested as follows. (1) High flood risk mainly occurs in the densely populated regions with gentle topography close to main streams. The concentrated population and intensive town construction have destroyed surface permeability and soil consolidation and caused high flood risk. (2) In the high-risk urban regions, the operational condition of old soil dikes should be continuously monitored, and the construction of high rock dikes are necessary to effective flood prevention in the future. In the densely-populated and high-risk urban regions, more emergency rescue infrastructures, for example hospitals and emergency shelters, are required to arrange or build. Rational planning of emergency shelters can not only protect people against floods but also reduce government investment. (3) Significant hydrological infrastructures require high-frequency or even near real-time monitoring to timely discover engineering diseases and to avoid huge losses caused by unstable prevention infrastructures during flash floods.

Mapping of soil erosion susceptibility using advanced machine learning models at Nghe An, Vietnam

Abstract Soil Erosion Susceptibility Mapping (SESM) is one of the practical approaches for managing and mitigating soil erosion. This study applied four Machine Learning (ML) models, namely the Multilayer Perceptron (MLP) classifier, AdaBoost, Ridge classifier, and Gradient Boosting classifier to perform SESM in a region of Nghe An province, Vietnam. The development of these models incorporated seven factors influencing soil erosion: slope degree, slope aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), rainfall, and soil type. These factors were determined based on 685 identified soil erosion locations. According to SHapley Additive exPlanations (SHAP) analysis, soil type emerged as the most significant factor influencing soil erosion. Among all the developed models, the Gradient Boosting classifier demonstrated the highest prediction power, followed by the MLP classifier, Ridge classifier, and AdaBoost, respectively. Therefore, the Gradient Boosting classifier is recommended for accurate SESM in other regions too, taking into account the local geo-environmental factors.