Assessment of gully influencing factors and susceptibility using remote sensing-based frequency ratio method in Sunshui River Basin, Southwest China.

Gully erosion is a serious global environmental problem associated with land degradation and ecosystem security. Examining the influencing factors of gullies and determining susceptibility hold significance in environmental sustainability. The study evaluates the spatial distribution, influencing factors, and susceptibility of gullies in the Sunshui River Basin in Sichuan Province, Southwest China. The frequency ratio method supported by satellite images and the gully inventory dataset (1614 gully head points) with different influencing factors were applied to assess the distribution and susceptibility of gullies. Additionally, gully head points were grouped into a training set (70%, 1130 points) and a test set (30%, 484 points). Spatial distribution results indicated that most gullies are located in the middle and upper part of the basin, characterized by moderate elevation (2100-3300 m), steep slopes (11.63-27.34°), abandoned farmland, and Cambisols soil, and fewer gullies are located in lower part characterized by lower elevation, gentle slopes, and low vegetation coverage. Land use and land cover influence on susceptibility is significantly greater than other factors with a prediction rate of 33.9, especially farmland abandonment, while the occurrence of gullies is also more often on southwest-orientated slopes. Gully susceptibility highlighted that the study area affected by the very low, low, moderate, high, and very high susceptibilities to these gullies covered an area of about 16%, 23%, 32%, 26%, and 3% of the total basin respectively, which indicates 61% of the study area is susceptible to gully erosion. Moderate to high susceptibility is situated in the upper and middle part, consistent with the spatial distribution of gullies in the basin, and very high susceptibility (3%) is distributed in both the lower and upper parts of the basin. These results have important implications for soil loss control, land planning, and integrated watershed management in the mountainous areas of Southwest China.

Evaluation of urban flood susceptibility through integrated Bivariate statistics and Geospatial technology.

Flood disasters are frequent natural disasters that occur annually during the monsoon season and significantly impact urban areas. This area is characterized by impermeable concrete surfaces, which increase runoff and are particularly susceptible to flooding. Therefore, this study aims to adopt Bi-variate statistical methods such as frequency ratio (FR) and weight of evidence (WOE) to map flood susceptibility in an urbanized watershed. The study area encompasses an urbanized watershed surrounding the Chennai Metropolitan area in southern India. The essential parameters considered for flood susceptibility zonation include geomorphology, soil, land use/land cover (LU/LC), rainfall, drainage, slope, aspect, Topographic Wetness Index (TWI), and Normalized Difference Vegetation Index (NDVI). The flood susceptibility map was derived using 70% of randomly selected flood areas from the flood inventory database, and the other 30% was used for validation using the area under curve (AUC) method. The AUC method produced a frequency ratio of 0.806 and a weight of evidence value of 0.865 contributing to the zonation of the three classes. The study further investigates the impact of urbanization on flood susceptibility and is further classified into high, moderate, and low flood risk zones. With the abrupt change in climatic scenarios, there is an increase in the risk of flash floods. The results of this study can be used by policymakers and planners in developing a preparedness system to mitigate economic, human, and property losses due to floods in any urbanized watershed.

E-Z Isomerization in Guanidine: Second-order Saddle Dynamics, Non-statisticality, and Time-frequency Analysis.

Our recent work on the E-Z isomerization reaction of guanidine using ab initio chemical dynamics simulations [Rashmi et al., Regul. Chaotic Dyn. 2021, 26, 119] emphasized the role of second-order saddle (SOS) in the isomerization reaction; however, we could not unequivocally establish the non-statistical nature of the dynamics followed in the reaction. In the present study, we performed thousands of on-the-fly trajectories using forces computed at the MNDO level to investigate the influence of second-order saddle in the E-Z isomerization reaction of guanidine and the role of intramolecular vibrational energy redistribution (IVR) on the reaction dynamics. The simulations reveal that while majority of the trajectories follow the traditional transition state pathways, 15 % of the trajectories follow the SOS path. The dynamics was found to be highly non-statistical with the survival probabilities of the reactants showing large deviations from those obtained within the RRKM assumptions. In addition, a detailed analysis of the dynamics using time-dependent frequencies and the frequency ratio spaces reveal the existence of multiple resonance junctions that indicate the existence of regular dynamics and long-lived quasi-periodic trajectories in the phase space associated with non-RRKM behavior.

Landslide Susceptibility Evaluation of Machine Learning Based on Information Volume and Frequency Ratio: A Case Study of Weixin County, China.

A landslide is one of the most destructive natural disasters in the world. The accurate modeling and prediction of landslide hazards have been used as some of the vital tools for landslide disaster prevention and control. The purpose of this study was to explore the application of coupling models in landslide susceptibility evaluation. This paper used Weixin County as the research object. First, according to the landslide catalog database constructed, there were 345 landslides in the study area. Twelve environmental factors were selected, including terrain (elevation, slope, slope direction, plane curvature, and profile curvature), geological structure (stratigraphic lithology and distance from fault zone), meteorological hydrology (average annual rainfall and distance to rivers), and land cover (NDVI, land use, and distance to roads). Then, a single model (logistic regression, support vector machine, and random forest) and a coupled model (IV-LR, IV-SVM, IV-RF, FR-LR, FR-SVM, and FR-RF) based on information volume and frequency ratio were constructed, and the accuracy and reliability of the models were compared and analyzed. Finally, the influence of environmental factors on landslide susceptibility under the optimal model was discussed. The results showed that the prediction accuracy of the nine models ranged from 75.2% (LR model) to 94.9% (FR-RF model), and the coupling accuracy was generally higher than that of the single model. Therefore, the coupling model could improve the prediction accuracy of the model to a certain extent. The FR-RF coupling model had the highest accuracy. Under the optimal model FR-RF, distance from the road, NDVI, and land use were the three most important environmental factors, ac-counting for 20.15%, 13.37%, and 9.69%, respectively. Therefore, it was necessary for Weixin County to strengthen the monitoring of mountains near roads and areas with sparse vegetation to prevent landslides caused by human activities and rainfall.

Gully erosion mapping based on hydro-geomorphometric factors and geographic information system.

Delineation of areas susceptible to gully erosion with high accuracy and low cost using significant factors and statistical model is essential. In the present study, a gully susceptibility erosion map (GEM) was developed using hydro-geomorphometric parameters and geographic information system in western Iran. For this aim, a geographically weighted regression (GWR) model was applied, and its results compared to frequency ratio (FreqR) and logistic regression (LogR) models. Almost twenty effective parameters on gully erosion were detected and mapped in the ArcGIS®10.7 environment. These layers and gully inventory maps (375 gully locations) were prepared using aerial photographs, Google Earth images, and field surveys divided into 70% and 30% (263 and 112 samples) ArcGIS®10.7. The GWR, FreqR, and LogR models were developed to generate gully erosion susceptibility maps. The area under the receiver/relative operating characteristic curve (AUC-ROC) was calculated to validate the generated maps. Based on the LogR model results, soil type (SOT), rock unit (RUN), slope aspect (SLA), Altitude (ALT), annual average precipitation (AAP), morphometric position index (MPI), terrain surface convexity (TSC), and land use (LLC) factors were the most critical conditioning parameters, respectively. The AUC-ROC results show the accuracy of 84.5%, 79.1%, and 78% for GWR, LogR, and FreqR models, respectively. The results show high performance for the GWR compared to LogR and FreqR multivariate and bivariate statistic models. The application of hydro-geomorphological parameters has a significant role in the gully erosion susceptibility zonation. The suggested algorithm can be used for natural hazards and human-made disasters such a gully erosion on a regional scale.

Habitat suitability modeling of Descurainia sophia medicinal plant using three bivariate models.

Climate change has caused medicinal plants to become increasingly endangered. Descurainia sophia (flixweed) is at risk of extinction in Fars Province, Iran, due to climate change and modifications of land use. Flixweed is highly valuable because of its medicinal properties. The conservation of this species using habitat suitability modeling seems necessary. In this research, the geographical locations of D. sophia's distribution in southern Iran were recorded and mapped using ArcGIS 10.2.2. Then, ten important variables affecting the growth of D. sophia medicinal plants were identified and prepared as thematic layers. These variables were, namely, "elevation," "slope degree," "slope aspect," "soil physical characteristics (sand, silt, and clay percentage)," "soil chemical properties (EC and pH)," "annual mean rainfall," "annual mean temperature," "distance to roads," "distance to rivers," and "plan curvature." In this study, three bivariate models, including the "index-of-entropy (IofE)," "frequency ratio (FR)," and "weight of evidence (WofE)," were used for mapping the habitat suitability of D. sophia. Moreover, the ROC curve and AUC index were used for evaluating the accuracy of the models. Based on the results, the IofE model ("AUC": 0.93) was the most accurate, while the FR ("AUC": 0.92) and WofE ("AUC": 0.90) models ranked second and third, respectively. The models in this study can be applied as tools for the protection of endangered medicinal plants. Furthermore, the map could assist planners, decision-makers, and engineers in extending study areas. By determining the habitat maps of medicinal plants, their extinction can be prevented. Such maps can also assist in the propagation of medicinal plants.

Global divergent trends of algal blooms detected by satellite during 1982-2018.

Algal blooms (ABs) in inland lakes have caused adverse ecological effects, and health impairment of animals and humans. We used archived Landsat images to examine ABs in lakes (>1 km2 ) around the globe over a 37-year time span (1982-2018). Out of the 176032 lakes with area >1 km2 detected globally, 863 were impacted by ABs, 708 had sufficiently long records to define a trend, and 66% exhibited increasing trends in frequency ratio (FRQR, ratio of the number of ABs events observed in a year in a given lake to the number of available Landsat images for that lake) or area ratio (AR, ratio of annual maximum area covered by ABs observed in a lake to the surface area of that lake), while 34% showed a decreasing trend. Across North America, an intensification of ABs severity was observed for FRQR (p < .01) and AR (p < .01) before 1999, followed by a decrease in ABs FRQR (p < .01) and AR (p < .05) after the 2000s. The strongest intensification of ABs was observed in Asia, followed by South America, Africa, and Europe. No clear trend was detected for the Oceania. Across climatic zones, the contributions of anthropogenic factors to ABs intensification (16.5% for fertilizer, 19.4% for gross domestic product, and 18.7% for population) were slightly stronger than climatic drivers (10.1% for temperature, 11.7% for wind speed, 16.8% for pressure, and for 11.6% for rainfall). Collectively, these divergent trends indicate that consideration of anthropogenic factors as well as climate change should be at the forefront of management policies aimed at reducing the severity and frequency of ABs in inland waters.

Integrated approach for spatial flood susceptibility assessment in Bhagirathi sub-basin, India using entropy information theory and geospatial technology.

Globally, floods as dynamic hydraulic hazard have caused widespread damages to both socioeconomic conditions and environment at various scales. Managing flood and management of water resource is a global challenge under the changing climatic condition. This study assessed flood susceptibility in the Bhagirathi sub-basin, India using entropy information theory and geospatial technology. Twelve flood susceptibility parameters such as land use/land cover, normalized difference vegetation index (NDVI), slope, elevation, geology, geomorphology, normalized difference water index (NDWI), soil, drainage density, average rainfall, maximum temperature, and humidity during monsoon season were utilized to examine flood susceptibility. Receiver operating characteristics (ROC) curve and Leave-One-Out Cross-Validation (LOOCV) techniques were carried out to validate flood susceptibility map. Kappa statistics was also used to check the reliability of the flood susceptibility model. Findings of the study revealed that nearly 45% area of the sub-basin was highly susceptible to flood followed by moderate (29.3%), very high (19%), low (6.9%), and very low (0.2%). These findings also revealed that nearly 92% area in the eastern, north-eastern, and deltaic sub-basin was susceptible to floods. ROC analysis indicated high success (0.932) and prediction (0.903) rates for the susceptibility map while LOOCV (R2 being 0.97) and Kappa (k = 0.934) have shown substantial prediction of the model. Hence, the susceptibility maps are useful for the local planners and government organization in designing the early flood warning system, and reducing the human and economic losses. The methodology used in this study is applicable for analyzing flood susceptibility at spatial scales in similar systems.

Assessment and delineation of potential groundwater recharge zones in areas prone to saltwater intrusion hazard: a case from Central Iran.

Demarcation of the potential zones for groundwater artificial recharge (GAR) based on the most influential factors is an urgent need for retardation of saltwater intrusion and, thus, sustainability of groundwater resources in the arid zones. This study developed an overlay-index methodology to delineate favorable GAR zones by a linear combination of 11 influential thematic layers in ArcGIS. The proposed methodology was implemented on two coastal aquifer settings Sharif-Abad (SAA) and Qom-Kahak (QKA) aquifers adjacent to Salt Lake, Central Iran. Results indicated that 16.41% of the surface of SAA and 28.58% of QKA were identified as the high potential zone for GAR mainly located in low GW vulnerability parts. Based on the analysis of the area under the receptive operating curve (AUC), the produced GAR map has an accuracy of 0.643, and 0.611 for SAA and QKA aquifers, respectively, which relies on the acceptable limit. Finally, the quantity of water required for GAR to control the intrusion of seawater at the suitable parts of these aquifers was estimated as 25 MCM and 35 MCM, annually. The methodology adopted in this study can serve as a holistic assessment for the detection of SWI in coastal aquifers, and also a comprehensive blueprint for managers to delineate the favorable GAR zones, especially in arid regions.

Landslide susceptibility mapping by integrating analytical hierarchy process, frequency ratio, and fuzzy gamma operator models, case study: North of Lorestan Province, Iran.

Identifying landslide-prone areas is an essential step in assessing landslide risk and reducing landslide damage. In this paper, GIS-based spatial analysis has been used to prepare the landslide susceptibility (LS) map in the north of Lorestan province in western Iran. For this purpose, three main criteria and their sub-criteria were identified as causative factors including geology and topography (i.e., distance from the fault, lithology, slope, aspect, and elevation), climate (i.e., rainfall and distance from the river), and environmental parameters (i.e., distance from the road, land-cover, NDVI). One hundred thirty-six known landslides were randomly divided into training ([Formula: see text] 70%) and validation ([Formula: see text] 30%) datasets. This study is based on the integration of popular analytic hierarchy process (AHP), frequency ratio (FR), and the fuzzy gamma operator (FGO) techniques. AHP was utilized to prioritize causal factors and fuzzy technique was applied in two stages of factor map fuzzification and calculation of sub-criteria maps and then overlap of fuzzified map layers. The fuzzy membership (FM) values were determined based on the FR method, which was normalized between the ranges of 0 and 1. Finally, LS zoning maps were estimated in five susceptibility classes (very low, low, moderate, high, and very high). Validation processes by comparing the three output maps with the layer of validation landslides in the study area and area under receiver operating characteristic curve confirm that the gamma value of 0.9 (AUC = 0.88) offers a more accurate LS map compared to other gamma values. The results of this study will be reliable for landslide risk reduction strategies.

GIS-based for prediction and prevention of environmental geological disaster susceptibility: From a perspective of sustainable development.

Geological disasters seriously threaten the safety of human life, property, ecological resources, and the environment. Effective control of geological disasters is the focus of achieving sustainable social development. The Helong City (Jilin Province, China) was selected as the case study. Combined with GIS technology, a new integrated prediction model of geological disaster susceptibility was developed to improve the accuracy of geological disaster assessment, reduce the cost of geological disaster treatment, and ensure the sustainable development of ecological environment. The research results showed that elevation and normalized difference vegetation index (NDVI) were the key factors affecting susceptibility. Compared with the conventional model, the accuracy of the developing integrated model FR-DT and FR-RF was improved by more than 6%, and the disaster points were more concentrated in the high susceptibility zone. Statistical results of disaster treatment cost estimation and gross domestic product (GDP) value showed that the integrated model can save about 10% of treatment cost, and the ratio of total GDP/disaster governance cost was higher. The performance of the integrated model FR-DT and FR-RF had obvious advantages over the conventional model in terms of prediction accuracy, prevention pertinence, and prevention cost. These research results promote the advancement of geological disaster prevention and control technology, ensure the safety of the geological environment, and are of great significance to the sustainable development of the regional economy.

An Automatic Bearing Fault Diagnosis Method Based on Characteristics Frequency Ratio.

Bearing is a key component of satellite inertia actuators such as moment wheel assemblies (MWAs) and control moment gyros (CMGs), and its operating state is directly related to the performance and service life of satellites. However, because of the complexity of the vibration frequency components of satellite bearing assemblies and the small loading, normal running bearings normally present similar fault characteristics in long-term ground life experiments, which makes it difficult to judge the bearing fault status. This paper proposes an automatic fault diagnosis method for bearings based on a presented indicator called the characteristic frequency ratio. First, the vibration signals of various MWAs were picked up by the bearing vibration test. Then, the improved ensemble empirical mode decomposition (EEMD) method was introduced to demodulate the envelope of the bearing signals, and the fault characteristic frequencies of the vibration signals were acquired. Based on this, the characteristic frequency ratio for fault identification was defined, and a method for determining the threshold of fault judgment was further proposed. Finally, an automatic diagnosis process was proposed and verified by using different bearing fault data. The results show that the presented method is feasible and effective for automatic monitoring and diagnosis of bearing faults.

Phytochemical Analysis and Habitat Suitability Mapping of Glycyrrhiza glabra L. Collected in the Hatay Region of Turkey.

The growth and quality of licorice depend on various environmental factors, including the local climate and soil properties; therefore, its cultivation is often unsuccessful. The current study investigated the key factors that affect the contents of bioactive compounds of Glycyrrhiza glabra L. root and estimated suitable growth zones from collection sites in the Hatay region of Turkey. The contents of three bioactive compounds (glycyrrhizic acid, glabridin, and liquiritin), soil factors (pH, soil bearing capacity, and moisture content), and geographical information (slope, aspect, curvature, elevation, and hillshade) were measured. Meteorological data (temperature and precipitation) were also obtained. An analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) were performed on the data. The soil bearing capacity, moisture content, slope, aspect, curvature, and elevation of the study area showed statistically significant effects on the glycyrrhizic acid and liquiritin contents. A habitat suitability zone map was generated using a GIS-based frequency ratio (FR) model with spatial correlations to the soil, topographical, and meteorological data. The final map categorized the study area into four zones: very high (15.14%), high (31.50%), moderate (40.25%), and low suitability (13.11%). High suitability zones are recommended for further investigation and future cultivation of G. glabra.

GIS-based landslide susceptibility mapping using heuristic and bivariate statistical methods for Iva Valley and environs Southeast Nigeria.

Udi-Iva Valley region of Enugu state has the most concentration of landslide in Southeastern Nigeria. Detailed field investigations alongside satellite image studies were employed to delineate nine landslide conditioning factors. Lithology, elevation, slope, aspect, curvature, distance from drainage, distance from road, land cover, and distance from lineament have been chosen as the landslide causative factors in the study area. This study presents a susceptibility mapping of landslides involving a combined bivariate statistical: frequency ratio (FR) and heuristic analytical hierarchy process (AHP) approach integrated in GIS environment. Validation and cross-validation of the susceptibility maps thus produced was achieved with the aid of landslide density approach in combination with prediction rate curve to check for the uniformity in the class areas in the susceptibility model produced. The analytical hierarchy process (AHP) produced results in which the lithology and slope factors had highest weights of 0.17 and 0.14 respectively. A strong correlation was observed in the lithology and slope conditioning factors; this is evident in the results of the FR approach with 10.68 and 6.86 FR values respectively. The landslide susceptibility maps were classified into five classes: very low susceptibility, low susceptibility, medium susceptibility, high susceptibility and very high susceptibility. Prediction rate curve was used to assess the predictive potential of the landslide susceptibility models, the result showed area under curve values of 70.49% for AHP and 72.09% for FR method. The similarity in the landslide density distribution in the susceptibility class, indicates a correlation between the generated susceptibility model and field observations. The statistical and heuristic methods employed have produced positive results; this was confirmed by the fact that all the 300 landslides were found to have occurred within the high susceptibility and very high susceptibility zones respectively.

Stress-Insensitive Resonant Graphene Mass Sensing via Frequency Ratio.

Herein, a peripherally clamped stretched square monolayer graphene sheet with a side length of 10 nm was demonstrated as a resonator for atomic-scale mass sensing via molecular dynamics (MD) simulation. Then, a novel method of mass determination using the first three resonant modes (mode11, mode21 and mode22) was developed to avoid the disturbance of stress fluctuation in graphene. MD simulation results indicate that improving the prestress in stretched graphene increases the sensitivity significantly. Unfortunately, it is difficult to determine the mass accurately by the stress-reliant fundamental frequency shift. However, the absorbed mass in the middle of graphene sheets decreases the resonant frequency of mode11 dramatically while having negligible effect on that of mode21 and mode22, which implies that the latter two frequency modes are appropriate for compensating the stress-induced frequency shift of mode11. Hence, the absorbed mass, with a resolution of 3.3 × 10-22 g, is found using the frequency ratio of mode11 to mode21 or mode22, despite the unstable prestress ranging from 32 GPa to 47 GPa. This stress insensitivity contributes to the applicability of the graphene-based resonant mass sensor in real applications.

GNSS Spoofing Network Monitoring Based on Differential Pseudorange.

Spoofing is becoming a serious threat to various Global Navigation Satellite System (GNSS) applications, especially for those that require high reliability and security such as power grid synchronization and applications related to first responders and aviation safety. Most current works on anti-spoofing focus on spoofing detection from the individual receiver side, which identifies spoofing when it is under an attack. This paper proposes a novel spoofing network monitoring (SNM) mechanism aiming to reveal the presence of spoofing within an area. Consisting of several receivers and one central processing component, it keeps detecting spoofing even when the network is not attacked. The mechanism is based on the different time difference of arrival (TDOA) properties between spoofing and authentic signals. Normally, TDOAs of spoofing signals from a common spoofer are identical while those of authentic signals from diverse directions are dispersed. The TDOA is measured as the differential pseudorange to carrier frequency ratio (DPF). In a spoofing case, the DPFs include those of both authentic and spoofing signals, among which the DPFs of authentic are dispersed while those of spoofing are almost overlapped. An algorithm is proposed to search for the DPFs that are within a pre-defined small range, and an alarm will be raised if several DPFs are found within such range. The proposed SNM methodology is validated by simulations and a partial field trial. Results show 99.99% detection and 0.01% false alarm probabilities are achieved. The SNM has the potential to be adopted in various applications such as (1) alerting dedicated users when spoofing is occurring, which could significantly shorten the receiver side spoofing cost; (2) in combination with GNSS performance monitoring systems, such as the Continuous Operating Reference System (CORS) and GNSS Availability, Accuracy, Reliability anD Integrity Assessment for Timing and Navigation (GAARDIAN) System, to provide more reliable monitoring services.

RNA-Seq bulked segregant analysis enables the identification of high-resolution genetic markers for breeding in hexaploid wheat.

The identification of genetic markers linked to genes of agronomic importance is a major aim of crop research and breeding programmes. Here, we identify markers for Yr15, a major disease resistance gene for wheat yellow rust, using a segregating F2 population. After phenotyping, we implemented RNA sequencing (RNA-Seq) of bulked pools to identify single-nucleotide polymorphisms (SNP) associated with Yr15. Over 27 000 genes with SNPs were identified between the parents, and then classified based on the results from the sequenced bulks. We calculated the bulk frequency ratio (BFR) of SNPs between resistant and susceptible bulks, selecting those showing sixfold enrichment/depletion in the corresponding bulks (BFR > 6). Using additional filtering criteria, we reduced the number of genes with a putative SNP to 175. The 35 SNPs with the highest BFR values were converted into genome-specific KASP assays using an automated bioinformatics pipeline (PolyMarker) which circumvents the limitations associated with the polyploid wheat genome. Twenty-eight assays were polymorphic of which 22 (63%) mapped in the same linkage group as Yr15. Using these markers, we mapped Yr15 to a 0.77-cM interval. The three most closely linked SNPs were tested across varieties and breeding lines representing UK elite germplasm. Two flanking markers were diagnostic in over 99% of lines tested, thus providing a reliable haplotype for marker-assisted selection in these breeding programmes. Our results demonstrate that the proposed methodology can be applied in polyploid F2 populations to generate high-resolution genetic maps across target intervals.

Influence of buffer distance on environmental geological hazard susceptibility assessment.

Previous researches seldom studied the selection of buffer distance between geological hazards (positive samples) and non-geological hazards (negative samples), and its reasonable selection plays a very important role in improving the accuracy of susceptibility zoning, protecting the environment and reducing the cost of hazard management. Based on GIS technology and random forest (RF) and frequency-ratio random forest (FR-RF) models, this study innovatively explored the influence of randomly selected non-geological hazard samples outside different buffer distances on the susceptibility evaluation results, with buffer distances of 100 m, 500 m, 1000 m and 2000 m in sequence. The results show that through the confusion matrix and ROC curve test, the accuracy of the model increases first and then decreases with the increase of buffer distance. Both RF and FR-RF models have the highest accuracy when the buffer distance is 1000 m, and the accuracy of the RF model is generally higher than that of the FR-RF model under the same buffer distance. Similar attribute values of positive samples and randomly selected negative samples or "extreme" attribute values of negative samples are the main reasons for the differences in evaluation results of different buffer distances. According to the weight analysis of causative factors, the distance from road, the distance from river and the normalized vegetation index (NDVI) are the main factors affecting the occurrence of hazards. The high and very high susceptibility areas in the study area are mainly distributed on both sides of roads and water systems, which are the key areas for hazard prevention and reduction. The HMC of RF-1000m decreased by 3.55% on average compared with other models. The results of this study improve the accuracy of geological hazard susceptibility assessment, maintain the safety of ecological environment, and provide a scientific basis for the selection of buffer distance index in local and surrounding areas in the future.

A geospatial analysis of flood risk zones in Cyprus: insights from statistical and multi-criteria decision analysis methods.

Over the past few decades, flood disasters have emerged as the predominant natural hazard in Cyprus, primarily driven by the escalating influence of climate change in the Mediterranean region. In view of this, the objective of this study is to develop a geospatial flood risk map for the island of Cyprus by considering 14 flood hazard factors and five flood vulnerability factors, utilizing geographic information systems (GIS) and remotely sensed datasets. A comparative assessment was conducted for hazard mapping, employing statistical methods of frequency ratio (FR) and FR Shannon's entropy (FR-SE), and multi-criteria decision analysis method of fuzzy analytic hierarchy process (F-AHP). The main findings indicated that the FR method exhibited the highest predictive capability, establishing it as the most suitable approach for flood hazard mapping. Additionally, vulnerability factors were aggregated using F-AHP to generate the vulnerability map. The resulting flood risk map, which is the product of flood hazard and flood vulnerability, revealed that 9% of the island was located within highly risky regions, while 13.2% was classified as moderate risk zones. Spatial analysis of these high-risk areas indicated their concentration in the primary city districts of the island. Therefore, to mitigate future risks within these cities, an analysis of potential expansion zones was conducted, identifying the best-suited zone exhibiting the lowest risk. The generated flood risk map can serve as a valuable resource for decision-makers on the island, facilitating the integration of flood risk analysis into urban management plans.

Exploring machine learning and statistical approach techniques for landslide susceptibility mapping in Siwalik Himalayan Region using geospatial technology.

Landslides are a natural threat that poses a severe risk to human life and the environment. In the Kumaon mountains region in Uttarakhand (India), Nainital is among the most vulnerable areas prone to landslides inflicting harm to livelihood and civilization due to frequent landslides. Developing a landslide susceptibility map (LSM) in this Nainital area will help alleviate the probability of landslide occurrence. GIS and statistical-based approaches like the certainty factor (CF), information value (IV), frequency ratio (FR) and logistic regression (LR) are used for the assessment of LSM. The landslide inventories were prepared using topography, satellite imagery, lithology, slope, aspect, curvature, soil, land use and land cover, geomorphology, drainage density and lineament density to construct the geodatabase of the elements affecting landslides. Furthermore, the receiver operating characteristic (ROC) curve was used to check the accuracy of the predicting model. The results for the area under the curves (AUCs) were 87.8% for logistic regression, 87.6% for certainty factor, 87.4% for information value and 84.8% for frequency ratio, which indicates satisfactory accuracy in landslide susceptibility mapping. The present study perfectly combines GIS and statistical approaches for mapping landslide susceptibility zonation. Regional land use planners and natural disaster management will benefit from the proposed framework for landslide susceptibility maps.