Susceptibility Classes Research Articles

A Model Study of The Impact of Enlightenment Rate on The Dynamics of Typhoid Fever

In this study, a mathematical investigation of the effect of enlightenment rate on the spread of typhoid fever is considered, using a system of nonlinear first order ordinary differential equations and a MATLAB ODE45 numerical scheme. The result shows that a decrease in vaccination rate significantly increases the size of the susceptible () class and the response functions, or thereby heightening the tendency for the disease to be endemic. If then the typhoid infection will spread throughout the population, but if then the infection will not be able to take hold and will eventually die out. Furthermore, it was shown that, the basic reproduction number has the tendency to reveal faster if a disease will result in an epidemic than the effective reproduction number. Finally, as enlightenment rates approach zero, it was observed that, over time, the disease will result in an epidemic. It is therefore recommended that enlightenment rate of the exposed, enlightenment rate to go for treatment and enlightenment rate to go for vaccination should be taken seriously by policy makers in order to stem the spread of typhoid.

Mapping landslide susceptibility in Enfraz to Addis Zemen area Northwestern Ethiopia

The study area (Enfraz to Addis Zemen) is located in northwestern Ethiopia, which frequently experiences landslides, causing damage to farmland, engineering structures, infrastructures, and villages, as well as animal and human fatalities. To manage this catastrophic hazard, a comprehensive GIS-based frequency ratio model (FR) was applied to produce a landslide susceptibility map. In this study, 134 landslides were identified from detailed fieldwork and Google Earth imagery analysis, split into 70% to develop the model and 30% for model validation. The relationship between landslide probability with landslide factor classes of lithology, annual mean rainfall, slope, aspect, curvature, elevation, distance to the river, and land use-land cover was analyzed in a GIS environment. FR model assigns weights to each factor class based on observed frequencies. These weighted factors were summed using a raster calculator to produce landslide susceptibility indexes (LSIs), which were classified into very low, low, moderate, high, and very high susceptibility classes using the natural break classification method. The model’s accuracy and performance were validated using the area under the curve of the receiver operating characteristics curve (ROC), which showed an AUC success rate of 92.2% and a predictive rate of 86.05%. These results confirm that the FR model is effective in landslide susceptibility modeling. The generated map can support decision-makers, urban planners, and researchers in land use planning, landslide mitigation strategies, and future research.

Influences of Media-Induced Awareness and Sanitation Practices on Cholera Epidemic: A Study of Bifurcation and Optimal Control

Media has a notable impact on reducing disease prevalence, while sanitation measures and heightened awareness can effectively control epidemics by diminishing bacterial growth rates and limiting direct contact with infected individuals. In this study, we propose and analyze an epidemic model to explore how media and sanitation practices influence the dynamics of diseases transmitted through direct contact between susceptible and infected individuals, as well as via bacteria in the environment. Our study entails a combined approach involving both analytical and numerical analyses of the system. We observe that the disease-free and endemic equilibria of the system are interconnected through a forward transcritical bifurcation. We estimate the most important model parameter using authentic cholera data from Sudan for calibration. Our numerical findings suggest that regulating disease transmission through direct contact and environmental bacteria can significantly decrease disease prevalence. Additionally, we note that the growth rate of social media advertisements, along with efforts made by government officials and informed individuals to eliminate bacteria through sanitation coverage, introduces destabilizing effects. However, system stability is reestablished when the baseline number of social media advertisements exceeds a specific threshold. The dissemination of awareness among susceptible individuals, as well as the rate of transfer of informed people to the susceptible class, initially leads to destabilization but eventually stabilizes the system. Disease eradication becomes feasible when the rate of transfer of informed individuals to the susceptible class is very low. Moreover, higher initial values for awareness programs and the dissemination rate of awareness could also eliminate the disease from society. Furthermore, we see that increasing the treatment rate of infectives plays a significant role in achieving disease eradication. Moreover, we investigate an optimal control problem that integrates sanitation interventions and awareness protocols.

Landslide susceptibility of Rwanda (Central Africa)

ABSTRACT This paper presents a novel landslide susceptibility map of Rwanda (Central Africa), sadly acknowledged as highly exposed to natural hazards and affected by recurring landslide phenomena. The susceptibility assessment was performed using an integrated approach based on morphometric analysis in a Geographic Information System (GIS), supported by orthophoto analysis and geomorphological field mapping. The analysis was completed following a heuristic approach; 10 landslide controlling parameters were identified. Each parameter was assigned an appropriate weight based on expert judgement and analysed to realize thematic maps. All the data collected were mapped through a process of cartographic overlap to carry out the final zonation. The resulting map was grouped into five susceptibility classes (very low, low, medium, high, and very high), identifying areas with different probabilities of being affected by landslides. The Main Map represents a valuable tool to support sustainable territorial planning and landslide hazard assessment.

Advanced landslide susceptibility mapping and analysis of driving mechanisms using ensemble machine learning models

This study focuses on advanced landslide susceptibility mapping in Bertioga, utilizing ensemble machine-learning models to identify and predict landslide-prone regions. Four algorithms—Adaptive Boosting (AdaBoost), Gradient-Boosting Decision Tree (GBDT), Multilayer Perceptron (MLP), and Random Forest (RF)—were employed to model landslide susceptibility at a 30-m spatial scale, using thirteen landslide conditioning factors (LCFs). These LCFs include topographical, geological, and environmental variables significantly influencing landslide occurrence. The performance of each model was evaluated based on precision (P), recall (R), F1-Score, and area under the curve (AUC), ensuring a robust assessment of prediction accuracy. Furthermore, the models demonstrated varying area coverage in terms of susceptibility classes. For instance, MLP identified 15% of the study area as very low susceptibility, 22% as low, 18% as moderate, 18% as high, and 27% as very high. RF predicted 40% of the region as very low susceptibility, whereas GBDT indicated a substantial 45% of the area as very high risk. AdaBoost, on the other hand, assigned the highest moderate risk coverage at 31%. These results provide a comprehensive understanding of the spatial distribution of landslide risks. Results show that MLP and GBDT achieved higher accuracies in the susceptibility mapping, with AUC values ranging from 0.85 to 0.96. For instance, in Parque CauiBura, MLP and GBDT performed exceptionally well with AUC scores of 0.98 and 0.97, respectively. The average prediction for all cities yielded a high accuracy of 0.95 in Parque CauiBura, followed by 0.92 in Centro. These findings highlight the importance of using ensemble machine learning techniques in regional landslide susceptibility mapping, offering valuable insights for risk management and mitigation strategies in landslide-prone areas such as Bertioga.

Unveiling gully erosion susceptibility: A semi-quantitative modeling approach integrated with field data in contrasting landscapes and climate regions

This study addresses the challenge of mapping gully erosion susceptibility, which is often hindered by limited observed data, the complexity of controlling factors, and the uncertainties associated with characterizing these factors. We utilized a semi-quantitative modeling approach that integrates field-based data and ten controlling factors in the Chemoga watershed of Ethiopia's Upper Blue Nile basin. The resulting gully erosion susceptibility map was compared with a random forest-based approach to assess the methodological applicability. Additionally, an independent dataset from adjacent watersheds was used to validate the approach. The findings revealed that certain landscape positions with specific elevation ranges and slope steepness were more susceptible to gully erosion due to factors such as rainfall, lithological formations, soil characteristics, and agricultural activities. Approximately 10% of the watershed area was affected by gully erosion, with varying susceptibility levels. The comparison between the semi-quantitative and random forest approaches demonstrates a total agreement of around 58%, with minimal differences in susceptibility classes. The study also highlights a strong agreement between simulated and observed susceptibility maps, with a 76% value for the simulation and a lower 48% agreement for the random forest approach. Furthermore, in the adjacent watershed, 65% of the area exhibits no discrepancies between observed and simulated maps. This suggests that the semi-quantitative approach is effective in extrapolating gully erosion susceptibility when detailed data is limited, offering a cost-effective and efficient solution. The study emphasizes the utility of the semi-quantitative modeling approach in mapping gully erosion susceptibility and its potential for practical applications in land management and intervention strategies.

A-posteriori analysis of the performance of a rockfall susceptibility map

BackgroundRockfalls pose a serious threat along the main road network, representing a major hazard in mountainous territory and causing damage and victims. Currently, susceptibility mapping represents a starting point to identify areas more susceptible to rockfall occurrence, a key approach in land use planning and risk management. Despite the extensive use of these maps by decision makers and administrators, the usability of such maps over time and their reliability represent a poorly discussed and examined feature.MethodsHere, we proposed a-posteriori analysis of a three-year-old rockfall susceptibility map, generated along the main road network of the Aosta Valley, an alpine region of north-western Italy. To verify map consistency over time, we implemented a dual-analysis in GIS-environment and by text mining, to respectively analyse the geocoded data and textual information derivable from the regional landslide inventory. The first one allowed us to extract rockfall events occurred after the susceptibility map generation. By this way, we operated to spatially and temporally verify the map consistency. Jointly, the textual information reported in the Event Description Form, linked to each geocoded event, are being exploited. This allowed us to derive relevant information about occurred damage and their degree, presence of protective measures or secondary roads, i.e. involvement of, farm or forestry, road.ResultsThe implemented approach allowed us to prove the quality of the previous map in terms of reliability, robustness and degree of fitting respect to the succession of rockfall occurrence over time. After only three years as many as 198 rockfall events have been occurred and collected since the map was generated. Particularly, 80% of rockfall fit with “high” and “very high” susceptibility classes, and pointed out large involvement of main roads in rockfall occurrence, representing the most affected target with damage to road pavement and vehicles, as well as a relevant involvement of existing rockfall barriers and of the dense network of forestry roads and footpaths that characterize this alpine region.ResultsThe proposed approach representing a starting point in landslide susceptibility map verification and usability as valid instrument for a reliable land planning.

Analyzing the posterior predictive capability and usability of landslide susceptibility maps: a case of Kerala, India

AbstractLandslide susceptibility maps serve as the basis for hazard and risk assessment, as well as risk-informed land use planning at various spatial scales. Researchers create these maps aiming to fulfil a variety of purposes, including infrastructure planning and restrictive land use zoning. These applications require accurate and specific information to fulfil these purposes, as decisions based on these maps have the potential to cost lives and cause infrastructure damage. The usability of the maps depends on whether they provide the required information and their accuracy to be utilized for the intended purpose. Therefore, assessing the usability and predictive accuracy of landslide susceptibility maps is of paramount importance. Typically, the accuracy of the maps is evaluated using the same landslide inventory that was used to create the map, which does not actually test the predictive ability of the maps in future situations. In this study, we briefly reviewed the purposes of the map creation using literature and stakeholder interviews and assessed the accuracy of three landslide susceptibility maps in a posterior manner. We generated a multi-temporal landslide event inventory after the creation dates of these landslide susceptibility maps. We devised a method to evaluate classified maps by making use of Unique Conditions Units (UCUs) to compare the posteriorly predicted susceptibility classes and the new landslide occurrences. Interviews with stakeholders revealed a disconnection between the aims set forth by map producers and the specific needs of the end users. Our posterior assessment shows that overall predictions of the maps provide plausible results; however, their interpretations for different use cases make them less likely to be used. When comparing the maps using UCUs, landslide densities overlap between the different susceptibility classes, indicating low predictive performance of the maps. Direct comparison of all maps shows a low agreement between susceptibility classes, which pinpoints the uncertainties in data and methods used to create different maps. This study highlights the need for purpose-oriented landslide susceptibility mapping and posterior assessment of the predictive capabilities of these maps aiming to fulfil respective purposes.

Grapevines grown from canes having spent 10 months in space: study of their downy mildew susceptibility

The control of plant diseases represents a significant challenge in agriculture, particularly with the current emphasis on reducing pesticide use, necessitating alternative approaches. Among them, resistant plant breeding programs offer a promising alternative. Considering the stressful conditions in space, space conditions emerge as an innovative method for advancing crop improvement. This study explored the unique potential of the International Space Station (ISS) environment to induce beneficial modifications in grapevine, resulting in plants with reduced susceptibility to diseases. Canes of two Vitis vinifera L. cultivars, Cabernet Sauvignon (CS) and Merlot (Me), have undergone a 10-month space journey on-board the ISS, while others stayed on Earth (Ground Control, GC). We conducted a 2-year analysis of the response to downy mildew (Plasmopara viticola) of plants developed from ISS and GC canes, called Mother plants. The disease development of progenies produced from Merlot Mother plants was also evaluated. A higher proportion of individuals with low-susceptibility was noted in the ISS Mother group compared to the controls for both cultivars. This trend was also observed for the Merlot progenies. Finally, 11% of Merlot individuals (Mother and progenies, and for the 2 years of study) were distributed in the low susceptibility class. This study suggests that grapevine canes exposure to ISS conditions resulted in an enhanced percentage of plants showing reduction of susceptibility to downy mildew.

An assessment of global land susceptibility to wind erosion based on deep-active learning modelling and interpretation techniques

Spatial accurate mapping of land susceptibility to wind erosion is necessary to mitigate its destructive consequences. In this research, for the first time, we developed a novel methodology based on deep learning (DL) and active learning (AL) models, their combination (e.g., recurrent neural network (RNN), RNN-AL, gated recurrent units (GRU), and GRU-AL) and three interpretation techniques (e.g., synergy matrix, SHapley Additive exPlanations (SHAP) decision plot, and accumulated local effects (ALE) plot) to map global land susceptibility to wind erosion. In this respect, 13 variables were explored as controlling factors to wind erosion, and eight of them (e.g., wind speed, topsoil carbon content, topsoil clay content, elevation, topsoil gravel fragment, precipitation, topsoil sand content and soil moisture) were selected as important factors via the Harris Hawk Optimization (HHO) feature selection algorithm. The four models were applied to map land susceptibility to wind erosion, and their performance was assessed by three measures consisting of area under of receiver operating characteristic (AUROC) curve, cumulative gain and Kolmogorov Smirnov (KS) statistic plots. The results revealed that GRU-AL model was considered as the most accurate, revealing that 38.5%, 12.6%, 10.3%, 12.5% and 26.1% of the global lands are grouped at very low, low, moderate, high and very high susceptibility classes to wind erosion hazard, respectively. Interpretation techniques were applied to interpret the contribution and impact of the eight input variables on the model’s output. Synergy plot revealed that the soil carbon content exhibited high synergy with DEM and soil moisture on the model’s predictions. ALE plot showed that soil carbon content and precipitation had negative feedback on the prediction of land susceptibility to wind erosion. Based on SHAP decision plot, soil moisture and DEM presented the highest contribution on the model’s output. Results highlighted new regions at high latitudes (southern Greenland coast, hotspots in Alaska and Siberia), which exhibited high and very high land susceptibility to wind erosion.

Landslide susceptibility mapping using modified frequancy ratio method in Correb area, South Wollo, North-Western Ethiopia

Correb area in Mekdela Woreda, South-Wollo Zone, North-Western Ethiopia is one of the most landslide affected region. The primary aim of this study was to assess the landslide susceptibility of the area and create a landslide susceptibility map. A bivariate statistical frequency ratio model was used to accomplish this goal. Using fieldwork and Google Earth picture interpretation, a map of landslides was made. A total of 524 landslides were identified and then classified in to training landslide (75%) for model development and validation landslide (25%) for model validation. Eight landslide causative factors slope, aspect, elevation, distance to drainage, distance to spring, slope material, distance to lineament, and vegetation cover were integrated with training landslides in order to determine frequency ratio value(weight) for each class of landslide causative factors. Furthermore, each causative factor’s prediction rate or weight was established. Relative frequency values were allocated to the appropriate factor classes and prediction rate values were allocated to the appropriate causative factors and summed using a raster calculator algorithm in order to produce the landslide susceptibility map. The final landslide susceptibility map revealed that 27.54% (31.52 km2) of the study area falls under very low susceptible, 30.35% (34.74 km2) low susceptible, 21.36% (24.44 km2) moderate susceptible, 14.74% (16.88 km2) high susceptible, and the rest 6.01% (6.88 km2) as very high susceptible. This shows that more than 20% of the area has probability and risk of landslide while about 80% has relatively low probability and risk of this natural hazard. The performance of modified frequency ratio model for landslide susceptibility mapping was validated using simply overlay, relative landslide density index, and receiver operating characteristics curve. Both training and validation landslides were rare in the very low and low susceptibility classes and strongly concentrated in the high and very high susceptibility classes, according to validation using simple overlay and the relative landslide density index. The implemented model performed exceptionally well, as seen by the receiver operating characteristics curve, which showed 82.6% success rate and an 83.1% prediction rate. In general, the model produced reasonable accuracy. The resultant map would be useful for general land use planning, site selection, and landslide prevention and mitigation programs.

Some Probabilistic Interpretations Related to the Next-Generation Matrix Theory: A Review with Examples

The fact that the famous basic reproduction number R0, i.e., the largest eigenvalue of the next generation matrix FV−1, sometimes has a probabilistic interpretation is not as well known as it deserves to be. It is well understood that half of this formula, −V, is a Markovian generating matrix of a continuous-time Markov chain (CTMC) modeling the evolution of one individual on the compartments. It has also been noted that the not well-enough-known rank-one formula for R0 of Arino et al. (2007) may be interpreted as an expected final reward of a CTMC, whose initial distribution is specified by the rank-one factorization of F. Here, we show that for a large class of ODE epidemic models introduced in Avram et al. (2023), besides the rank-one formula, we may also provide an integral renewal representation of R0 with respect to explicit “age kernels” a(t), which have a matrix exponential form.This latter formula may be also interpreted as an expected reward of a probabilistic continuous Markov chain (CTMC) model. Besides the rather extensively studied rank one case, we also provide an extension to a case with several susceptible classes.

New computations of the fractional worms transmission model in wireless sensor network in view of new integral transform with statistical analysis; an analysis of information and communication technologies

Wireless sensor networks (WSNs) have attracted a lot of interest due to their enormous potential for both military and civilian uses. Worm attacks can quickly target WSNs because of the network's weak security. The worm can spread throughout the network by interacting with a single unsafe node. Moreover, the analysis of worm spread in WSNs can benefit from the use of mathematical epidemic models. We suggest a five-compartment model to characterize the mechanisms of worm proliferation with respect to time in WSN. Taking into account the ZZ transform convoluted with the Atangana-Baleanu-Caputo (ABC) fractional derivative operator, we employ it to analyze the characteristics and applications of the ZZ transformation using the Mittag-Leffler kernel. Moreover, we construct a new algorithm for the homotopy perturbation method (HPM) in conjunction with the ZZ transform technique to generate analytical solutions for the worm transmission model. Also, we address the qualitative aspects such as positivity, boundness, worm-free state, endemic state, basic reproduction number (R0) and worm-free equilibrium stability. Furthermore, we prove that the virus rate in sensor nodes is extinct if R0<1 and the virus persists if R0>1. In addition, we develop analytical findings to evaluate the series of solutions. Furthermore, a detailed statistical analysis is conducted to verify the nonlinear dynamics of the system by verifying the 0−1 test to determine whether uncertainty exists using approximation entropy and the C0 data. An extensive analysis of the vaccination class with respect to the transmitting class as well as the susceptible class is being used to investigate the effects of stepping up precautions on WP in WSN. Moreover, the modeling of the WSN revealed that reducing the fractional-order from 1 requires that the recommended approach be implemented at the highest rate so that there is no long-lasting immunization; instead, nodes remain briefly defensive before becoming vulnerable to future worm attacks.

DYNAMIC ANALYSIS OF SEITR MATHEMATICAL MODEL ON THE SPREAD OF HEPATITIS B DISEASE IN AMBON CITY

Hepatitis B is a disease caused by infection with the HBV (Hepatitis B Virus) virus that commonly infects the liver and can develop into liver cancer. The disease can be transmitted through blood, semen, breast milk, saliva, vaginal fluids, and sperm. One effective way to prevent Hepatitis B disease is by vaccination. This study will construct a mathematical model, such as the SEITR model, to study the spread of Hepatitis B disease in Ambon City. The SEITR epidemic model is a disease spread model that divides the population into five subpopulation classes, namely the susceptible individual subpopulation class, the exposed individual subpopulation class, the infected individual subpopulation class, the treatment individual subpopulation class, and the recovered individual subpopulation class. Based on the dynamic system analysis conducted, two equilibrium points were obtained, namely the disease-free equilibrium point and the endemic equilibrium point. In addition, based on the data and simulation results, it can be concluded that the spread of Hepatitis B in Ambon City depends on the transmission rate from infected individuals to susceptible individuals

A fuzzy analytical hierarchy process -GIS approach to flood susceptibility mapping in NEOM, Saudi Arabia

The Kingdom of Saudi Arabia is undergoing massive and rapid urbanization as part of Vision 2030. This includes development projects along Saudi Arabia’s coastline across the Red Sea. Coastal areas, especially the ones along Saudi’s western regions are susceptible to natural disasters such as flooding. NEOM, a futuristic city currently being developed in the northwest of Saudi Arabia, exemplifies a potential flooding hazard due to its geographic location and proposed urbanization plans. This research aims to enhance flood hazard assessment in NEOM by applying the Fuzzy Analytical Hierarchy Process (FAHP) in combination with Geographic Information System (GIS). Acknowledging traditional limitations related to data availability and parameter selection consensus, the study carefully selects parameters such as drainage density, elevation, slope, rainfall, land use/land cover (LULC), soil type, normalized difference vegetation index (NDVI), and topographic wetness index (TWI). The 30 m DEM was used to derive Drainage Density, Slope, and TWI while LULC data helped assess land cover changes. Rainfall data and soil type information are integrated to evaluate their impact on flood susceptibility. NDVI is employed to analyze vegetation cover. Utilizing ArcGIS Pro’s weighted overlay model, the criteria were combined to generate the final flood susceptibility map. The research outcomes manifest in a flood susceptibility map categorizing areas into seven distinct susceptibility classes, ranging from ‘very low’ to ‘very high.’ A quantitative breakdown in a summary table provides insights into the proportional distribution of flood risk. Results indicate a significant portion of NEOM falls within varying degrees of moderate susceptibility range with relatively limited distribution of flood susceptibility on the extremes, equating to areas with ‘low to moderate’ susceptibility is 4,322.8 km2, areas with ‘moderate’ susceptibility is 5,109.69 km2, areas with ‘moderate to high’ is 4,081.39 km2. The flood susceptibility map developed in this study can shed insights on potential optimum areas for flood mitigation measures (i.e., optimum locations for establishing stormwater collection points).

Mathematical analysis of a novel fractional order vaccination model for Tuberculosis incorporating susceptible class with underlying ailment

ABSTRACT Tuberculosis (TB) is a communicable, airborne infection caused by the bacillus Mycobacterium tuberculosis. Pulmonary tuberculosis (PTB) is the most common presentation, although infection can spread anywhere to cause extra-pulmonary tuberculosis (EPTB). In this paper, a novel fractional order mathematical model is designed for the transmission dynamics of tuberculosis. Uninfected vulnerable individuals are categorized into the following: susceptible with underline ailment and susceptible without underline ailment. The research seeks to qualitatively and quantitatively analyze the proposed model and suggests comprehensive intervention measures for the control of tuberculosis among individuals with underline ailment. Some of the major highlights from the numerical investigation points out that TB vaccination is key to reducing the spread of TB among individuals with underline ailment. Furthermore, efforts to step down the spread of TB through awareness campaigns could significantly reduce the burden of the disease among individuals with co-morbidity.

Flood Susceptibility Mapping Using Information Fusion Paradigm Integrated with Decision Trees

Accurate estimation of flood-damaged zones in a watershed is prominent in guiding a framework for developing sustainable strategies. For these purposes, several flood conditioning factor values at flooded and non-flooded points are extracted, and those points are analyzed using decision tree algorithms and eight novel information fusion techniques to get more reliable flood susceptibility mapping. The belief function values of flood susceptibility values at leaf nodes of the tree are fused by several techniques named Dempster-Shafer (DS), Fuzzy Gamma Overlay (FGO), Hesitant Fuzzy Weighted Averaging (HFWA), Hesitant Fuzzy Weighted Geometric (HFWG), Hesitant Fuzzy Weighted Ordered Averaging (HFWOA), HFWOG, Closeness coefficient (Cc) using Euclidean and Manhattan distances. The flood susceptibility values are extracted from the generated maps and are validated by receiver operating characteristics (ROC) curve parameters, and the seed cell area index (SCAI) of classified flood levels. The area under ROC (AUROC) values of training process are 0.997 for DS, HFWA, HFWOA, and Cc-Euclidean, 0.996 for Cc-Manhattan, 0.995 for FGO and 0.994 for HFWG and HFWOG. The AUROC values of the testing process are 0.951 for DS, HFWA, HFWOA, Cc-Euclidean, and Cc-Manhattan, 0.945 for FGO, 0.943 for HFWG, and 0.941 for HFWOG. True Skill Statistics values are 0.962 and 0.870 for training and testing processes. Although these techniques present excellent performance, the SCAI values versus flood susceptibility classes are fitted to assess the prediction capabilities of the techniques further. HFWA and HFWOG have the first- and second-best performances on the estimations. Hence, information fusion paradigm can be employed to combine flood conditioning factors based on a robust classification method to get reliable predictions of flood potential levels and utilize them for land use and construction planning and management.

Soil surface erosion susceptibility analysis using the USLE model. Case study: Bran - Dragoslavele Corridor, Romania

The present study falls within the scope of the analysis of the morphodynamic potential of the Bran - Dragoslavele Corridor (Romania), a low mountain area, favorable to erosion processes due to the high energy of the relief, diversified lithology, tectonic complexity, and due to the variation in the energy of atmospheric risk phenomena. For the expansion of pastoral agricultural areas and those intended for human settlements, the deforestation of the last 3-4 centuries deprived the slopes and the soil of the protective forest cover on an area of 55.44%, favoring the amplification of the action of natural morphodynamic agents. In this context, the quantitative and spatial assessment of sheet erosion, expressed as the average annual rate of surface soil erosion (measured in tons hectare–1) was approached by applying the calculation model implemented in the GIS environment by using the universal soil loss equation (USLE). Implementation of the model was realized through the spatial delimitation of the potential erosion and the rendering by susceptibility classes of the quantitative results which reveal the fact that the largest surface of the studied mountain unit (91.68%) presents tolerable values, of 0-1.5 t ha–1 year–1. The highest values, over 4 t ha–1 year–1 (1.24%), are characteristic of the calcareous areas belonging to the many sectors of the gorges with discontinuous vegetation, as well as the limestone exploitation area from Mateiaș mountain. The quantitative knowledge and spatial distribution of surface erosion becomes important for communities and local authorities, interested in the planning and management of the implementation of some agrotechnical measures to improve the quality and productivity of soils or to prevent and restrict erosion processes with a tendency to expand areally and in depth.

Building Vulnerability to Landslides: Broad-Scale Assessment in Xinxing County, China.

This study develops a model to assess building vulnerability across Xinxing County by integrating quantitative derivation with machine learning techniques. Building vulnerability is characterized as a function of landslide hazard risk and building resistance, wherein landslide hazard risk is derived using CNN (1D) for nine hazard-causing factors (elevation, slope, slope shape, geotechnical body type, geological structure, vegetation cover, watershed, and land-use type) and landslide sites; building resistance is determined through quantitative derivation. After evaluating the building susceptibility of all the structures, the susceptibility of each village is then calculated through subvillage statistics, which are aimed at identifying the specific needs of each area. Simultaneously, different landslide hazard classes are categorized, and an analysis of the correlation between building resistance and susceptibility reveals that building susceptibility exhibits a positive correlation with landslide hazard and a negative correlation with building resistance. Following a comprehensive assessment of building susceptibility in Xinxing County, a sample encompassing different landslide intensity areas and susceptibility classes of buildings was chosen for on-site validation, thus yielding an accuracy rate of the results as high as 94.5%.

Exploring threshold dynamics of a behavioral epidemic model featuring two susceptible classes and second-order jump–diffusion

Understanding and modeling behavioral changes during epidemic outbreaks is crucial for devising effective public health interventions. In this paper, we introduce an enhanced behavioral epidemic model that integrates novel elements to better capture real-world phenomena. Our model incorporates logistic growth as a recruitment rate, reflecting the population’s intrinsic growth constraints. Additionally, we consider two incidence rates to account for varying transmission dynamics. To extend the model’s applicability, we introduce perturbations through second-order jump–diffusion processes, allowing for abrupt changes in population dynamics. We define a threshold parameter based on the properties of an auxiliary system, which distinguishes between the existence of a unique stationary distribution and stochastic extinction. Through numerical simulations, we validate our theoretical findings and demonstrate the impact of nonlinear noise on the model dynamical behavior.