Linear Combination Method Research Articles

GTDI: a game-theory-based integrated drought index implying hazard-causing and hazard-bearing impact change

Abstract. Developing an effective and reliable integrated drought index is crucial for tracking and identifying droughts. The study employs game theory to create a spatially variable weight drought index (game-theory-based drought index, GTDI) by combining two single-type indices: an agricultural drought index (standardized soil moisture index, SSMI), which implies drought hazard-bearing conditions, and a meteorological drought index (standardized precipitation evapotranspiration index, SPEI), which implies drought hazard-causing conditions. In addition, the entropy-theory-based drought index (ETDI) is introduced to incorporate a spatial comparison with GTDI to illustrate the rationality of gaming weight integration, as both entropy theory and game theory belong to linear combination methods in the development of the integrated drought index and entropy theory has been applied in related research. Leaf area index (LAI) data are employed to confirm the reliability of GTDI in identifying drought by comparing it with SPEI, SSMI, and ETDI. Furthermore, a comparative analysis is conducted on the temporal trajectories and spatial evolution of the GTDI-identified drought to discuss GTDI's level of advancement in monitoring changes in hazard-causing and hazard-bearing impacts. The results show that GTDI has a very high correlation with single-type drought indices (SPEI and SSMI), and its gaming weight integration is more logical and trustworthy than that of ETDI. As a result, it outperforms ETDI, SPEI, and SSMI in recognizing drought spatiotemporally and is projected to replace single-type drought indices to provide a more accurate picture of actual drought. Additionally, GTDI exhibits the gaming feature, indicating a distinct benefit in monitoring changes in hazard-causing and hazard-bearing impacts. The case studies show that drought events in the Wei River basin are dominated by a lack of precipitation. The hazard-causing index, SPEI, dominates the early stages of a drought event, whereas the hazard-bearing index, SSMI, dominates the later stages. This study surely serves as a helpful reference for the development of integrated drought indices as well as regional drought prevention and monitoring.

Multi-criteria site selection for wastewater treatment plant in Bent Saidane, Tunisia

Optimal site selection for the establishment of a municipal waste-water-treatment plant (WWTP) is an amazingly complex task that involves several factors such as regulations, as well as environmental, economic and social considerations. This paper presents the criteria and applied methods (geographic information system (GIS)/fuzzy analytical hierarchy process (AHP)) used in the screening of potential sites for the WWTP of the Bent Saidane rural locality (northern Tunisia). For this reason, nine factors accompanied by three sets of site selection criteria were used. GIS-based multi-criteria analysis techniques based on the standardised fuzzy logic concept and weighted overlay were applied for generation of all maps in the Universal Transverse Mercator coordinate system. Multiscale evaluations based on the weighted linear combination method were also assessed. The study indicated that 0.14 and 0.11% of the total area of the Bent Saidane locality were found to be highly suitable and very highly suitable, respectively. According to these results, the GIS followed by the fuzzy AHP method could pinpoint the optimal sites selected as the most suitable, due to their minimum impact on the environment, as well as on public health, for the construction of a future WWTP in the Bent Saidane rural area.

The geography of wild American ginseng in North Carolina, USA: a comparative study of four capability analysis methods

Wild American ginseng is the root component of a family of plants called Panax, and it grows mainly in the understory of the eastern deciduous forests in North America. The growing market for wild American ginseng in Asia along with the scarcity of this plant in the wild due to rising illegal harvest have been simultaneously pushing up the price of American ginseng in the market, increasing incentives further for illegal harvest of this soon-to-be-extinct species. This paper studies the geography of wild American ginseng to identify the spatial distribution of the suitable habitat of this plant in the wild and provides advice on what conditions are required and what regions are suitable for cultivating it in farms, introducing an alternative to the wild American ginseng to decrease the harvesting pressure on its wild counterpart. By employing four popular geospatial methods, including the Binary Screening method, the Ordinal Ranking method (both Graduated Screening and Addition-of-Factors methods), and the Weighted Linear Combination method, this paper carries out a capability analysis and mapping for wild American ginseng in North Carolina (NC). After building the models, they are validated by using the data of the known locations of wild American ginseng plants in NC. Developing these models helps in gaining a more comprehensive understanding of where the plant naturally grows and sheds light on how government agencies can more effectively and more efficiently plan for law enforcement activities to better protect this plant from illegal harvest. The general pattern of our results suggests that western NC counties such as Jackson, Haywood, Transylvania, Henderson, Cherokee, and Ash are some of the most suitable geographical areas for wild American ginseng to grow. The results of our model validation analysis, along with the comparison of our models' predictions to the observed occurrences of wild American ginseng in nature, indicate that the Binary Screening method’s predictions align with almost 96% of the reported observations in nature. This suggests that the influential natural factors necessary for wild American ginseng are more of complementary factors to each other than being substitutes, meaning that they all must exist in the environment for it to grow, and it could not be that the lack of one natural factor can be compensated by the abundance of another natural factor.

Wildlife habitat suitability analysis and mapping the former dhidhessa wildlife sanctuary using GIS-based analytical hierarchal process and weighted linear combination methods

Management of wildlife populations and the creation of conservation programs depend on the evaluation of wildlife habitats. Habitat suitability mapping is a technique typically used to map appropriate environmental factors and assess species existence in different areas. This study aims to map wildlife habitat suitability sites in Former Dhidhessa wildlife sanctuary, Ethiopia, using GIS-based Analytical Hierarchal Process and Weighted Linear Combination Methods. This study used both primary and secondary data sources. Datasets used to collect data include Digital Elevation Model (DEM), Landsat 9 (OLI/TIRS) and population data. Beside, large mammalian species occurrence data obtained from field survey was used. To map wildlife habitat suitability sites in Former Dhidhessa wildlife sanctuary, environmental factors such as proximity of road network, distance to surface water, land use land cover types, slope, population density and topography were used with the integration of species occurrence data recorded from the study area. These environmental factors scaled to common ranges, and assigned appropriate weights. The quantile classification method was utilized to classify suitability index into five zones (unsuitable, less suitable, moderately suitable, suitable, and highly suitable) to produce the map. Accordingly, the model revealed that 18.9 % of the study area is highly suitable, 19.5 % is suitable, 19.9 % is moderately suitable, 19.5 % is less suitable, and 22.2 % is unsuitable for wildlife. About 58.3 % of the study area is currently identified as suitable for wildlife whereas 41.7 % is unsuitable. This showed that the former Dhidhessa wildlife sanctuary is still having large suitable habitats that can support wide ranges of wildlife. Hence, based on the developed preliminary habitat suitability indices and maps, the federal and local governments shall reevaluate the status of former Dhidhessa wildlife sanctuary and develop future conservation and management plans to enhance the conservation of wildlife and their habitats in the area.

Autoencoder-based composite drought indices

Depending on the type, drought events are described using different indices, such as meteorological, agricultural, and hydrological. The use of different indices often causes confusion for making water-related management decisions. One simple summarized index which can describe the different aspects of drought is desired. Several methods have therefore been proposed, especially with the linear combination method which does not adequately describe drought characteristics. Meanwhile, autoencoders, nonlinear transformation in dimensional reduction, have been applied in the deep learning literature. The objective of this study, therefore, was to derive autoencoder-based composite drought indices (ACDIs). First, a basic autoencoder was directly applied as ACDI, illustrating a negative relation with the observed drought indices which was further multiplied by a negative. Also, the hyperbolic tangent function was adopted instead of the sigmoid transfer function due to its higher sensitivity to drought conditions. For better expression of drought indices, positive and unity constraints were applied for weights, denoted as ACDI-C. Further simplification was made as sACDI by excluding the decoding module since it was not necessary. All applied weights of different sites over a country can be unified into one weight, and the same weights were made for all the sites, called as sACDI1. In the context of model evaluation, a comprehensive analysis was undertaken employing metrics as root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficients. The collective findings underscore the superior performance of both the sACDI and sACDI1 models over their counterparts. Notably, these simplified models manifestly diminished RMSE and MAE values, indicating their enhanced predictive capabilities. Of particular note, sACDI1 exhibited a discernibly lower MAE in comparison to alternative models. Further alarm performance metrics was conducted including the false alarm ratio, probability of detection, and accuracy (ACC). The investigations revealed superiority of the simplified models in terms of alarm ACC, especially in the case of SRSI(A). The developed ACDI can comprehensively summarize multiple drought aspects and provide summarized information about drought conditions.

A constrained NILC method for CMB B mode observations

The Internal Linear Combination (ILC) method is commonly employed to extract the cosmic microwave background (CMB) signal from multi-frequency observation maps.However, the performance of the ILC method tends to degrade when the signal-to-noise ratio (SNR) is relatively low, particularly when measuring the primordial B-modes to detect the primordial gravitational waves. To address this issue, an enhanced version of the ILC method, known as constrained ILC, is proposed.This method is designed to be more suitable for situations with low signal-to-noise ratio (SNR) by incorporating additional prior foreground information.In our study, we have modified the constraint Needlet ILC method and successfully improved its performance at low SNR.We illustrate our methods using mock data generated from the combination of WMAP, Planck and a ground-based experiment in the northern hemisphere, and the chosen noise level for the ground-based experiment are very conservative which can be easily achieved in the very near future. The results show that the level of foreground residual can be well controlled. In comparison to the standard NILC method, which introduces a bias to the tensor-to-scalar ratio (r) of approximately 0.05, the constrained NILC method exhibits a significantly reduced bias of only around 5 × 10-3 towards r which is much smaller than the statistical error.

QWLCPM: A Method for QoS-Aware Forwarding and Caching Using Simple Weighted Linear Combination and Proximity for Named Data Vehicular Sensor Network

The named data vehicular sensor network (NDVSN) has become an increasingly important area of research because of the increasing demand for data transmission in vehicular networks. In such networks, ensuring the quality of service (QoS) of data transmission is essential. The NDVSN is a mobile ad hoc network that uses vehicles equipped with sensors to collect and disseminate data. QoS is critical in vehicular networks, as the data transmission must be reliable, efficient, and timely to support various applications. This paper proposes a QoS-aware forwarding and caching algorithm for NDVSNs, called QWLCPM (QoS-aware Forwarding and Caching using Weighted Linear Combination and Proximity Method). QWLCPM utilizes a weighted linear combination and proximity method to determine stable nodes and the best next-hop forwarding path based on various metrics, including priority, signal strength, vehicle speed, global positioning system data, and vehicle ID. Additionally, it incorporates a weighted linear combination method for the caching mechanism to store frequently accessed data based on zone ID, stability, and priority. The performance of QWLCPM is evaluated through simulations and compared with other forwarding and caching algorithms. QWLCPM’s efficacy stems from its holistic decision-making process, incorporating spatial and temporal elements for efficient cache management. Zone-based caching, showcased in different scenarios, enhances content delivery by utilizing stable nodes. QWLCPM’s proximity considerations significantly improve cache hits, reduce delay, and optimize hop count, especially in scenarios with sparse traffic. Additionally, its priority-based caching mechanism enhances hit ratios and content diversity, emphasizing QWLCPM’s substantial network-improvement potential in vehicular environments. These findings suggest that QWLCPM has the potential to greatly enhance QoS in NDVSNs and serve as a promising solution for future vehicular sensor networks. Future research could focus on refining the details of its implementation, scalability in larger networks, and conducting real-world trials to validate its performance under dynamic conditions.

A novel two-stage combination model for tourism demand forecasting

The tourism literature has shown that a combination of tourism forecasting models can provide better performance than individual models. In the literature, the models to be combined are usually subjectively selected and authors focus on the combination method. In this study, a new direction is represented by the integration of forecasts from several models using a two-stage forecasting system. In stage I, a subset of the best available models is objectively selected according to their performance. Then, in stage II, linear and nonlinear combination methods are utilized to integrate the forecast values of the optimal subset. The empirical study using tourist arrivals in Hong Kong from five major source markets indicates that the proposed two-stage tourism forecasting system with linear combination substantially improves forecasting performance compared with benchmarks. This study also finds that when combining forecasts from different models, the linear combination method is more suitable than nonlinear AI models.

Optimum ground-mounted on-grid connected photovoltaic system

This research endeavors to enhance grid-connected solar photovoltaic systems by refining the methodology used to select suitable geographical photovoltaic sites. The prevalent criterion in existing literature for choosing sites emphasizes proximity to power transmission lines. However, this criterion overlooks essential grid requirements, such as approved bus bars and permissible sizes for new photovoltaic systems. To overcome this limitation, the study introduces a novel criterion. Instead of relying solely on proximity to transmission lines, the proposed approach replaces this with the concept of optimal geographical bus bar locations. Additionally, the generated findings can be fine-tuned through consideration of the specified system size of the grid, thereby improving the identification of geographically suitable sites more effectively.The proposed methodology lies in the incorporation of five distinct approaches. The primary approach employs the Artificial Bee Colony (ABC) algorithm to ascertain the most suitable bus bars and sizes for three Photovoltaic Distributed Generation (PV-DG), with a focus on minimizing power loss within Aden's electric grid. The second approach involves utilizing the Analytic Hierarchy Process-Weighted Linear Combination (AHP-WLC) method in conjunction with Geographic information systems (GIS) to accurately select appropriate PV sites. The third approach is an initial design to eliminate small geolocations that do not align with the PV system's size. Fourthly, the Least Costly Path Model (LCPM) is harnessed to choose a single geolocation. The final approach is dedicated to performing techno-economic analysis.The key findings reveal that the optimal bus bars are located at 30, 46, and 64 in the grid, with a total injected active power of 26 MW. Simultaneously, the identified optimal geolocations cover areas of 0.88, 0.48, and 1.37 km^2 for each bus bar, respectively. In the comparative analysis, the utilization of the proposed criterion involving optimal bus bars results in a larger geographical coverage (97.99 km2) compared to the traditional criterion (76.28 km2). Furthermore, the techno-economic evaluation indicates the viability of implementing PV systems in Aden.The authors have a strong conviction that the framework and outcomes of this study will garner significant attention and prove valuable in assisting decision-makers to improve the planning of grid-connected solar photovoltaic systems.

Enhancing multi-class SSVEP classification performance using regularized covariance estimators, spatiotemporal beamforming, and short-time EEG trials

One of the main challenges in BCI technologies is the problem of poor information transfer rate (ITR) of BCI systems and its effect on reducing the commands users can give. At the state of the art, the Linearly Constrained Minimum Variance (LCMV) beamformer is one of the most effective strategies that can provide satisfactory performance in the analysis of steady-state visual evoked potentials (SSVEP). When only a short-period SSVEP signal is available, a poor estimate of the covariance matrix used in the original LCMV method is obtained. Therefore, the main drawback of the conventional LCMV beamformer and its spatiotemporal form (LCMVst) is that they require a long-period SSVEP signal to achieve a reliable estimate for the beamformer weights. To cope with the mentioned problems, this paper presents two types of regularized estimators to achieve a robust estimate for the covariance matrix when only a short-period SSVEP signal is available. These two types, which are user-independent, include the convex combination (CC) and general linear combination (GLC) methods. In the next step, in order to provide suitable beamformer weights, the CC and GLC covariance estimators are combined with the original LCMVst beamformer. Furthermore, two new subject-based electrode selection algorithms, i.e., sequential forward electrode selection (SFES) algorithm and common spatial pattern (CSP) based technique, are applied to pick out the best collection of electrodes and to maximize the classification accuracy. The introduced methods were assessed using a four-class SSVEP dataset recorded on 20 subjects. Experimental results indicate that the proposed estimators optimally classify SSVEPs by estimating proper beamformer weights. The proposed LCMVst-CC and LCMVst-GLC beamformers improved the average classification accuracy by about 27.75% in comparison to the original LCMV-based beamformer at the 0.25-second segment length. Using SSVEP stimulations, we show that our proposed beamformers offer a better classification performance than the conventional LCMV-based spatiotemporal beamformer for all stimulation times.

Identification of priority areas for water conservation in the Alto Iguaçu basin, Brazil.

Geoprocessing tools are great allies in the management of water resources, permitting the evaluation of several factors integrated into a multicriteria analysis. This improves the interpretation of the behavior of water and soil in hydrographic basins, allowing the delimitation of priority areas for actions. The objective of this study was to prepare a map of priority areas for water conservation in the Alto Iguaçu basin through a multicriteria analysis, aiming to assist in the management of water resources in the region. The priority map for water conservation was prepared using the weighted linear combination (WLC) method, considering the weight added to the factors that were obtained by the analytic hierarchy process (AHP) method. The method successfully identified priority areas for water conservation, where 40.7% of the Alto Iguaçu watershed was considered High priority and Very High priority, a number consistent with regional characteristics. The multicriteria analysis proved to be a flexible and easy to implement methodology, which can help managers when considering the trade-offs among several factors. The implementation of water conservation actions in areas that have been classified as being of High and Very High priority can benefit the regional ecosystem by controlling erosion, regulating the flow of water, and thus conserving the quantity and quality of water in the basin and its downstream region, benefiting the population and regional economics. Integr Environ Assess Manag 2024;20:1087-1098. © 2023 SETAC.

Land Suitability Assessment for Pistachio Cultivation Using GIS and Multi-Criteria Decision-Making: A Case Study of Mardin, Turkey.

Site selection for pistachio orchards is an important issue for sustainable agricultural policies, crop productivity, agricultural planning, and communities. This study aims to investigate suitable places for pistachio in the Mardin Province (SE Turkey) by considering several variables, such as meteorological data, topographic conditions, economic factors, and soil characteristics, using Geographic Information System (GIS) and Multi-Criteria Decision Analysis. Pistachio farmers, expert opinions, and literature data were used to determine the requirements for pistachio cultivation. Four main assessment criteria (thirteen sub-criteria), sixty value ranges, and fourteen exclusion criteria were determined for the pistachio land suitability assessment. The weighting of the evaluation criteria was calculated using the Analytical Hierarchy Process (AHP). Farmers and experts have stated that meteorological factors are more important than soil, topography, and economic factors. All data were transferred to the GIS environment, and a land suitability map was created using the weighted linear combination method. The results show that Mardin province has very suitable lands for pistachio cultivation. The resulting map determined that the 228,891.59ha area in Mardin province is very suitable for pistachio. To evaluate the accuracy of the land suitability map generated for pistachio, the Receiver Operating Characteristic (ROC) curve was used. The value of the area under the curve (AUC) was calculated to be 0.806, which indicates that the study is consistent. The created suitability map will be an essential data source for developing sustainable agricultural strategies in the Southeastern Anatolia region.

Nonfragile power and frequency control in islanded microgrids under abnormal asynchronous stochastic cyber attacks

In this paper, we focus on the real power sharing and frequency regulation of distributed generators in islanded microgrids with abnormal asynchronous stochastic cyber attacks, which is of great significance to the information security and stable operation of microgrids. Firstly, considering the possible cyber attacks in the communication network, a distributed non-fragile controller with coupled memory delay is proposed according to the nonperiodic sampled-data control. Then, the construction of delay-dependent two-sided looped-functional makes the Lyapunov–Krasovskii functional contain more delay and sampling information and relaxes constraints on free matrices. In addition, based on the enhanced integral inequality technique and the linear convex combination method, a sampling-based consensus protocol is presented to solve the issues of real power sharing and frequency regulation in the islanded microgrids. Finally, to verify the effectiveness and feasibility of the designed control strategy, a modified IEEE 34-bus test system is used for the experiment.

GIS-based ecotourism potentiality mapping in the East Hararghe Zone, Ethiopia

The East Hararghe Zone (EHZ) is one of the eastern Ethiopian zones most endowed with diverse landscapes and abundant resources to promote and use for ecotourism development. Potential ecotourism sites have, however, hardly ever been explored so far. The objective of this study was to model and identify potential ecotourism sites by combining Geographic Information System (GIS)-based Multi-Criteria Decision-Making (MCDM) and Analytical Hierarchy Process (AHP) methods. Six criteria, including landscape and naturalness, wildlife, topography, accessibility, geology, and climate, were established based on experts' preferences and literature, and the thematic factors for suitability modeling were derived from freely accessible satellite imagery and existing geospatial data and combined using a weighted linear combination (WLC) method. The results reveal that about 26.19% were highly suitable, 35.34% were moderately suitable, 25.28% were marginally suitable, and 13.17% were not suitable. Most areas with high to marginal suitability were found in the southeast, southwest, and uplands in the northern part, while the southernmost extent had the highest proportion of areas unsuitable for ecotourism development. The area under the curve (AUC) assessment has verified the model's performance, resulting in an overall AUC of 74.96%. This suggests that a model-driven map is a reliable spatial support tool for sustainable ecotourism development in areas with diverse landscapes and resources.

Cross feature enhanced graph convolutional network for aspect-based sentiment analysis

Traditional graph convolutional neural networks (GCN) utilizing linear feature combination methods have limited capacity to capture the interaction between complex features. While current research has extensively investigated various syntactic dependency tree structures, the optimization of GCN algorithms has often been overlooked, leading to suboptimal efficiency in practical applications. To address this issue, this paper proposes a cross-feature method that utilizes feature vector multiplication to construct non-linear combinations of GCN features and enhance the model’s capability to extract complex feature correlations. Experimental results demonstrate the superiority of the proposed method, with our models outperforming state-of-the-art methods and achieving significant improvements on three standard benchmark datasets. These results suggest that the cross-feature method can effectively extract potential connections between features, highlighting its potential for improving the performance of GCN-based models in real-world applications.

Continuum limits for rational, breather solutions, and gauge equivalent model of the generalized discrete nonlinear Schrödinger equation

In this paper, we focus on an integrable higher‐order discrete nonlinear Schrödinger (dNLS) equation, which yields the Lakshmanan–Porsezian–Daniel (LPD) equation in the continuum limit. The breather solutions of the higher‐order dNLS are obtained for the first time. We show that the rational solutions and breather solutions of the higher‐order dNLS equation yield the counterparts of the integrable fourth‐order NLS equation under proper continuous limits. Most importantly, we also explore the gauge equivalent equation of the higher‐order dNLS equation from the point of view of the continuous limit theory. We make integrable discretization for the linear spectrum problem of the integrable generalized Heisenberg ferromagnetic equation that is just the gauge equivalent structure of the LPD equation. Compatibility condition of the discrete linear spectrum problem leads to a discrete integrable generalized Heisenberg Ferromagnetic equation. It is revealed that the discrete linear spectrum problem of the discrete integrable generalized ferromagnetic equation converges to the corresponding spectrum problem of the integrable generalized Feisenberg spin model in the continuum limit.In addition, with the help of the method of linear combinations for the conserved fluxes and densities, the conservation laws of the integrable higher‐order dNLS equation converge to the homologous results of the LPD equation.

Performance prediction of multivariable linear regression based on the optimal influencing factors for ranking aggregation in crowdsourcing task

PurposeFor ranking aggregation in crowdsourcing task, the key issue is how to select the optimal working group with a given number of workers to optimize the performance of their aggregation. Performance prediction for ranking aggregation can solve this issue effectively. However, the performance prediction effect for ranking aggregation varies greatly due to the different influencing factors selected. Although questions on why and how data fusion methods perform well have been thoroughly discussed in the past, there is a lack of insight about how to select influencing factors to predict the performance and how much can be improved of.Design/methodology/approachIn this paper, performance prediction of multivariable linear regression based on the optimal influencing factors for ranking aggregation in crowdsourcing task is studied. An influencing factor optimization selection method based on stepwise regression (IFOS-SR) is proposed to screen the optimal influencing factors. A working group selection model based on the optimal influencing factors is built to select the optimal working group with a given number of workers.FindingsThe proposed approach can identify the optimal influencing factors of ranking aggregation, predict the aggregation performance more accurately than the state-of-the-art methods and select the optimal working group with a given number of workers.Originality/valueTo find out under which condition data fusion method may lead to performance improvement for ranking aggregation in crowdsourcing task, the optimal influencing factors are identified by the IFOS-SR method. This paper presents an analysis of the behavior of the linear combination method and the CombSUM method based on the optimal influencing factors, and optimizes the task assignment with a given number of workers by the optimal working group selection method.

Event-Triggered Control Strategy for 2-DoF Helicopter System Under DoS Attacks

This paper focuses on the event-triggered strategy design of a two-degree-of-freedom (DoF) helicopter system (HS) with denial-of-service (DoS) attacks, which has essential applications in traffic information security and military information security. First, the attack characteristics of DoS are introduced from the cloud servers and converters at the physical layer. Then, hackers launching a DoS attack on HS also are considered. Second, a new first-order weight sampling (FOWS) control method is developed, fully considering sampling intervals resource consumption. Meanwhile, novel looped-functionals are constructed, which contain more sampling time information and sampling interval weight information. In addition, quadratic scaling is used to process the integral terms to obtain a tighter upper limit, which helps optimize the two control algorithms (TCAs). Furthermore, TCAs are obtained using the linear convex combination method (LCCM). Then, a new event-triggered controller under DoS attacks is designed to ensure the 2-DoF HS is asymptotically stable. Finally, the feasibility and superiority of our designed approach are verified through an experimental simulation.

Modelling of Wildfire Susceptibility in Different Climate Zones in Montenegro Using GIS-MCDA

Montenegro has different influences on the weather and climate; in general, according to Köppen’s classification, there are two climate zones: warm temperate (C) and cold temperate (D). The aim of this study is to determine the susceptibility to wildfires in the Montenegrin coastal municipality of Budva and the northern municipality of Rožaje, which are located in different climatic conditions, using multicriteria GIS decision analysis (GIS-MCDA). Nine natural and anthropogenic criteria were used for the analysis. Open geospatial data were used as input data for all criteria. The assignment of weighting coefficients for the criteria in relation to wildfire susceptibility importance was based on the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (F-AHP) procedures. The results for the AHP and F-AHP models were obtained using the Weighted Linear Combination (WLC) method. According to the AHP model, the very high and high category covers 80.93% of the total area in Budva and 80.65% in Rožaje. According to the F-AHP model, the very high and high category occupies 80.71% of the total area in Budva and 82.30% in Rožaje. The validation shows that the models of GIS-MCDA perform fair in both climatic zones. The proposed models, especially in the absence of geospatial data, can be a strategic and operational advantage in the development of plans and strategies for protection against wildfires.

Earthquake vulnerability assessment through spatial multi-criteria analysis: a case study of Quetta city, Pakistan

Vulnerability assessment of an urban area to earthquake hazards is the requirement to attaining sustainable urban resilience. Quetta city is the capital of the province of Balochistan surrounded by mountains with the existence of many active faults. The main objective of the current study was to assess the earthquake vulnerability in Quetta valley. A total of 400 households were selected for the primary household survey. A simple random sampling technique is employed using proportionate allocation method because of the heterogeneity of the area in terms of population. Secondary data was taken from the Geological Survey of Pakistan (GSP), the Pakistan Bureau of Statistics (PBS), and the United States Geological Survey (USGS). Analytical Hierarchal Process (AHP) & Weighted Linear Combination (WLC) methods are used to identify earthquake vulnerability. The results of the study reveal that the Northwestern and Southeastern parts of Quetta are seismically vulnerable to earthquake hazard; its geology coupled with the human dimension indicates a more disastrous future events. Results of the composite earthquake vulnerability show that seven out of thirteen Zones of Quetta city are highly vulnerable to all four components of vulnerability. Four Zones have a medium level of vulnerability and only two Zones are considered low earthquake vulnerable Zones in the study area. The topmost influential factors that make Quetta city highly earthquake-vulnerable are the soil type (0.45), peak ground acceleration (0.34), proximity to the hospitals and fire service stations (0.22, 0.21), elderly population (0.22) and fault lines (0.21) as shown in Fig. 12. This research study has significant implications for urban planners and provides a risk reduction platform in order to reduce future earthquake losses and make Quetta city more resilient and sustainable.