Analytic Hierarchy Process Technique Research Articles

Modelling, quantification and estimation of the soil water erosion using the Revised Universal Soil Loss Equation with Sediment Delivery Ratio and the analytic hierarchy process models

AbstractThis research used the Revised Universal Soil Loss Equation (RUSLE) with Sediment Delivery Ratio (SDR) model. The analytic hierarchy process (AHP) method, while also incorporating the use of a geographic information system (GIS) and remote sensing (RS) to predict the annual soil loss rate and spatialise the processes of water erosion at the scale of the Loukkos Watershed, Morocco. The RUSLE model and AHP parameters were estimated using RS data, and the erosion vulnerability zones were determined using GIS. We used five parameters, including precipitation erosivity, soil erodibility, slope length and steepness, vegetation cover, and soil erosion control practices in the RUSLE. For the AHP technique, we used seven geo‐environmental factors, including annual average precipitation, drainage density, lineament density, slope, soil texture, land use/land cover and landform maps. The results of RUSLE indicated that the average annual soil loss varied from 0 to 2388.27 . The total estimated annual potential soil loss was approximately 40 790 220.11 , and a sediment yield estimated by RUSLE‐SDR was 8 647 526.66 , equivalent to 6.65 Mm3. This value is very close to the measured value of 6.81 Mm3, for a difference of 0.16 Mm3. Furthermore, the results of the AHP indicate that the soil erosion potential index varies from 0 to 0.205315 . Overall, nearly 13.7% of the area suffered from severe soil erosion exceeding 50 . Approximately 80% of the Loukkos Watershed area experienced only slight erosion, while the remaining 6% incurred moderate erosion. Integrating GIS and RS into the RUSLE model and AHP helped us robustly estimate the extent and degree of erosion risk. Territorial decision‐makers should adopt our results to develop soil conservation strategies, water management plans and other necessary soil and water conservation measures for this region.

MIF and AHP methods for delineation of groundwater potential zones using remote sensing and GIS techniques in Tirunelveli, Tenkasi District, India

Abstract The present study aims to identify whether the delineation of potential groundwater potential zones (GWPZs) is essential for monitoring surface and conserving underground water resources. This study analysed the morphology of earth surface characteristics such as geomorphology, lineament density, lithology, slope, soil types, land use and land cover, drainage density, land surface temperature, normalized difference vegetation index, rainfall, and topographic wetness index parameters to delineate the potential groundwater zones. This article applies the analytical hierarchy process (AHP) and multi-influence factor (MIF) methods to identify potential groundwater zones in the Tirunelveli and Tenkasi districts of Tamil Nadu, India. In the AHP method, individual parameter's geometric mean and normalized weights were determined using the pair-wise matrix analytical method. Remote sensing-geographic information system (RS-GIS) techniques were used to generate thematic map layers from normalized weights to delineate GWPZs. The GWPZs were classified as Very Low, Low, Medium, High, and Very High. The result shows that the GWPZs were identified as 3.57, 0.55, 6.62, 58.09, and 31.21% in the study area for the five classes, respectively. In this study, the thematic maps were also prepared by assigning fixed scores and weights from the MIF approach. In the MIF approach, GWPZs were classified into five classes and identified as 3.16, 0.33, 2.14, 61.21, and 33.16% in the study area, respectively. GWPZ maps were evaluated for both MIF and AHP techniques using the Kappa statistics method with agreement values of 0.77 and 0.72%, respectively. This study's GIS-RS method is more proficient and efficient in delineating the GWPZs.

Designing a Competency-Focused Course on Applied AI Based on Advanced System Research on Business Requirements

The consortium of “The Future is in Applied Artificial Intelligence” Project designed the first competency-based applied artificial intelligence curriculum at the higher-education institution level. The development was based on advanced system research on existing artificial intelligence-related resources and surveying target groups of teachers, information technology students, and employers, which should enhance the performance of implementing artificial intelligence education. A review of applied artificial intelligence was prepared in the form of keyword clustering. The initial data were collected with the help of surveying by identifying job offers, existing artificial intelligence training courses, scientific projects, and real cases. A synthetic analysis of the textual information from the studies was conducted using the word clouds technique. A tensor-based approach was used for the presentation of the competency-based course. The specific numerical requirements for the course in the form of priorities followed from the solution to decision-making problems using the analytic hierarchy process technique. Based on a comprehensive study of surveys, educational experience, scientific projects, and business requirements, and a meta-analysis of the recent references, we specified the criteria for a training course in the form of a tensor-based representation of competencies in relation to content and educational modules.

Febrile disease modeling and diagnosis system for optimizing medical decisions in resource-scarce settings

Febrile diseases are highly prevalent in tropical regions due to elevated humidity and high temperatures. These regions, mainly comprising low- and middle-income countries, often face challenges related to inadequate medical infrastructure and a lack of skilled personnel for accurately diagnosing febrile diseases. Distinguishing one febrile illness from another posed a significant challenge, adding to the complexity of accurate diagnoses. This study developed a multi-symptom multi-disease model to address this challenge, leveraging exploratory data analysis of patient datasets from field studies and the expertise of medical practitioners specializing in tropical diseases. The research investigated the most effective modeling approach for differentiating among 11 febrile illnesses that are prevalent in Nigeria using three intelligent techniques: Extreme Gradient Boost (XGBoost), Fuzzy Cognitive Map (FCM), and Analytic Hierarchy Process (AHP). Comparative analysis demonstrates that AHP surpassed the others, achieving a precision of 84%, recall of 83%, and an F1-score of 84%. Consequently, the AHP technique was integrated into the development of “Febra Diagnostica,” an app aimed at enhancing febrile disease diagnosis in resource-constrained settings. The app was then deployed and utilized in select Nigerian states, offering scalability and empowering frontline health workers in primary health facilities. Febra Diagnostica featured user-friendly interfaces, automated diagnosis and treatment suggestions, streamlined referrals, and provisions for further investigations. Encryption, access control, and multi-factor authentication were some of the security and privacy considerations in the app which gained acceptance from medical experts and adapted to regulatory and ethical policies for smart healthcare systems.

Escalating rainstorm-induced flood risks in the Yellow River Basin, China

The warming climate-induced intensification of hydrological cycle is amplifying extreme precipitation and increasing flood risk at regional and global scales. The evaluation of flood risk, which depends on assessment indicators, weights, as well as data quality, is the first step toward mitigation flood disasters. In this study, we accepted ten risk assessment indicators concerning hazard of disaster-causing factors, sensitivity of hazard-forming environments, and vulnerability of disaster-bearing bodies. We used a combined weighting method based on the analytic hierarchy process and entropy weight (AHP-EW) technique to evaluate rainstorm-induced flood risks across the Yellow River Basin (YRB) from 2000 to 2018. We observed flood hazards are intensifying across the YRB. Specifically, areas with medium flood hazards expanded from the lower to the middle and upper YRB. The sensitivity to floods exhibited a spatial pattern of increasing from southeast to northwest (lower to upper YRB). The increase in vegetation coverage in the middle and upper reaches of the YRB reduces the sensitivity to flood disasters. Flood vulnerability shows an increasing trend, with higher vulnerability mainly observed in the middle and lower YRB. The overall flood risk in the YRB shows an increasing trend, with a 9-fold increase in flood risk from 2000 to 2018. Medium to high flood risk and vulnerability can mainly be identified in the middle and lower YRB, where population and gross domestic product are concentrated. The intensifying rainstorm-induced flood risks over urban areas in these regions should arouse public concern.

Integrating remote sensing, GIS-based, and AHP techniques to delineate groundwater potential zones in the Moulouya Basin, North-East Morocco

Groundwater is a valuable shared resource in the Moulouya Basin, but it has been in decline due to recent years of low rainfall and rapid population growth. To support socio-economic development, there is increased demand for this precious resource. This paper presents a standard methodology for delimiting potential groundwater zones using geographic information systems (GIS), an integrated analytical hierarchy process (AHP), and remote sensing techniques. Seven parameters that monitor the presence and mobility of groundwater, including drainage density, lithology, slope, precipitation, land use/land cover, distance to river, and lineament density, were incorporated into a raster data model using ArcGIS software. AHP-based expert knowledge was used to prepare a groundwater potential index and assign weights to the thematic layers. The study classified the area into five zones of varying groundwater potential: very high (26%), high (51%), moderate (13%), poor (9%), and very poor (1%). The accuracy of the model was validated by comparing the Groundwater Potential Zones map with data from 96 wells and boreholes across the basin. The validity of the results was confirmed by comparing them with the specific yield of the aquifer in the study area, yielding a high correlation coefficient (R2) of 0.79. The analysis revealed that 89.5% of the boreholes were situated in the high and very high potential zones, demonstrating the reliability and robustness of the employed approach. These findings can aid decision-making and planning for sustainable groundwater use in the water-stressed region.

Groundwater recharge potential zone modeling in the Wadi Al-Lith Basin, Saudi Arabia

The management of groundwater resources is crucial in arid and semi-arid regions such as Al-Lith Basin, and therefore, the identification of suitable areas for groundwater recharge is important in solving the problem of water scarcity and ensuring the long-term sustainability of aquifers. In this study, the Analytic Hierarchy Process (AHP) technique, in conjunction with GIS, was applied to locate potential zones for groundwater recharge in the Al-Lith basin. The research methodology includes collecting the essential data, including lineament density, slope, rainfall, drainage density, LU/LC, soil, elevation, and TWI. AHP is used to assign relative weights to different qualities, considering their significance in influencing groundwater recharge. The ArcGIS was employed to process and analyze the weighted layers, which resulted in the creation of a comprehensive map illustrating the potential for groundwater recharge. The study results suggested that the Al-Lith Subbasin has several zones that exhibit different levels of groundwater recharge capability. The results obtained from this study indicated that 12.83% of the basin area has significant groundwater recharge potential zones (GWPZ), ranging from high to very high levels. The groundwater recharge potential zones in the basin are categorized as very poor to poor, covering 19.4% of the area, while the moderate groundwater recharge potential zones represent 67.77% of the basin area. The groundwater protection zones were validated using 19 wells distributed across the basin. The validation findings indicate there is an overlap between the GWPZ classes and the wells in the specified location. Overall, the findings of this study can enhance groundwater resource management and inform policy development for targeted interventions aimed at enhancing groundwater recharge.

Assessing the impact of green finance on sustainable tourism development in China

The escalating urgency to address environmental degradation and promote sustainable development globally has emphasized the critical role of Green Finance (GF) in fostering responsible practices across industries. The tourism sector has drawn significant attention due to its substantial environmental impact, necessitating the implementation of robust financial mechanisms to mitigate its ecological footprint. China, recognized as a key player in the global tourism arena, the convergence of rapid economic expansion and the imperative for environmental conservation presents a distinctive set of challenges and opportunities. The study employed the Fuzzy Analytic Hierarchy Process (AHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodologies to systematically evaluate the impact of GF. The findings of fuzzy AHP indicate the critical role of environmental sustainability measures (GF2) as the topmost impacting factor. This refers to the practice of utilizing financial mechanisms and instruments to support initiatives that promote environmental conservation, minimize ecological footprints, and foster sustainable development. While financial allocation and effectiveness (GF1) and policy and regulatory framework impact (GF4) also hold significant importance in shaping sustainable tourism development. Moreover, the results of fuzzy TOPSIS identified top strategies such as green bond innovation for ecosystem regeneration (S1) and circular economy integration (S4), which can play a significant role in promoting sustainable tourism practices by facilitating initiatives aimed at ecosystem regeneration and integrating circular economy principles into the tourism industry.

Evaluating Lecturer Performance in Vietnam: An Application of Fuzzy AHP and Fuzzy TOPSIS Methods

Higher education is regarded as being of paramount importance in Vietnam and as being essential to raising the level of the country's labor force and promoting economic progress. Evaluation of lecturers is one of the institution's activities and a crucial component of managing human resources in higher education institutions. How to evaluate faculty members' overall performance using a range of criteria is one of the key evaluation-related challenges. This study presents a method that uses fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS) to assess and rank the performance of lecturers. Specifically, the evaluation framework is developed by identifying criteria and sub-criteria based on a comprehensive review of existing literature. Following that, the fuzzy AHP approach is used to determine the weights of the criteria and sub-criteria using the pairwise comparisons. The Fuzzy TOPSIS approach is employed to assess and prioritize lecturers identified through expert evaluation. When applied in group decision-making, utilizing fuzzy AHP and fuzzy TOPSIS promotes agreement among decision-makers and diminishes uncertainty in decision-making processes. The utilization of the multiple criterion measurement approach can then be used to carry out the evaluation. The suggested framework is also demonstrated via a case study. The use of this framework can improve the evaluation's objectivity, accuracy, and scientific methodology. It is believed that this work will assist managers of higher education institutions improve their standards for educational quality.

Assessment of groundwater potential zone mapping for semi-arid environment areas using AHP and MIF techniques

AbstractGroundwater resources are essential for drinking water, irrigation, and the economy mainly in semiarid environments where rainfall is limited. Currently, unpredictable rainfall due to climate change and pollution on the Earth’s surface directly affects groundwater resources. In this area, most people depend on groundwater resources for irrigation and drinking purposes, and every summer, most of the area depends on groundwater in a semiarid environment. Hence, we selected two popular methods, the analytical hierarchy process (AHP) and multiple influence factor (MIF) methods, which can be applied to map groundwater potential zones. Nine thematic layers, such as land use and land cover (LULC), geomorphology, soil, drainage density, slope, lineament density, elevation, groundwater level, and geology maps, were selected for this study using remote sensing and geographic information system (GIS) techniques. These layers are integrated in ArcGIS 10.5 software with the help of the AHP and MIF methods. The map of the groundwater potential zones in the study area revealed four classes, i.e., poor, moderate, good, and very good, based on the AHP and MF methods. The groundwater potential zone area is 241.50 (ha) Poor, 285.64 (ha) moderate, 408.31 (ha) good, and 92.75 (ha) very good using the AHP method. Similarly, the MIF method revealed that the groundwater potential classes were divided into four classes: 351.29 (ha) poor, 511.18 (ha), moderate, 123.95 (ha) good, and 41.78 (ha) very good. The results were compared to determine which methods are best for planning water and land resource development in specific areas that have basaltic rock and drought conditions. Both groundwater potential zone maps were validated with water yield data. The receiver operating characteristic (ROC) curve and area under the curve (AUC) model results are found to be 0.80 (good) and 0.93 (excellent) using the MIF and AHP methods, respectively; hence, the AHP method is best for delineation of groundwater potential zone maps and groundwater resource planning. The present study’s framework and the results will be valuable for improving the efficiency of irrigation, conserving rainwater and maintaining the ecosystem in India.

Remote Sensing-Enabled Urban Growth Simulation Overlaid with AHP-GIS-Based Urban Land Suitability for Potential Development in Mersin Metropolitan Area, Türkiye

This study delves into the integration of analytic hierarchy process (AHP) and geographic information system (GIS) techniques to identify suitable areas for urban development in six districts within the Mersin Metropolitan Area of Turkey. The specific aim is to generate an urban land use suitability map, in order to facilitate informed decision-making for urban development. Drawing on open Landsat satellite imagery and employing the random forest (RF) algorithm, the study spans a fifteen-year period, over which land use/land cover (LULC) changes are measured. Furthermore, a novel approach is introduced by incorporating the urban land use suitability map into an urban growth simulation model developed using a logistic regression (LR) algorithm. This simulation forecasts urban growth up to 2027, enabling planners to evaluate potential development areas against suitability criteria. Findings reveal spatial patterns of land suitability and projected urban growth, aiding decision-makers in selecting optimal areas for development while preserving ecological integrity. Notably, the study emphasizes the importance of considering various factors such as topography, accessibility, soil capability, and geology in urban planning processes. The results showcase significant proportions of the study area as being moderately to highly suitable for urban development, alongside notable shifts in LULC classes over the years. Additionally, the overlay analysis of simulated urban growth and land suitability maps highlights areas with contrasting suitability levels, offering valuable insights for sustainable urban growth strategies. By overlaying the urban land suitability map with a simulated LULC map for 2027, it is revealed that 2247.3 hectares of potential new urbanization areas demonstrate very high suitability for settlement, while 7440.12 hectares exhibit very low suitability. By providing a comprehensive framework for assessing urban land suitability and projecting future growth, this research offers practical implications for policymakers, urban planners, and stakeholders involved in Mersin’s development trajectory, ultimately fostering more sustainable and resilient urban landscapes.

Augmenting input-output system under the curse of dimensionality: AHP-RAS integration and algorithm simulation

ABSTRACT In the estimation of input-output tables, one frequently encountered challenge is the aggregation of sectors in initial tables, which complicates the estimation of more detailed tables due to missing data—a phenomenon referred to as the “curse of dimensionality” when augmenting from lower to higher dimensions. We propose an AHP-RAS method to address the substantial data deficiencies encountered in the expansion of input-output tables. The method applies the Analytical Hierarchy Process (AHP) to transform the subjective relative importance of sectors into input coefficients using Pairwise Comparison Matrices (PCM) and eigenvector methods. Subsequently, it incorporates the RAS approach to supplement objective data, thereby integrating subjective judgement and objective information to tackle the curse of dimensionality. Unlike traditional AHP methods, this study employs binary integer programming to decompose large-scale matrices into subsets, making the AHP technique applicable to input-output tables with a broader range of sectors. We employ Monte Carlo simulation techniques to evaluate the robustness of the proposed methodology, specifically assessing the impacts of subjective judgement, objective data utilization, and the variation in sector numbers. Overall, the method overcomes the curse of dimensionality, yielding coefficients within an acceptable error margin, thereby demonstrating a significant advancement in the field of input-output analysis.

A Hybrid Approach Based on Consensus Decision Making for Green Supplier Selection in Automotive Industry

With increased global commerce, businesses must manage their supply chains while taking into account not only costs but also environmental implications. The decision-making of Green Supplier Selection (GSS) is a strategic priority for companies to survive in challenging market conditions and to effectively and sustainably manage their supply chains in the increasingly polluted and resource-depleted world. Environmental sustainability can be enhanced with the appropriate criteria when choosing green suppliers. Based on these motivations, it is necessary to determine the correct criteria, classify the chosen criteria and employ an effective evaluation method in the GSS process. In particular, evaluating each criterion at its own level is of strategic importance. In this paper, the GSS model, handled by group decision-making, is constructed with multi-sub-criteria to increase the competitive advantage of businesses in challenging market conditions for the purpose of ensuring a sustainable future. A novel hybrid methodology of the Interval Type-2 Fuzzy (IT2F) Analytical Hierarchy Process (AHP) and IT2F Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is presented for the GSS model to deal with uncertainty. This study provides decision-makers with an effective method that performs fuzzy calculations at all steps until a solution is found, especially in areas that may have a complex hierarchical structure, such as the automotive industry. In the proposed method, unlike most studies in the literature, if a criterion has sub-criteria (or multi-sub-criteria) in the hierarchy considered, each criterion is evaluated with other criteria at its own level, without the need for all other criteria to have sub-criteria (or multi-sub-criteria). The effectiveness of the proposed method has been demonstrated by testing it with an application taken from the automotive industry with a complex-structured multi-level hierarchy. Additionally, sensitivity analysis has been conducted to assess the impact of changes in subjective input by means of scenarios.

랜덤박스 캐릭터 디자인에서 감성적 디자인 평가요소 분석

Recently, there has been an expansion in the development of new products based on consumer data, highlighting the growing importance of emotional design elements. Companies are exploring new evaluation methods for innovative character designs. This study proposes a model to assess and improve the emotional aspects of random box character designs. The methodology involved identifying emotional design elements and analyzing their importance through expert surveys using the AHP (Analytic Hierarchy Process) technique. The key elements identified were emotional empathy, creativity, sustainable value, and stability, each showing varying levels of importance. User satisfaction was measured using the IPA (Importance-Performance Analysis) technique and compared with AHP results, revealing that the 'HIRONO character' had higher satisfaction levels than the 'SOSKID character'. This research presents a systematic evaluation method for emotional design elements, expected to provide important guidelines for the development of innovative character designs and contribute to continuous design improvement.

Detection of groundwater conditioning factors in a hilly environment

Champhai, the rice bowl of Mizoram, is known for wet rice practices. Rapid urbanization and global climate change increased the demand for groundwater. Champhai city, being a hilly township in northeast India, is very difficult to identify the potential groundwater water availability. The present study aimed at groundwater potentiality zonation in Champhai town. For these purposes ten (10) factors from topographic, climatic, hydrological and other conditions were assigned weightage based on the AHP (analytical hierarchy process) technique. The aspect was given maximum weightage as the aspect working as the controlling factors of precipitation intensity, vegetation and soil type, while LULC (land use land cover) was assigned minimum weightage that unscientific human activities reduced infiltration rate. Subsequently, an addition of ten weighted rasters was performed for groundwater potentiality zonation. As a result, it was seen that the maximum part of the study area covered low potentiality zones (31.88%), whereas high and very high potential zone covered 14.73% and 5.08, respectively. A scientific validation technique, namely AUC (area under curve) value of 0.737, denoted a good classified groundwater water potentiality zone. Further, the single-layer sensitivity analysis highlighted that elevation, slope, aspect, infiltration number, distance to streams and rainfall had the maximum effective factors based on their effective weightage. The map removal sensitivity analysis highlighted that TWI (Topographic Wetness Index) and aspect had the maximum influence on the model. However, in a hilly environment like Champhai, the output of the current study will work as a blueprint for planning and decision making.

Geospatial technique for the delineation of groundwater potential zones using multi-criteria-based AHP and MIF methods

ABSTRACT In the recent past, the growing climate change and transformation of the green cover into urban areas have posed a threat to natural water supply, which will have a direct impact on water demand for emerging cities such as Nava Raipur. As a result, the increasing demand coupled with the reduced availability of surface water prompts scientific investigation into groundwater availability and its sustainable management as an alternative. The study attempted to determine groundwater potential zones using the analytic hierarchy process (AHP) and multi-influencing factor (MIF) techniques. Twelve contextually significant regulating environmental factors were selected, and their significance and influences on decision-making approaches models have been attempted to determine through the sensitivity analysis. The final GWPZ map obtained, from a combination of thematic layers, was verified using the receiver operating curve (ROC) and the area under curve (AUC) with discharge (yield) records taken from 21 bore wells. According to the ROC curve's AUC estimation, MIF can explain 82.9% of the actual groundwater situation in the region, and for AHP, an AUC value of 0.751 is relatively low. This indicates that the MIF model is the most appropriate to accurately define potential groundwater zones for emerging cities like Nava Raipur.

Mapping natural groundwater potential recharge zones using GIS-AHP in the Upper Cheliff alluvial aquifer, Algeria

Water scarcity is a big issue in arid and semi-arid regions. This challenge is particularly evident in the Upper Cheliff plain in Algeria, where the alluvial aquifer plays a vital role in drinking water supply and supporting irrigation. This aquifer faces high demand and quality issues. A study was conducted in this context, employing a cartographic approach to assess potential groundwater recharge from precipitation into the alluvial aquifer. The current study aimed at mapping zones with potential natural groundwater recharge zones by applying the Analytic Hierarchy Process (AHP) integrated within a Geographic Information System (GIS) environment, combining various factors that can influence recharge, such as rainfall, surface soil type, slope degree, land use and land cover, unsaturated zone, groundwater depth, and curve number. The map resulting from the analysis indicates that only 22% of the assessed area covers zones with very low and low potential recharge, 35% with moderate potential recharge zones, and 43% with high and very high potential recharge zones. This map reveals that the eastern region of the plain, from the cities of Djendel to Ain Soltane, is moderately to highly favorable for recharge. This is due to the natural recharge from rainfall and watercourse infiltration during dam release periods, excess irrigation water, and recharge from the Miocene sandstone aquifer in areas with direct aquifer contact. A validation process was performed using data from 66 wells distributed in this plain and it indicated that 48 wells exhibited good agreement with the resulting map, while 18 wells showed slight deviations. The results indicate an agreement of 72.72% between expected and exist number value of wells which confirming the good prediction of the AHP technique.

CFD-based AHP-TOPSIS decision-making model for gas explosion overpressure mitigation measures in underground utility tunnels

Gas explosion is a highly destructive form of disaster, and it seriously threatens the safety of structures and personnel. The reduction and mitigation measures of gas explosions have been extensively investigated, such as blast walls, safety gaps, and inert gases. Among these numerous measures, how to select the most economical, feasible, and effective measure to reduce the consequences of gas explosions is one of the most important problems faced by decision-makers. Therefore, a CFD-based analytic hierarchy process–Technique for Order Preference by Similarity to an Ideal Solution (AHP-TOPSIS) decision-making model was developed in this study. It combines the subjective experiences of decision-makers with the objective results of CFD numerical simulations, which makes the selected measure more reasonable and effective. The selection of mitigation measures for gas explosion overpressures in the UUT was taken as an example to verify the effectiveness of the proposed decision-making model. Three effective measures proposed in this study (i.e., yve = 10 m, D = 300 μm, yve = 10 m – D = 400 μm) were evaluated using the CFD software Flame Acceleration Simulator (FLACS) and used as target decision measures for the model. The results of the CFD-based AHP-TOPSIS decision-making model show that the measure of yve = 10 m – D = 400 μm is the most appropriate mitigation measure for gas explosion overpressures in the UUT under the evaluation index system, including feasibility, effectiveness, and economy. The results will support the selection of the appropriate overpressure mitigation measure of gas explosions and the blast-resistant design of UUTs.

Multi-model seismic susceptibility assessment of the 1950 great Assam earthquake in the Eastern Himalayan front

The seismic susceptibility and mitigation management is paramount concern in tectonically active area like Northeastern India. This area has been devastated innumerably during the 1950 Assam great earthquake. The present study area falls in the foreland basin (Brahmaputra Valley) of Eastern Himalaya. This region is seismically vulnerable due to the tectonic complexity caused by the convergence of the Eurasian, Indian, and Burmese plates. In such, an area optimal disaster management and preparedness is necessary to define the non-linear character of seismic susceptibility, where population and unscientific urbanization have increased manifold. Therefore, for the present study, various multi-criteria decision making (MCDM) methods such as analytical hierarchy process (AHP), fuzzy-AHP (FAHP), and maximum entropy technique (MaxEnt) have been used for determining the seismic susceptibility, by assigning weightage to nine controlling factors such as: predominant frequency (f0), geology (G), vulnerability index (K), peak amplification (A0), liquefaction potential (LP), groundwater condition (WT), shear wave velocity (Vs30), peak ground acceleration (PGA), and land use/land cover (LU). The MaxEnt model exhibits the highest accuracy (87.5%) when the performance of the models was compared using the receiver operating characteristic curve (ROC) and area under the curve (AUC) value. Further, overlay analysis of best seismic susceptibility model using MaxEnt and PGV-based Japan Meteorological Agency (JMA) intensity shows that 40% the study area is in the very high and high seismic risk zone. In tectonically active areas, this kind of integration work is essential to improves the mitigation strategy and aids urban planners in designing earthquake-resistant buildings.

Risk assessment based on fault tree analysis and analytic hierarchy process in rural areas of Thailand

Introduction The importance of understanding the factors contributing to road accidents at schools cannot be overstated. This study aims to determine the risk of accidents in situations that could lead to accidents near schools. Methods A total of 10 kindergarten to junior high schools were selected for the study. The research used the Haddon Matrix to classify factors at risk of accidents, risk assessment through fault tree analysis, and the analytic hierarchy process (FTA-AHP) techniques. Fourteen sub-criteria were defined for risk identification, risk probability analysis, and risk assessment of the 10 roads. The likelihood of each event was analyzed using the AHP technique for all schools with an expert choice program. RI (random index) was calculated, and CR (consistency ratio) < 0.10 was considered satisfactory. Results The possibility of human accidents ranked highest in three areas: 1) Risk perception in SC 01, 03, and 02, with probabilities of 69.30%, 61.90%, and 57.4%, respectively. 2) The likelihood of accidents from vehicles/equipment, with the highest probabilities in a) Handling (SC01) at 64.70%, b) Braking (SC07) at 61.90%, and c) Lighting (SC03) at 57.80%. 3) The likelihood of accidents from the environment, with the highest probabilities in 1) driving at excessive speeds in areas SC01, 06, 03, and 09, which were 43.60%, 40.90%, and 40.00%, respectively. Conclusions The impacts of all three main factors were as follows: a) humans had the highest impact in the SC01 area (77.90%), b) vehicles/equipment had the highest impact in the SC01 area (75.90%), and c) the highest environmental impact in the SC01 area was 69.90%. The accident risk assessment revealed the highest risk score in three areas: 1) human risk perception, 2) environment with driving at excessive speeds, and 3) vehicle/equipment, including lighting, braking, and handling.