Consistency Ratio Research Articles

Multi-criteria analysis and mapping of the vulnerable flood risk areas of Jigawa State, Nigeria

This study evaluates flood risk factors and maps flood-prone areas in Jigawa State, Nigeria, using geospatial techniques. By integrating various flood-causing factors—such as rainfall distribution, elevation, slope, drainage network and density, land cover and use, flow accumulation, and soil type— through a multi-parametric approach, it employs the Analytical Hierarchy Process (AHP) to perform pairwise comparisons and assign priority weights to each factor. Spatial multi-criteria analysis (MCA) then ranks and identifies potential flood locations. Using ArcGIS's weighted overlay tool, these layers are combined to produce a final flood risk map, classifying areas into high, very high, moderate, low, and very low-risk categories. Elevation and slope were found to have the greatest impact on flooding, with normalized criterion weights of 30 and 22, respectively. The Consistency Ratio (CR) of 0.06 validated the strength of the judgment, and these AHP-derived factor weights were used to create the flood risk map showing areas with varying risk levels, highlighting eight local governments in the study area. —Auyo, Hadejia, Kirika-samma, Kafin-Hausa, Ringim, Miga, Jahun, and Dutse—compose areas of very high risk, while Guri, some portions of northern Kiri-kasamma, southern Jahun, northern Auyo, Birnin-Kudu, and northwestern Ringim local governments comprise high-risk areas. Areas of moderate risk comprise a small portion of northern Kirika-samma, larger portions of Gwaram, small portions of Jahun, Kafin-Hausa, Dutse and Birnin-kudu local governments respectively and areas of low to very low risks consist of many parts of Gwaram and some tiny portions of Birnin-kudu.

Potential Sites for Rainwater Harvesting Focusing on the Sustainable Development Goals Using Remote Sensing and Geographical Information System

An innovative way to combat water scarcity brought on by population increase and climate change is rainwater harvesting (RWH), particularly in arid and semiarid areas. Currently, Pakistan is facing major water issues due to underprivileged water resource management, climate change, land use changes, and the sustainability of local water resources. This research aims to find out the suitable sites and options for RWH structures in the Quetta district of Pakistan by integrating the depression depth technique, Boolean analysis, and weighted linear combination (WLC) with hydrological modeling (HM), multicriteria analysis (MCA), a geographic information system (GIS), and remote sensing (RS). To find suitable sites for RWH, a collection of twelve (12) thematic layers were used, including the slope (SL), land use land cover (LULC), subarea (SA), runoff depth (RD), drainage density (DD), lineament density (LD), infiltration number (IFN), distance from built-up area (DB), distance from roads (DR), distance from lakes (DL), maximum flow distance (MFD), and topographic wetness index (TWI). The Boolean analysis and WLC approach were integrated in the GIS environment. The consistency ratio (CR) was calculated to make sure the assigned weights to thematic layers were consistent. Overall, results show that 6.36% (167.418 km2), 14.34% (377.284 km2), 16.36% (430.444 km2), 18.92% (497.663 km2), and 18.64% (490.224 km2) of the area are in the categories of very high, high, moderate, low, and very low suitability, respectively, for RWH. RWH potential is restricted to 25.35% (666.86 km2) of the area. This research also identifies the five (5) best locations for checking dams and the ten (10) best locations for percolation tanks on the streams. The conducted suitability analysis will assist stakeholders in selecting the optimal locations for RWH structures, facilitating the storage of water, and addressing the severe water scarcity prevalent in the area. This study proposes a novel approach to handle the problems of water shortage in conjunction with environmental and socioeconomic pressures in order to achieve the sustainable development goals (SDGs).

A new performance evaluation indicator for the LEE Jong-wook Fellowship Program of Korea Foundation for International Healthcare to better assess its long-term educational impacts: a Delphi study.

The Dr. LEE Jong-wook Fellowship Program, established by the Korea Foundation for International Healthcare (KOFIH), aims to strengthen healthcare capacity in partner countries. The aim of the study was to develop new performance evaluation indicators for the program to better assess long-term educational impact across various courses and professional roles. A 3-stage process was employed. First, a literature review of established evaluation models (Kirkpatrick's 4 levels, CIPP model, OECD DAC criteria) was conducted to devise evaluation criteria. Second, these criteria were validated via a 2-round Delphi survey with 18 experts in training projects from May 2021 to June 2021. Third, the relative importance of the evaluation criteria was determined using the analytic hierarchy process (AHP), calculating weights and ensuring consistency through the consistency index (CI) and consistency ratio (CR), with CR values below 0.1 indicating acceptable consistency. The literature review led to a combined evaluation model, resulting in 4 evaluation areas, 20 items, and 92 indicators. The Delphi surveys confirmed the validity of these indicators, with content validity ratio values exceeding 0.444. The AHP analysis assigned weights to each indicator, and CR values below 0.1 indicated consistency. The final set of evaluation indicators was confirmed through a workshop with KFIH and adopted as the new evaluation tool. The developed evaluation framework provides a comprehensive tool for assessing the long-term outcomes of the Dr. LEE Jong-wook Fellowship Program. It enhances evaluation capabilities and supports improvements in the training program's effectiveness and international healthcare collaboration.

Selection of Solar-Based Water-Splitting Hydrogen Production and Hydrogen Storage Technologies Using Neutrosophic Analytic Hierarchy Process and Neutrosophic Complex Proportional Assessment

Abstract The production of hydrogen through water-splitting using solar energy has the potential to meet the world’s future energy demand while also addressing the issue of greenhouse gas emissions. To compare and select the best technology among solar thermochemical cycles, photoelectrochemical, and photocatalytic water-splitting in terms of solar-to-hydrogen efficiency, production cost, safety, life expectancy, maintenance cost, and detrimental impact on the environment, this study used a multi-criteria decision analysis hybrid called Neutrosophic Analytic Hierarchy Process (NAHP) and Neutrosophic Complex Proportional Assessment (NCOPRAS). The same method was used to determine the best technology for automobiles among compressed hydrogen storage, metal hydrides, metal-organic frameworks, and chemical storage in terms of gravimetric system capacity, volumetric system capacity, safety, system cost, cycle life, energy efficiency, detrimental impact on the environment, and refueling time. There were 4 experts each in hydrogen storage and hydrogen, specifically from the Philippines, Australia, India, Romania, and Italy. All experts’ judgments have a consistency ratio (CR) < 0.1. The priority criterion for hydrogen production was life expectancy (weighted value (WV) = 0.189), and gravimetric system capacity for hydrogen storage (WV = 0.137). The result showed that among all alternatives for hydrogen production, the solar thermochemical cycle was the best (relative significance value (RSV) = 0.3432) as for hydrogen storage, the best technology among the alternatives was metal hydrides (RSV = 0.2575). A sensitivity analysis was used to check the variability and robustness of the solution.

Assessment of mangrove health based on pressure–state–response framework in Guangxi Beibu Gulf, China

Mangroves, primarily found along tropical and subtropical coastlines, are crucial ecosystems that offer significant ecological services, including coastal protection, carbon sequestration, and biodiversity support. However, these ecosystems are under severe threat from human activities such as aquaculture, agriculture, and urban development, leading to deforestation and degradation. This study aims to develop a comprehensive framework for assessing the health of mangrove ecosystems using the Pressure-State-Response (PSR) model with annual temporal resolution from 2019 to 2023. It consists of three dimensions: Pressure (human activities and natural factors affecting the ecosystem), State (current condition of the ecosystem), and Response (efforts to mitigate adverse effects and restore health). This study incorporates innovative physical and structural parameters alongside traditional indicators to improve the accuracy of the assessment. The method offers advantages such as ease of measurement, low cost, early warning capabilities for ecosystem damage, and high sensitivity to ecological changes. It provides valuable insights for decision-makers and enhances the ability to detect trends in mangrove health over time through a large-scale, multi-temporal analysis. Using remote sensing techniques and the Analytic Hierarchy Process (AHP), we identified 12 key indicators. The AHP method was used to determine the weight of each indicator, achieving a consistency ratio (CR) of less than 0.1, which ensures the reliability of the assessment. The evaluation included calculating and analysing the Comprehensive Health Index (CHI), Pressure (PHI), State (SHI), and Response (RHI) health indices. Results indicated that the mangrove ecosystems in the Beibu Gulf region were “healthy” state by 2019 through 2022. However, in 2023, the health status declined to “relatively healthy,” indicating a downward trend. In conclusion, this study presents a robust framework for assessing mangrove ecosystem health using the PSR model. The findings underscore the urgent need for conservation efforts to reverse the declining health trend and ensure the long-term sustainability of mangrove ecosystems in the Beibu Gulf.

Urban Flood Vulnerability Assessment in Freetown, Sierra Leone: AHP Approach

This study presents a comprehensive flood vulnerability assessment for Freetown, Sierra Leone, spanning the period from 2001 to 2022. The objective of this research was to assess the temporal and spatial changes in the flood vulnerability using Geographic Information System (GIS) tools and AHP-based Multi-Criteria Decision-Making (MCDM) analysis. This study identified the flood-vulnerable zones (FVZs) by integrating critical factors such as the rainfall, NDVI, elevation, slope, drainage density, TWI, distance to road, distance to river, and LULC. The analysis reveals that approximately 60% of the study area is classified as having medium to high vulnerability, with a significant 20% increase in the flood risk observed over the past two decades. In 2001, very-high-vulnerability zones covered about 68.84 km2 (10% of the total area), with high-vulnerability areas encompassing 137.68 km2 (20%). By 2020, very-high-vulnerability zones remained constant at 68.84 km2 (10%), while high-vulnerability areas decreased to 103.26 km2 (15%), and medium-vulnerability zones expanded from 206.51 km2 (30%) in 2001 to 240.93 km2 (35%). The AHP model-derived weights reflect the varied significance of the flood-inducing factors, with rainfall (0.27) being the most critical and elevation (0.04) being the least. A consistency ratio (CR) of 0.068 (< 0.1) confirms the reliability of these weights. The spatial–temporal analysis highlights the east and southeast regions of Freetown as consistently vulnerable over the years, while infrastructure improvements in other areas have contributed to a general decrease in very-high-vulnerability zones. This research highlights the urgent need for resilient urban planning and targeted interventions to mitigate future flood impacts, offering clear insights into the natural and human-induced drivers of the flood risk for effective hazard mitigation and sustainable urban development.

Geospatial and geo-electrical assessment of groundwater vulnerability and potential in parts of Cross River, Southern Nigeria

Assessing groundwater vulnerability and potential is essential for sustainable management. This study evaluates these factors in southern Cross River, Nigeria, using geospatial and geo-electrical methods, including Vertical Electrical Sounding (VES), Geographic Information Systems (GIS), and Remote Sensing (RS). Twenty VES surveys were performed, revealing aquifer resistivity from 5.12 to 506.00 Ω/m, thickness from 6.60 to 34.30 m, and depth from 18.60 to 68.00 m. Rainfall was the most significant factor (38 %) affecting groundwater potential, followed by geology (24 %), slope (13 %), drainage density (8 %), land use/cover (6 %), lineament density (5 %), and soil type (3 %). With a Consistency Ratio (CR) of 0.043884, data consistency was high. Groundwater potential zoning (GWPZ) maps categorized the area into low, moderate, good, and excellent zones, highlighting high potential in western areas with high lineament density and moderate slopes. This research demonstrates the effectiveness of integrating geospatial and geo-electrical techniques for groundwater assessment.

GIS based Soil Erosion Susceptibility Mapping in Middle Himalayas using MCDM: A Case Study of Rajouri District.

The Pirpanjal range, nestled within the Himalayan region, confronts substantial soil erosion challenges owing to its diverse topography and the inherent instability of geological formations. This study focuses on the Rajouri region, to assess soil erosion susceptibility and spatially prioritize vulnerable zones. Utilizing a Geographical Information System (GIS)-based approach, we employed the Analytic Hierarchy Process (AHP) and the Weighted Sum Method (WSM). Various datasets, including precipitation records, geological maps, soil maps, and satellite imagery, were incorporated to derive eleven critical factors. These factors encompassed topographical derivatives; land use and land cover (LULC), soil properties, drainage patterns, rainfall data, lithological characteristics, wetness index, and the vegetative health of the area. The methodology employed yielded unbiased and reliable ratings and weightages, as substantiated by a Consistency Ratio (CR) of 0.095. The results reveal that 41% of the total area in the study region is exceedingly vulnerable to soil erosion. Slope variations range from 0 to 67.14 degrees, delineating high and very high susceptibility zones spanning 531.79 km² (19.82%) and 316.20 km² (11.78%) of the area, respectively. Additionally, assessments based on the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) underscore the severity of soil erosion, covering 40% and 25% of the highly susceptible zones, respectively. High drainage density and curvature zones were identified in 13% and 31% of the study area, respectively. This comprehensive study contributes valuable insights for the planning and implementation of effective soil conservation measures in the Rajouri region.

Evaluation of Indian Railway’s Supply Chain Parameters for Improved Sustainability Under Industry 4.0 Transformation: An Application of AHP and TOPSIS

Indian Railways (IR) is India’s major public transportation system and one of the world’s largest and busiest rail networks. The Indian government is in charge of owning and running it. This paper presents an alternative approach to measuring the overall operational performance of the Indian Railway supply chain. The operation of Indian railways from a supply chain perspective needs to be analyzed to improve customer service and inventory management.Industry 4.0 is used for the easy processing of individual transportation components efficiently and safely. The study is useful for the Indian Railways because it enables the classification of various issues related to government procurement. A supply chain model is proposed to assess service expectations and quality. Various issues (such as the integration system, financial behavior, and management perspective) related to supply chain management have been identified, and various factors affecting availability, railway service quality, and reliability have been analyzed for managing its supply chain. The Analytical Hierarchy Process (AHP) and TOPSIS have been used to examine inventory-related difficulties and make recommendations for the public procurement environment. The result of the AHP method showed that Model 1 (Integration System) has the highest Max_C_weight of 0.5348, followed by Model 2 (Financial Behavior, 0.381695) and Model 3 (Management Perspective, 0.399443). The consistency ratio (CR) for all three models was also less than 0.1. Further, the TOPSIS method was used to validate the accuracy of AHP results and found that the model is significant. This result will help the decision-makers at Indian Railways design an assessment and relationship management model that will help them make procurement the driving force behind their supply chain.As per COP27, Industry 4.0 will help to achieve net zero emissions. The net carbon emissions will be reduced to zero, which will result in a clean environment.To improve sustainability within the framework of the Industry 4.0 transition, the research aimed to evaluate the Indian Railway’s supply chain metrics. Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are methodologies used in this assessment.

Criteria for selection of technology to exploit groundwater in water-scarce area in Vietnam

Abstract. Water extraction solutions in the high mountainous areas of Northern Vietnam commonly include rainwater harvesting, dug wells, drilled wells, groundwater springs, and hanging lakes. However, many water supply systems operate inefficiently and lack flexibility. This study established 10 criteria for selecting groundwater exploitation technology, divided into three groups: water resources, economic and technical, social, and environmental criteria. These criteria aim to identify appropriate water extraction technologies suitable for high mountainous and water-scarce regions, ensuring the long-term and efficient operation of water supply systems. The Geographic Information System (GIS) approach was utilized, integrating the criteria using the Analytical Hierarchy Process (AHP) method to select suitable water extraction technologies. The research results indicate that the evaluation criteria for determining suitable areas for implementing sustainable water extraction technologies, and the weights assigned to these criteria, ensure a consistent ratio (CR) <10 % according to the hierarchical analysis method. This article presents the results of identifying areas suitable for implementing groundwater extraction technologies using drilled wells, based on seven criteria within three groups: water resources, economic and technical, and social criteria. The GIS approach has been employed, and the criteria have been integrated using the AHP to select and determine the areas suitable for implementing groundwater extraction technologies using drilled wells.

Prioritizing Intellectual Capital Assets for Strategic Investment Using an Analytic Network Process Approach

Intellectual Capital Assets (ICAs) refer to the intangible resources that are critical to the creation of organizational value out of human, customer, innovation and process capital. High impact ICAs should be identified and prioritized for investment in by organizations as they strive to increase their value. This research aims at assessing and ranking ICAs by using Analytic Network Process (ANP) model for capturing the interactions between the assets. The method used to gather the pairwise data aimed at supervising CEOs, scientific supervisors, shareholders, employees and customers in order to determine the relative value each ICA contributes to value creation. A mathematical model is employed to translate qualitative assessments into quantitative results with regard to Consistency Index (CI) and Consistency Ratio (CR) to check the reliability of the judgments. Cost benefit analysis shows the ICAs which yield the greatest return on investment. Our findings suggest that knowledge sharing and innovation are the most dominant ICA that positively and significantly affects value creation. These assets involve moderate investment but have huge returns, while other assets such as employee training and customer relations also have greater potential but involve high investment. The results offer practical guidance to organizations in terms of how to effectively prioritize ICAs to support their achievement of strategic objectives in creating innovative value.

Developing integrated person-centered care quality indicators for home health agencies in Shanghai, China: A modified Delphi-analytic hierarchy process study

ObjectivesTo develop person-centered integrated care quality indicators for home health agencies in Shanghai, China. DesignThe study combined the Delphi method and the analytic hierarchy process (AHP). Material and methodsThe Delphi consultation questionnaire was distributed to experts in home healthcare in Shanghai, China. A panel of experts with experience in home healthcare in Shanghai, China, was selected. Purposive sampling was used to choose experts. In this study, ten experts were selected from within sub-fields of home healthcare, including nursing, health policy, quality improvement, person-centered care (PCC), and integrated care. ResultsThe authority coefficient (Cr) in this study was 0.835. The coordination degree of experts’ opinions, which is expressed by Kendall coordination coefficient W (a higher value, better coordination of the item), ranged from 0.352 to 0.386 (p < 0.001). The consistency ratio (CR) values for each level were less than 0.1. The quality indicator system included three first-level indicators, 15 second-level indicators, and 56 third-level indicators. ConclusionsA person-centered integrated care quality indicator system was developed for home health agencies. The findings from this study enable nurses, managers, and policymakers in home and community-based settings to evaluate person-centered integrated care quality using a robust framework. In addition, these indicators can also be used as a standardized tool to guide the development of long-term care services and supports (LTSS) for home-bound elderly.

Navigating urban sustainability: balancing livability, smartness, services and environment through AHP analysis

PurposeSustainable urban development involves integrating intelligent technology into urban planning to promote economic growth, social services, and environmental preservation. It fosters inclusiveness, safety, resilience, and sustainability in urban environments. However, this strategy faces challenges balancing economic growth, social equality, and environmental preservation. Design experts must balance economic development with sustainability preservation to achieve sustainable urban development. They depend on technology for smarter communities. The paper aims to identify the criteria for sustainable urban design and design professionals' bias and their awareness of using the fourth industrial revolution technologies.Design/methodology/approachThe research methodology involves a document review to identify criteria and weights for sustainable urban design. A questionnaire was conducted with 30 design professionals to understand their preferences and biases. A hierarchical framework was created, and pairwise comparisons were computed using an AHP Online System. The weight of each criterion was calculated by aggregating normalized pairwise values within each row and dividing by the total number of cells. The Consistency Ratio (CR) was determined by aggregating the values in the row to produce the weighted aggregate value for each principle.FindingsThe document review emphasizes the significance of sustainable urban design, livability, intelligence, and environmental conservation in sustainable communities. However, participants often overlook these aspects, focusing on comfort, privacy, security, and exclusiveness. They also ignore the need for smart city technologies and environmental protection. This lack of understanding may hinder Egypt’s achievement of SDG11.Research limitations/implicationsThe paper’s main limitation is the small sample size and composition, as all participants were Egyptians working in Egypt or the Arabian Gulf region. The findings cannot be generalized, and future research could involve a team from different regions. Egypt needs to address the lack of knowledge and awareness of sustainable urban design principles among design professionals to achieve SDG 11 goals. This can be achieved through educational initiatives, readjusting priorities towards sustainability, harmonizing policy and practice with sustainable urban design principles, and interdisciplinary teamwork. Design professionals should customize their methods to suit local requirements and ecological circumstances.Originality/valueThe paper reveals that many design professionals lack an integrated and holistic understanding of sustainable urban design principles that the document review outlined. Their responses and practices reveal an incomplete understanding of sustainable urban development, requiring attention to organizing the profession, training practitioners, and reforming planning education. The paper shows that AHP has the potential to serve as a suitable instrument for decision-making; nonetheless, it is essential to exercise caution when interpreting its outcomes.

From data to decisions: evaluating flood vulnerability in the Sindh watershed through Analytical Hierarchy Process

Floods are recurrent global catastrophes causing substantial disruptions to human life, extensive land degradation, and economic losses. This study aims to identify flood-triggering watershed features and employ a Multi-Criteria Decision-Making (MCDM) approach based on the Analytical Hierarchy Process (AHP) model to delineate flood-prone zones. Weights for various flood-influencing factors (slope, rainfall, drainage density, land-use/land-cover, geology, elevation, and soil) were derived using a 7 × 7 AHP decision matrix, reflecting their relative importance. A Consistency Ratio (CR) of 0.089 (within acceptable limits) confirms the validity of the assigned weights. The analysis identified approximately 128.51 km2 as highly vulnerable to flooding, particularly encompassing the entire stretch of riverbanks within the watershed. Historically, snow avalanches and flash floods have been the primary water-related disasters in the region, posing significant threats to critical infrastructure. In this context, this model-based approach facilitates the proactive identification of susceptible areas, thereby promoting improved flood risk mitigation and response strategies.

Use of remote sensing, spatial and geophysical modeling, and real recharging capabilities to identify suitable areas for groundwater exploitation in dry coastal areas

Accurate identification of groundwater potential areas in arid regions is an important task for groundwater management and sustainability. As a result, this study used the innovative integration of remote sensing (RS), geographic information system (GIS), watershed modeling system (WMS), geophysical survey, and water mass balance equation to identify potential groundwater areas in the W. Dara, Eastern Desert, Egypt. A weighted spatial probability model (WSPM) of groundwater potential based on eight regulatory factors was implemented within ArcGIS software. Drainage density (DD), precipitation (P), net groundwater recharge (NGR), terrain slope (TS), lineament density (LD), lithologic group (LG), water quality (TDS), and depth to groundwater level (DGW) are the aspects considered. The Analytical hierarchy process (AHP) method was used to assign weights to these parameters, and their accuracy was estimated using the consistency ratio (CR). The resulting groundwater potential map classified W. Dara study area into five categories, ranging from very low to very high potential. A geophysical survey, in the form of Vertical Electrical Sounding (VES) and Transient Electromagnetic (TEM), was conducted along W. Dara to validate the results of the WSPM, which identified areas of high groundwater potential. The 1D inversion of VES/TEM shows that the central and western parts of W. Dara are considered the most promising areas for groundwater occurrence, and are located in areas of high and very high potential classes derived from WSPM. Moreover, the results of VES and TEM surveys showed that the proposed aquifers (Nubian Sandstone, Miocene, and Quaternary) in the study area are horizontally and vertically connected through a set of normal faults traversing NW-SE. Ten sites have been proposed for drilling additional exploitative wells in W. Dara area based on the WSPM and geophysical survey with the aim of sustainable development. Thus, the integrated techniques applied in this study proved effective in accurately determining the development strategy for arid and semi-arid coastal areas, especially those that suffer from scarcity of rainfall and increased agricultural reclamation requirements in remote areas.

Prioritising Actions for Improving Classroom Air Quality Based on the Analytic Hierarchy Process: Case Studies in China and the UK

The air quality in classrooms significantly impacts school children’s health and learning performance. It has been reported worldwide that classroom air quality does not meet the required standard and actions are pledged for improvement. However, it poses a challenge for decision-making in terms of prioritising taking-up measures. The aim of this study is to propose a method of identifying the action measures for improving classroom air quality and prioritising them. Case studies in the UK and China were conducted, and the key measures were identified through literature studies, open-ended questionnaire surveys, and workshop discussions, which are classified into three categories: B1, policy; B2, technology; and B3, information sharing. The analytical hierarchy process (AHP) is applied in the prioritisation of the action measures. A total of 138 teachers and parents from China and the UK participated in this case study. The genetic algorithm-optimised Hadamard product (GAOHP) method is applied to justify the consistency ratio (CR) within the required threshold value in order to ensure the consistency of the subjective perception and the accuracy of comparative weights. The results show that item B2, technology, is the most desired measure by both Chinese and British parents and teachers, despite the deviation from the optimal choice in China and the UK. Among the proposed action measures, the UK respondents strongly expected air purifiers with natural ventilation as opposed to their Chinese counterparts preferring to share the real-time status of classroom air quality. Our work will provide strong support for the subsequent selection of indoor air quality improvement strategies for schools.

TREE PLANTING PRIORITIZATION IN NATIONAL CAPITAL REGION, PHILIPPINES USING REMOTE SENSING, ANALYTIC HIERARCHY PROCESS AND GIS

Abstract. The need for trees in cities is rapidly increasing because of global warming, rapid population growth, and urbanization. To maximize the benefits of trees, areas of greatest need should be identified and prioritized. In this study, a geospatial framework for tree planting prioritization is developed for NCR using remotely sensed datasets, AHP, and GIS. A Planting Priority Index (PPI) for NCR is derived based on five criteria with corresponding weights calculated based on ratings from 18 experts – air quality (25.2%), tree cover (23.8%), land cover (17.8%), population (17.4%), and land surface temperature (15.8%). The AHP resulted in a consistency ratio (CR) value of 1.7% and a consensus value of 53.2%. PPI values were calculated and statistically significant prioritization clusters and outliers were identified using Anselin Local Moran’s I statistics. The resulting PPI and cluster maps showed that the high-priority areas for tree planting clustered near the region’s center and northwest portion covering the cities of Malabon, South Caloocan, Navotas, Quezon City, Marikina, San Juan, and Mandaluyong, while the low priority areas were found mostly along the region’s outskirts at cities of Pasay, Las Piñas, Muntinlupa, Taguig, Valenzuela, and North Caloocan. The generated maps showing PPI values across the region may aid local government agencies and environmental organizations in evaluating and recalibrating their local greening programs. The workflow presented in this study can also be adopted in other regions with localized variables and site-specific goals relevant to tree planting and greening programs.

Closed-form solutions of consistency ratio in best worst method minmax optimization model: max of edge error matrix and minmax edge error determinant methods

The Best Worst Method (BWM), a reduced version of the AHP, is a recent multi-criteria decision-making tool based on pairwise comparisons with reference to the best and worst criteria. Consistency Ratio (CR) measurement for the rating quality and prioritizations is still a controversial topic. Firstly, the computation for the current CR of BWM must rely on a software optimization solver to find the optimal values, and the solver may not always guarantee the exact optimal solutions, especially if the computational cost settings are not large enough for higher number of criteria. Secondly, much effort to evaluate optimization algorithms is needed to find the best solutions with the least computational resources due to diverse solvers possibly leading to different results with different performances. Thirdly, optimization programming code is not trivial to be implemented for general BWM users. To address these issues, this paper presents the closed-form solutions, Max of Edge Error Matrix (MEEM) (Eq. (44) of Theorem 4) and Minmax Edge Error Determinant (MEED) (Algorithm 1), to replace the BWM optimization models to directly calculate the CR values. Two simulations have been performed with a basic laptop using a single process. One simulation of twenty thousand random pairs of vectors took 26.34 h to perform to verify that the approximate results are higher than or very close to the exact closed-form values of both methods when high computational cost is allocated for the solver to increase the precision. Another simulation of one million random pairs of vectors only took 1.27 h to perform to verify that the MEED and MEEM methods always produce the same results for the number of criteria up to nine. The computational time for the exact results is dramatically reduced when the solver is not needed. The advantages of the proposed solutions include the following: the software to solve the optimization model to obtain CR is unnecessary, and the proposed calculation is extremely efficient to obtain the exact accuracy. The two-step optimization model can preserve the fixed Minmax Edge Error to find the weights which add up to one, which is the condition to determine if the model reaches exact optimal solutions. As the CR optimization model produces multiple versions of weights, which are recommended not to be used, the new method does not need to compute the unnecessary weight values to get the Minmax Edge Error. With the provision of equations leading to closed forms, users can understand the properties of CR in much clearer perspectives. Due to the computational efficiency and explainability, the proposed closed forms can replace the CR optimization model to compute CR efficiently and accurately for all diverse applications using BWM.

Technical risk model of machine learning based software project development - A multinational empirical study using modified Delphi-AHP method

ContextThe development of machine learning (ML) based software projects has increased significantly over the past decade, introducing new technical risks that rarely or never appear in traditional software development projects. ObjectiveThis research aims to identify and prioritize the technical risk factors that may lead to the failure of ML-based software development projects. MethodFirst, a literature review was conducted to compile a preliminary list of technical risk factors for ML-based software project development. Then, two rounds of the modified Delphi process were conducted with 17 ML experts to review and verify the completeness and appropriateness of the preliminary technical risk factors. A hierarchy of five technical risk categories with 22 technical risk factors was concluded for the analytic hierarchy process (AHP). Then, three rounds of online AHP questionnaires were administered. The consistency ratio (CR) was used to check the respondents’ answers, and the quartile deviation (QD) was applied to assess the consensus on all 96 questions. Finally, we prioritized the technical risk categories and associated technical risk factors. ResultsWe found that "data availability and quality" ranked as the top technical risk category in terms of severity, probability, and impact rankings of the five technical risk categories. Furthermore, all four technical risk factors within this category also occupied the top four positions of impact ranking. ConclusionThe research results highlight the crucial role of the four data availability and quality risk factors for the failure of ML-based software project development. The proposed technical risk model of ML-based software project development with the identified severity and probability priorities may provide practitioners and research community with a clear overview, highlighting areas demanding priority attention to effectively mitigate project failure risks. These findings have broader implications for improving the success rates of ML-based software projects across various domains.

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) &lt; 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.