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This study aims at providing expertise for preparing public-based flood mapping and estimating flood risks in growing urban areas. To model and predict the magnitude of flood risk areas, an integrated Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) analysis techniques are used for the case of Eldoret Municipality in Kenya. The flood risk vulnerability mapping follows a multi-parametric approach and integrates some of the flooding causative factors such as rainfall distribution, elevation and slope, drainage network and density, land-use/land-cover and soil type. From the vulnerability mapping, urban flood risk index (UFRI) for the case study area, which is determined by the degree of vulnerability and exposure is also derived. The results are validated using flood depth measurements, with a minimum average difference of 0.01 m and a maximum average difference of 0.37 m in depth of observed flooding in the different flood prone areas. Similarly with respect to area extents, a maximum error of not more than 8% was observed in the highly vulnerable flood zones. In addition, the Consistency Ratio which shows an acceptable level of 0.09 was calculated and further validated the strength of the proposed approach.

Risk management plays a vital role in ensuring the safety and efficiency of tunnel construction by considering various factors, including uncertainties associated with concurrent adverse sources. One key aspect of risk management is prioritizing hazardous zones to devise an optimal countermeasure plan within time and cost constraints. This study developed an advanced tunnel risk management model, combining the analytic hierarchy process (AHP) and fuzzy set theory (FST). The model derived the impact using AHP and the probability using FST. By selectively combining causal factors that met the selection criterion, the risk of each hazardous zone was determined, enabling the prioritization of identified hazardous zones. The model application results indicated that causal combinations associated with significant tunnel convergence posed a relatively high risk. Moreover, the hazardous zones where unstable ground formations were excavated by a gripper tunnel boring machine (TBM) were revealed as the most vulnerable locations. Consequently, adopting a shield TBM or implementing ground reinforcement is recommended. Overall, the developed model effectively prioritizes identified hazardous zones and provides an optimal countermeasure plan, contributing to the overall safety and efficiency of the operations.

Lao PDR faces seasonal power supply problems due to its heavy reliance on hydropower. Thus, the aim of this paper was to prioritize renewable energy (RE) resources for sustainable electricity generation in Lao PDR using the analytic hierarchy process (AHP) method, and to further estimate the energy available for the prioritized RE to enhance the seasonal power supply. Four RE alternatives were assessed considering technological, economic, environmental, and social criteria with twelve overall sub-criteria. The results indicated that hydropower was the most highly prioritized alternative, followed by solar. The resulting weights of the RE prioritization were in agreement with the Lao energy policy and plan. In order to address the seasonal power supply problem, setting-up floating photovoltaic (FPV) units in the existing hydropower reservoirs was proposed. The FPV potential was estimated, and the results revealed that the predicted power demand by 2030, as calculated in the latest Lao national power development strategy, could be fully covered by integrating the FPV output from 10% coverage of the water surface in four existing hydropower reservoirs with the existing power supply in 2020. The proposed FPV technology would provide a solution to enhance the seasonal power supply and reduce the power import.

Purpose: To reduce the subjective prejudice and uncertainty in evaluating product quality. Design/methodology/approach: AHP method is used to analyze the structure of product quality evaluation problem and determine weights for evaluation criteria. After structure judge matrix, sequencing calculation and concordance examination, evaluation methods such as fuzzy synthesis evaluation are used to calculate the integrated quality evaluation result of each product. Findings: A new model is proposed by comprehensively using AHP method, weighted comprehensive evaluation and fuzzy comprehensive evaluation. A practical example of a product has been used to illustrate the theoretical qualitative proposed evaluation model. Practical implications: The result of this research offers a new method for the enterprises production quality management. Originality/value: Using AHP fuzzy comprehensive evaluation method in building product quality evaluation system.

In the study of biomass assessment, geospatial modeling-based analysis becomes crucial for the sustainable management of agriculture. Currently, there is no integrated sustainability assessment of the geographic information system (GIS) cluster or suitability analysis for the feedstock of crop residues. In order to fill this research gap and support the strategy of bioenergy formulation with the circular economy concept in agriculture residues in Myanmar, this study aims to assess the energy generation potential and site locations of treatment facilities for crop residue, utilizing the integrated assessment of GIS cluster and suitability modeling. The cluster analysis identifies the rice straw as the highest feedstock of crop residues and township-based high/low clusters. In addition, the electricity generation potential is estimated at 279.14 MW for different clusters of rice straw. Moreover, the suitability analysis in the study uses the conceptual model of variables for constraints and factors with the analytical hierarchy process (AHP) technique to evaluate the weights. The suitability analysis found high suitability areas of 14,603 hectares for treatment facilities within the high/low cluster of feedstock for rice straw. The multicriteria and GIS integrated assessment model adopted in this research can support the decision-makers in developing spatial-based strategic planning for bioenergy promotion which will support sustainable farming practices in Myanmar. Additionally, the proposed model is adaptable in study areas with similar feedstock.

The development of landslide susceptibility maps is of great importance due to rapid urbanization. The purpose of this study is to present a method to integrate the subjective weight with objective weight for regional landslide susceptibility mapping on the geographical information system (GIS) platform. The analytical hierarchy process (AHP), which is subjective, was employed to weight predictive factors’ contribution to landslide occurrence. The frequency ratio (FR) method, which is objective, was used to derive subclasses’ frequency ratio with respect to landslides that indicate the relative importance of a subclass within each predictive factor. A case study was carried out at Tsushima Island, Japan, using a historical inventory of 534 landslides and seven predictive factors: elevation, slope, aspect, terrain roughness index (TRI), lithology, land cover and mean annual precipitation (MAP). The landslide susceptibility index (LSI) was calculated using the weighted linear combination of factors’ weights and subclasses’ weights. The study area was classified into five susceptibility zones according to the LSI. In addition, the produced susceptibility map was compared with maps generated using the conventional FR and AHP method and validated using the relative landslide index (RLI). The validation result showed that the proposed method performed better than the conventional application of the FR method and AHP method. The obtained landslide susceptibility maps could serve as a scientific basis for urban planning and landslide hazard management.

In this study, performance and its basic concepts, efficiency, and productivity, are explained and performance measurement methods are discussed.The Analytical Hierarchy Process (AHP) was used to measure efficiency in multi-criteria problems and to determine the weights of criteria for efficiency measurement.With the help of the Data Envelopment Analysis (DEA), which is one of the best methods of measurement of efficiency, efficiency levels of the banks in Turkey, whose data can be fully reached, was evaluated. The criteria for bank efficiency were weighted with the AHP and then the efficiency scores of the banks were determined using the Weighted DEA method. Potential improvements have been proposed for inefficient banks.

Salt accumulation within the soil is one of the subtle ecological issues around the world. An integrated of remote sensing with different statistical techniques has indicated accomplishment for creating soil quality forecasting models. The objective of this research was to unveil the degree and location of the salt affected soils as it has a severe effect on the agricultural crop yield of the Gautam Buddha Nagar (GBN) district. To assess spatial variation of the salt-affected soil a simulation model integrating satellite observation data, artificial neural network (ANN) and multiple linear regression (MLR) was used. The statistical correlation amongst ground-truth data and Landsat original bands and band ratios showed that all the bands and ratios showed a non-significant correlation with SAR. While four optical bands and eleven band ratios showed high correlation with all the soil quality parameters. Combining all the remotely sensed variables into models resulted in the finest fit with the R value equal to 0.84, 0.69, 0.59 and 0.85 for EC, pH, ESP and TSS, respectively. The soil quality parameter maps generated using selected models revealed that most of the part of the agricultural land of the study area lies in the range of moderately saline and moderately sodic soil. Further Analytical Hierarchy Process (AHP) was applied to generate overall soil degradation probability map of the district, with respect to salt accumulation. The result revealed that the major portion of the entire agricultural field of the study area lie between low (32.74 %) to moderate (29.53 %) probability zones of salt susceptibility.

China has achieved the goal of building a moderately prosperous society in a well-rounded way by 2020. At this stage, effectively dealing with poverty and not returning to it has become the bottom-line task of rural revitalization. The purpose of this study is to construct a poverty-return early warning and evaluation system for X and Y counties in Guangxi. Based on the field survey data of 150 households from the questionnaire survey in X County and Y County of Guangxi Province, an early warning evaluation system for returning to poverty in the two counties of Guangxi Province is constructed. The AHP analytic hierarchy process is used to evaluate the early warning of returning to poverty for farmers. The BP neural network algorithm is used to verify the rationality of the method; the overall poverty relief situation in the two counties is stable and the living conditions are good. The early warning results are as follows: One household in X County has a severe early warning, six households have a slight early warning, and sixty-four households have no early warning; in Y County, six households had severe early warning, six households had mild early warning, and sixty-seven households had no early warning. For farmers, serious early warnings are mainly caused by the lack of labor force and low annual per capita net income, as well as the lack of the main means of livelihood and capacity. The characteristics of mild early warnings for farmers are mainly that the proportion of non-labor income is relatively high, and the farmers lack the ability and way of long-term development. Different suggestions are put forward for farmers with different early-warning levels, focusing on improving their viability and development ability.

The spread of invasive species (IS) has the potential to upset ecosystem balances. In extreme cases, this can hinder economical utilization of both aquatic (fisheries) and terrestrial (agricultural) systems. As a result, many countries regard risk assessment of IS as an important process for solving the problem of biological invasion. Yet, some IS are purposefully introduced for what is seen as their potential economic benefits. Thus, conducting IS risk assessments and then formulating policies based on scientific information will allow protocols to be developed that can reduce problems associated with IS incursions, whether occurring purposefully or not. However, the risk assessment methods currently adopted by most countries use qualitative or semiquantitative methodologies. Currently, there is a mismatch between qualitative and quantitative assessments. Moreover, most assessment systems are for terrestrial animals. What is needed is an assessment system for aquatic animals; however, those currently available are relatively rudimentary. To fill this gap, we used the analytic hierarchy process (AHP) to build a risk assessment model system for aquatic IS. Our AHP has four primary indexes, twelve secondary indexes, and sixty tertiary indexes. We used this AHP to conduct quantitative risk assessments on five aquatic animals that are typically introduced in China, which have distinct biological characteristics, specific introduction purposes, and can represent different types of aquatic animals. The assessment results show that the risk grade for Pterygoplichthys pardalis is high; the risk grade for Macrobrachium rosenbergii, Crassostrea gigas, and Trachemys scripta elegans is medium; and the grade risk for Ambystoma mexicanum is low. Risk assessment of the introduction of aquatic animals using our AHP is effective, and it provides support for the introduction and healthy breeding of aquatic animals. Thus, the AHP model can provide a basis for decision-making risk management concerning the introduction of species.