Multi-criteria Decision Analysis Approach Research Articles

Evaluation of pharmaceutical intervention strategies against pandemics in Sweden: A scenario‐driven multiple criteria decision analysis study

AbstractTo enhance preparedness for diverse pandemic situations we aim to predict the performance of various pharmaceutical intervention strategies. We gathered domain experts and ran a series of decision conferences where a scenario‐based multi‐criteria decision analysis (MCDA) model was interactively defined and implemented. Assuming an influenza pandemic, a micro simulation model was used to estimate societal health impact, a health‐economic model was used to estimate economic losses, and expert preferences were elicited to define trade‐offs between multiple criteria and synthesize various estimates. Sensitivity analysis to address various forms of uncertainty was also conducted. Nine intervention strategies, including the baseline “no interventions” strategy, were evaluated and ranked under five pandemic scenarios for Sweden's population. We conclude that a scenario‐based MCDA approach relying on multiple models for assessment of consequences is instrumental in defining robust interventions and support decision‐making at the pre‐pandemic and pandemic situations.

Modeling multi-criteria decision analysis in residential PV adoption

Multi-Criteria Decision Analysis (MCDA) is a sub-discipline of operations research that aims to solve multi-objective optimization problems by evaluating competing factors in decision-making. MCDA supports multidimensional decision-making processes through the analysis of diverse inputs at several levels of description, e.g. economic, technical, social, and environmental. The use of MCDA has been gaining momentum in the energy field, especially in endeavors that require evaluating the feasibility of different adoption scenarios for renewable energy technologies. The study presented in this paper investigates the combined use of multi-agent simulation, Bayesian modeling and sensitivity analysis for the development of a novel MCDA approach that supports the analysis of residential solar Photovoltaic (PV) adoption. The ensuing MCDA approach is evaluated alongside four popular MCDA methods (AHP, TOPSIS, SAW and ELECTREII) in a variety of tests aimed to assess overlap in criterion rankings and decision-making outcomes, covariation of criteria rankings in alternative scenarios, and the capacity to provide a model of correct decision-making. The comparative evaluation with AHP, TOPSIS, SAW and ELECTREII shows that overall ABM-BN-SA is well correlated with most of other MCDA methods and provides the best performing model of decision-making, with reference to the PV adoption use case under analysis. TOPSIS shows the closest fit with ABM-BN-SA, as expected since it used a ranking approach considerably closer to ABM-BN-SA as compared to the other MCDA treatments. ELECTREII yields the lowest degree of ranking overlap with ABM-BN-SA. All the reviewed methods have been illustrated and evaluated within our residential solar Photovoltaic (PV) adoption decision support system. In general, these methods enable a user to select an optimal solution out of a set of plausible alternatives according to multiple criteria and assist him in the design and exploration of the decision space. The ensuing decision-making methodology can be applied not only by Solar PV panel purchasers but also by stakeholders in other industries to logically and straightforwardly model and simulate the adoption decision-process of the public based on their individual preferences, behavioral rules, and interaction within a social network, with specific reference to a consumer utility function.

Analysis of core risk factors and potential policy options for sustainable supply chain: an MCDM analysis of Saudi Arabia's manufacturing industry.

Sustainable supply chain management (SSCM) has been a tough challenge for developing economies like Saudi Arabia. Implementation of SSCM practices in the manufacturing industry has been prone to multiple risk factors that need to be identified, evaluated, and prioritized especially considering the dynamics of the manufacturing industry in a developing economy. Moreover, it is also imperative to trace out feasible and sustainable strategies to overcome the risks to SSCM practices adoption. This study serves this purpose and identifies, evaluates, prioritizes the risk factors, sub-factors, and strategies to overcome these risk factors in the implementation of SSCM practices in the manufacturing industry in Saudi Arabia. An integrated multi-criteria decision analysis approach by combining fuzzy AHP and fuzzy WASPAS methods is employed for the analyses. The fuzzy AHP analysis results show that economic risks are dominant risks followed by the managerial policy risks and environmental risks in implementing SSCM. Industrial emissions are the leading risk factors in the overall ranking of the sustainable supply chain sub-risk factors followed by market dynamics, management policy failures, financial constraints, and credit uncertainty. While evaluating the sustainable supply chain strategies using fuzzy WASPAS, it is concluded that commitment and support of top, middle, and lower level management is the most pivotal strategy to deal with the risks to SSCM in Saudi Arabia followed by establishing environmental policies and goals to adopt SSCM, and provision of the financial resources and subsidies.

Carbon dioxide treatment method for autonomous underwater vehicles powered by direct methanol fuel cells: A multi-criteria decision analysis approach

Autonomous Underwater Vehicles are very valuable tools in a great variety of marine related sectors that demand new capacities and improvements. These improvements go through increasing their endurance and, in this sense, the use of Direct Methanol Fuel Cells can be very interesting with an adequate CO2 Treatment Method. This work investigates four CO2 Treatment Methods for their application onboard Autonomous Underwater Vehicles powered by a Direct Methanol Fuel Cell-based power plant. The evaluation was carried out considering different working conditions defined by the fuel cell power, navigation depth and endurance. To rank the treatment methods, three Multi Criteria Decision Methods and eight selection criteria were used. The results show that direct disposal of CO2 ejecting it outside the Autonomous Underwater Vehicle is the most adequate method for navigation depths up to 50 m. For deeper waters CO2 storage embedded in an adsorbent material and CO2 stored as a pressurized gas seem to be the best choices. In all cases, the results present a good stability against changes in the criteria weights vector.

Aquifer evaluation in parts of north-central Nigeria from geo-electrical derived parameters

In order to reduce the level of risk associated with borehole drilling, it is important to have detailed knowledge about the aquifer distribution. In a view to generating groundwater potential model of Lokoja and its environs, the detailed subsurface characterization was carried out using a GIS-based multi-criteria decision analysis approach. One hundred and twenty-four vertical electrical sounding (VES) data points were covered within the study area using the Schlumberger array of electrical resistivity surveys. Hydrogeological investigation of one hundred and twenty-four existing boreholes within the vicinity of the sounded VES points was carried out by measuring in situ parameters of each borehole such as borehole depth, elevation, static water level and borehole yield. A Multi-Criteria Decision Analysis-Analytical Hierarchy Process (MCDA)-AHP-based was carried out by estimating the probabilistic ratings for the classes of parameters used for modeling groundwater potential. Four groundwater potential influencing factors, namely coefficient of anisotropy, transverse resistance, aquifer resistivity and aquifer thickness, were classified and rated. The output of the multi-criteria decision analysis was processed in the GIS environment to produce a groundwater potential index map. The obtained model was validated by comparing it with in situ borehole yield data to determine the accuracy of the proposed model. The groundwater potential map generated classified the study area into low, medium and high yield zones. Areas with medium potential zones dominate the largest part of the with 66% area coverage, and the dominance of these zones was visible in the northern and western part of the study area. Areas with high groundwater potential exist toward the southern and eastern sections of the study area. This area was observed to be underlined with sandstone, siltstone and migmatite. The validation exercise carried out on the proposed model reveals a 70% prediction accuracy.

GIS-Fuzzy DEMATEL MCDA model in the evaluation of the areas for ecotourism development: A case study of “Uzundere”, Erzurum-Turkey

Ecotourism has become an integral part of the tourism sector for raising awareness of environmental protection, reducing the negative effects of tourism while using natural and cultural tourist attractions sustainably. The study primarily aims to determine suitable areas for sustainability approaches in the region regarding ecotourism and develop a comprehensive and reliable model in Uzundere. Various methodological approaches have been applied to develop ecotourism models, but certain deficiencies have emerged regarding spatial differences and criteria weighting. The proposed model applied the Geographic Information Systems and Fuzzy Decision Making Trial and Evaluation Laboratory Combined approaches such as Multi Criteria Decision Analysis approaches. The model was designed under 4 main groups and 16 sub-criteria. The ecotourism suitability map was analyzed through Weighted Linear Combination as highly suitable, suitable, moderately suitable and not suitable areas. Besides offering great opportunities in selecting suitable areas to be designated for ecotourism activities, this modeling provides advantages for accurate decision-making, planning, implementation and also conservation of the natural and socio-cultural environment. This study can facilitate the planning of the sustainable structure for ecotourism and the suitable areas determination in other areas with similar geographical conditions, with its applicable, reproducible and modifiable criteria structure.

High Voltage Overhead Powerlines Routing in Central Nigeria using GIS-MCDA Approach and Environmental Impact Factors

High voltage overhead transmission line route selection has become a vital component of the power transmission line project life cycle as demand for electricity has increased and global awareness of the environmental implications of large-scale power projects has grown. The project necessitates meticulous planning, engineering designs, facility installations, commissioning, and maintenance, as any errors made at this stage may have a flowing detrimental impact on the following phase, which may be too costly to fix. Using geographic information system (GIS), multi-criteria decision analysis approach, and environmental impact factors strategies, this study determines the best route from existing geographical space between Lafia 330/132/33kV Substation in Nasarawa State and New Apo (Pigba) 330/132/33/kV Substation in the Federal Capital Territory, Central Nigeria, Abuja. Land use, land cover, and other environmental data were obtained by performing image classification on Landsat 8 satellite image using ArcGIS 10.1 to achieve this goal. Three key terrain elements were identified to define the land cover types: built-up areas, vegetation, and water bodies. These layers were created to reveal the locations of significant settlements, geological formations, and the HV-OHTL route that avoided crossing protected areas. Using pair-wise comparison of analytic hierarchy process (AHP) approach, each criterion evaluated was assigned relative weight that shows their level of influence on the proposed power transmission line, and these were provided as GIS layers for visualizations. To build the cost surfaces assessed during route selection for HV-OHTL, weights derived from AHP method for each decision makers were applied to all layers to create the cost surfaces analyzed during route selection for HV-OHTL.

THE SICKLE CELL DISEASE PROFILE IN THE BRAZILIAN PUBLIC HEALTH SYSTEM: A MULTI-CRITERIA DECISION ANALYSIS APPROACH FOR PRIORITY SETTING

Diseases included in the Strategic Component of Pharmaceutical Services (SCPS) are illness with high socioeconomic impact or considered as strategic by the Brazilian Ministry of Health. This study aims to identify how different stakeholder groups prioritize sickle cell disease (SCD) among other three diseases included in SCPS. A literature review (LR) was carried out to form a criteria set. A one day online panel with specialists, patient representatives, and public payers (n = 3, each from all areas) was conducted to validate the final criteria set and to apply a multi-criteria decision analysis (MCDA) approach to assign an importance weight for each criterion. The PROMÉTHÉE II MCDA method was applied to compare four SCPS diseases (hemophilia, SCD, HIV and tuberculosis) considering criteria weighting and their performances on each criterion. LR and a retrospective analysis of Brazilian Ambulatory Information System (SIA/SUS) and Brazilian Hospital Information System (SIH/SUS) database (2019) were carried out to complete the diseases performance matrix. PROMÉTHÉE II generated the following outcomes for each stakeholders'group: a disease prioritization ranking, prioritization level from A (main level to prioritization) to E (worst level), and a criteria panel classifying each criterion as strong or weak point for each disease in relation to the others. The final criteria set included: disease rarity, mortality, morbidity, acute complications, quality of life impact, access to treatment, specific treatment, direct costs and indirect costs. For specialists, the main criteria were quality of life impact and morbidity (15.90% and 15.07% of the total weight, respectively), while acute complications and mortality were the main criteria for patient representatives (13.83% and 13.43%, respectively). Public payers defined mortality and morbidity as the most important criteria, both with a weight equal to 14.65%. For specialists, the prioritization should be tuberculosis; SCD; HIV; hemophilia. For patient representatives, the prioritization established was: SCD; tuberculosis; hemophilia; HIV. Public payers defined the following prioritization: tuberculosis; SCD; HIV; hemophilia. Specialists assigned B level for tuberculosis and SCD and C level for HIV and hemophilia. It was assigned B prioritization level for SCD and tuberculosis, and C level for hemophilia and HIV according to patient'representatives. Tuberculosis received B level and the other diseases received C level through public payers'preferences. The criteria panel classified as strong points to prioritize SCD over the others were specific treatment, access to treatment, morbidity, and disease rarity. The specialists and patient representatives prioritize tuberculosis and SCD over the other two diseases. The same prioritization level (B) was assigned for these diseases by these two stakeholder groups. Public payers prioritize tuberculosis (level B) over the other diseases. The criteria panel results reflected the absence of SCD specific treatment in Brazil. The MCDA approach for HTA process demonstrated that tuberculosis and SCD are the potential SCPS diseases to be prioritized of the 4 diseases in the Brazilian public healthcare system according to the three stakeholder groups preferences.

Geospatial and multi-criteria decision approach of groundwater potential zone identification in Cuma sub-basin, Southern Ethiopia

Groundwater is an essential component of our country's freshwater supplies. It plays a critical role in satisfying the water demands of the nation's many user sectors. The groundwater resource cannot be exploited and sustained optimally unless prospective zones are identified prior to the drilling of wells. The current study intended to analyze groundwater potential possibilities in the Cuma sub-basin, Omo Gibe basin, southern Ethiopia, utilizing geospatial and multi-criteria decision analysis approaches. For this purpose, a range of thematic layers like geology, Rainfall, drainage density, slope, land use and land cover (LULC), Soil type, faults density, saturated hydraulic conductivity, Available water capacity, and soil depth were organized for the study area. The different sub-criteria of each theme layer were rated according to their effect on groundwater recharge, and a weightage was assigned to each thematic layer based on the analytical hierarchy method (AHP). The identification of groundwater-potential regions of the sub-basin was one of the study's main results. The resulting groundwater potential zone map is further classified into five groundwater potential classes: very good (7.01%), good (19.49%), moderate (17.48%), poor (29.51%), and very poor (26.51%). The study's findings have important significance for developing sustainable groundwater strategies in the area.

Decision Support System Design Model for Choosing Effective Learning Method in Higher Education Institution

In an educational institution, especially higher education, producing qualified graduates is the highest priority achievement. The quality of the graduates is highly dependent on the applied learning method. However, sometimes universities have difficulty in determining appropriate learning for their students. Determination of this decision requires a tool that can facilitate decisionmakers in analyzing all the considerations in choosing decisions. Another challenge is when the decision is shared among multiple stakeholders with equal contribution. It consequently creates a need for a mechanism that can provide an equal contribution for each stakeholder. The research tries to create a model to design a tool that can determine the decisions that need to be taken. This model is built with the Multi-Criteria Decision Analysis (MCDA) approach. MCDA is selected because it can be implemented in a collaborative ecosystem where multiple stakeholders are involved. Then, a literature study is conducted to determine the attributes and decision-making parameters. In addition, the primary data derived from interviews to a total of 40 respondents consisting of students, lecturers, and staff are taken into consideration to complete the literature study. The literature review and interview output are then translated to some parameters that influence an effective learning system. The result shows that this model can be used as a reference in developing a web-based application as an implementation of MCDA.

Combining multi-criteria decision analysis with GIS approaches for decentralized organic wastes composting plants site selection in Tiassalé, Southern Côte d'Ivoire.

In recent years, decentralized composting appeared as one of the most appropriate treatment options for organic waste valorization in low- and middle-income countries. In Cote d'Ivoire, a pilot project has proved the feasibility of organic municipal solid waste composting for the city of Tiassalé. However, numerous issues still need to be addressed for the establishment of a sustainable decentralized composting system in this city. One of the key issues is site selection. Until now, there is no clear model for such plant site selection. In this study, multi-criteria decision analysis (MCDA) and geographical information system (GIS) approaches were combined to develop an appropriate model for selecting decentralized composting sites in the city of Tiassalé. The methodology used involved two different and complementary phases. First, MCDA and GIS techniques were used to identify the most suitable site areas. Seven criteria clustered in three main factors (environmental, social and economic), and five constraints were considered in the analysis process. Second, five sites were selected within the most suitable areas after a basic field visit and ranked using the Analytic Hierarchy Process. The results showed that the most suitable spaces for decentralized composting plant siting represent only 2.6% of the study area. The investigation yielded on the selection of the two best options for decentralized composting plant siting for the city of Tiassalé. This study proved that the combination of MCDA and GIS is a practical and efficient method to identify suitable sites for decentralized composting plants.

On the performance and strategy of innovation systems: A multicriteria group decision analysis approach

Mainstream approaches to analyzing innovation systems are based on statistical methods, use standard sets of indicators, and are typically deployed to measure and classify innovation performance. As they are not based on decision-theoretic approaches that involve the multiple agents that are usually embedded in innovation systems, conventional approaches are rather inadequate to generate the strategy and describe the dynamics of innovation systems. Taking a rather different approach that has been motivated by the need to generate the strategy of innovation systems, we set out a methodology that lies at the intersection of strategy, innovation and decision theory. The methodology construes the strategy of an innovation system as a process that emerges from the evaluations of agents and the aggregation logics used to arrive at a group decision. The methodology draws upon the TOPSIS multicriteria decision analysis method, uses the Mahalanobis metric of distance and applies the Borda count to aggregate the preferences of multiple agents who participate as strategists in the process of allocating resources based on a set of agreed-upon indicators. The resulting methodology can be used in concert with different innovation frameworks using sets of indicators that may show varying degrees of correlation in order to drive the strategy of innovation systems as a dynamic group decision-theoretic process.

Spatial multicriteria approach to support water resources management with multiple sources in semi-arid areas in Brazil

Semi-arid regions often face severe drought events that reduce agricultural and livestock production. In recent years, some international studies have used multicriteria decision analysis (MCDA) approaches combined with geographic information systems (GIS-MCDA) to support decision-makers in assessing the suitability of agricultural land for irrigation in semi-arid regions. Unlike previous studies, which have only considered a single source of water for crop irrigation, this study proposes a GIS-MCDA approach that considers all potentially available local water sources (e.g., groundwater, surface water, and wastewater) as possible alternatives for better multisource water resource management (MWRM) in regions facing water shortages. The geospatial multicriteria evaluation implemented in this study considers a series of technical, environmental, and agricultural productivity criteria using the analytical hierarchy process (AHP) method. Three independent baseline maps were generated, showing the spatial distribution of suitable areas for crop irrigation for each considered water source in the studied area. Surface water, groundwater, and wastewater offered suitable crop irrigation for 83%, 70%, and 26% of the study area, respectively. Overlapping these areas produced a final map showing all the feasible areas for each crop irrigation alternative at the same time. The MWRM approach considering all water sources increased the coverage of suitable areas to be irrigated in the study area by 2.2%, 20.4%, and more than 225% compared to considering surface water, groundwater, and wastewater, respectively, independently. The GIS-MCDA framework proposed in this study provides better support for decision-makers and stakeholders, favouring a reduction in possible conflicts over water scarcity, the diversification of irrigated crops, and an improvement in the quality-quantitative management of water resources in semi-arid regions.

Study of selected influential criteria on groundwater potential storage using geospatial technology and multi-criteria decision analysis (MCDA) approach: A case study

Analytical hierarchical process (AHP) is nowadays a very popular tool in multi-criteria decision analysis (MCDA) in the groundwater suitability zone assessment. The present study involves the selection of six major influencing criteria, i.e., land use and land cover, drainage density, normalized difference vegetation index, lineament density, groundwater fluctuation, and the slope. Using remotely sensed data in a GIS environment, various thematic layers are generated. Suitable weights are assigned to these criteria using Saaty’s scale as well as reclassification and ranking scores are also done to sub-criteria. Accordingly, consistency checks are also done to the weights applied to all the criteria. The weighted overlay technique is adopted under ArcGIS environment. Generated groundwater potential zone (GWPZ) map, illustrates five classified zones (1–5) of groundwater potentials, where 1 is representative of very poor prospects zone (10.9%), and 5 is indicative of excellent storage zones (37.8%).

Case studies for implementing MCDA for tender and purchasing decisions in hospitals in Indonesia and Thailand

BackgroundA multi-criteria decision analysis (MCDA) approach has been suggested for helping purchasers in low- and middle-income countries in an evidence-based assessment of multi-source pharmaceuticals to mitigate potential adverse consequences of price-based decisions on patient access to effective medicines. Six workshops for developing MCDA-instruments for purchasing were conducted in Indonesia, Kazakhstan, Thailand, and Kuwait in 2017–2020. In Indonesia and Thailand, two pilot-initiatives aimed to implement the instruments for hospital drug purchasing decisions.ObjectiveBy analysing and comparing the experiences and progress from the MCDA-workshops and the two case-examples for hospital implementation in Indonesia and Thailand, we aim to gain insights, which will support future implementation.MethodsThe selection of criteria and their average weight were compared quantitatively across the MCDA-instruments developed in all four countries and settings. Implementation experiences from two case-examples were studied, which included (1) testing the instrument across a variety of drugs in seven hospitals in Thailand and (2) implementation in one specialty hospital in Indonesia. Semi-structured interviews were conducted via web-conferences with four diverse stakeholders in the pilot implementation projects in Thailand and Indonesia. The open responses were evaluated through qualitative content analysis and synthesis using grounded theory coding.ResultsDrivers for implementation were making ‘better’ decisions, achieving transparency and a rational selection process, reducing drug shortages, and assuring consistent quality. Challenges were seen on the technical level (definition or of criteria, scoring methods, access to data) or change-related challenges (resistance, perception of increased workload, lack of competencies or capabilities, lack of resources). The comparison of the MCDA instruments revealed high similarity, but also clear need for local adaptations in each specific case.ConclusionA set a of measures targeting challenges related to utility, methodology, data requirements, capacity building and training as well as the broader societal impact can help to overcome challenges in the implementation. Careful planning of implementation and organizational change is recommended for ensuring commitment and fit to local context and culture. Designing a collaborative change program for each application of MCDA-based purchasing will enable healthcare stakeholders to maximally benefit in terms of quality and effectiveness of care and access for patients.

Multicriteria Decision Analysis for Renewable Energy Integration Design: For an Institutional Energy System Network

The hybridization of a renewable energy generator with an existing system in a specific building plays a vital role in solving many important problems of electrical energy systems such as reliability, cost-effective, and ecological option. In this perspective, a modified energy system is proposed in an existing building with integrated renewable energy using multicriteria decision analysis (MCDA). To achieve the sustainable energy solution, the hybrid power source configurations available in the locations are analyzed to ensure reliability using the HOMER software tool. Among these, the best configuration is identified by using the Best Worst Method (BWM) based MCDA approach. The study examined the technical, economic, and environmental perspectives for the achievement of the Sustainable Development Goals (SDGs) such as affordable, reliable, sustainable, and modern energy. The results of the survey in the building show that the best optimal hybrid configuration of the proposed location is the Gird-PV-Wind-based systems for the specific location based on the key techno-economic-environmental criteria of the SDGs.

Decision making in urban development: The application of a hybrid evaluation method for a critical area in the city of Turin (Italy)

Smart cities and sustainable neighbourhoods are increasingly gaining more attention within policy decisions on urban transformations. In this context, Decision-Makers (DMs) need evaluation methodologies able to support the definition of policies and actions for the future of cities, that focus on a higher degree of life quality and new needs. In this paper, an integrated framework is proposed which combines the Stakeholder Analysis, the STEEP and SWOT analyses (STEEP + SWOT), the Scenario Building and the Multicriteria Decision Analysis approach (MCDA) which can be used to envision sustainable future scenarios for an underdeveloped area in Northern Italy. The adoption of integrated methodologies facilitates the analysis of each phase of the decision problem, from its early stage to the selection of the most suitable scenario according to the context and the stakeholders engaged. With this perspective, the present research is a guiding tool for DMs for the design of transformation/regeneration scenarios with a long-term perspective of sustainable cities and neighbourhoods. From the results obtained, it is possible to build new and unexpected scenarios by combining the main opportunities and strengths of most suitable alternatives.

GIS-Based Multi-Criteria Decision Analysis Approach (GIS-MCDA) for investigating mass movements’ hazard susceptibility along the first section of the Algerian North-South Highway

The North-South highway is a central axis of the Algerian road network. This major project is expected to enhance the socio-economic development of Algeria. However, its first section is implemented within a complex region with very contrasted landscapes marked by mass movements. In the present work, we conducted a mass movement susceptibility assessment on the first section of the road infrastructure using a Multi-Criteria Decision Analysis approach. Various data from different sources were processed in a GIS environment, and nine landslide and rockfall conditioning factors were considered: Lithology, Slope, Daily Temperature Amplitude, Rainfall, Lineament Density, NDVI, Relative Relief, Drainage Density, and Distance to Streams. Each of the conditioning factors was selected, reclassified, and ranked according to its influence on each mass movement type. Two popular GIS-MCDA methods, namely AHP and WLC, were used to weight the influence and combine the selected factors and construct the landslide and rockfall assessment models. Then, landslide and rockfall susceptibility indices were calculated and used to produce mass movement susceptibility maps of the study area. Finally, a spatial analysis was performed to assess the highway section’s susceptibility to the two mass movements. The results show that 22.49% and 12.44% of the road section are highly susceptible to landslide and rockfall hazards, respectively. Finally, the combination of the resulting map with the road chainage enabled the delineation of segments highly susceptible to landslide and rockfall hazards by kilometric points.

Unraveling the Water-Energy-Food-Environment Nexus for Climate Change Adaptation in Iran: Urmia Lake Basin Case-Study

A holistic approach to the management of water, energy, food, and the environment is required to both meet the socioeconomic demands of the future as well as sustainable development of these limited resources. The Urmia Lake Basin has faced environmental, social, and economic challenges in recent years, and this situation is likely to worsen under the impacts of climate change. For this study, an adaptability analysis of this region is proposed for the 2040 horizon year. Two models, the water evaluation and planning (WEAP (Stockholm Environmental Institute, Stockholm, Sweden)) and the low emissions analysis platform (LEAP (Stockholm Environmental Institute, Boston, MA, USA)), are integrated to simulate changes in water, energy, food, and the environment over these 20 years. Two climate scenarios and nine policy scenarios are combined to assess sustainable development using a multi-criteria decision analysis (MCDA) approach. Results show that, through pursuing challenging goals in agricultural, potable water, energy, and industrial sectors, sustainable development will be achieved. In this scenario, the Lake Urmia water level will reach its ecological water level in 2040. However, social, technical, and political challenges are considered obstacles to implementing the goals of this scenario. In addition, industry growth and industry structure adjustment have the most impact on sustainable development achievement.

Optimization of rain gauge network using multi-criteria decision analysis and entropy approaches: case of Tekeze River basin, northwestern Ethiopia

Rainfall is an important hydro-meteorological variable that has various applications in water resource planning and development. Adequate rain gauge network gives immediate and precise rainfall data that are crucial for effective and economical water resource management. This study describes a multi-criteria decision analysis (MCDA) in combination with kriging and entropy methods to optimize rain gauge networks in the Tekeze River basin, northwestern Ethiopia. The Tekeze River basin is a trans-boundary river basin in the northwestern part of Ethiopia having a drainage area 86 510 km2. The MCDA was used for selecting suitable locations for additional rain gauges in the basin. The kriging and entropy methods were used to determine the optimal number of rain gauge stations in the basin. Initial analysis showed that the existing 63 rain gauge stations in the basin are not adequate and result in about 12% rainfall estimation error. Optimization of rain gauge network in the Tekeze River basin resulted in 216 additional gauging stations. The total optimal number of rain gauges for the Tekeze River would thus be 279. The results of the study revealed that the combined use of the MCDA, kriging, and entropy methods is useful to optimize the spatial distribution and number of rain gauge stations in a basin.