Multi-criteria Approach Research Articles

Evaluation of Climate Suitability for Maize Production in Poland under Climate Change

Climatic conditions are the main factor influencing the suitability of agricultural land for crop production. Therefore, the evaluation of climate change impact on crop suitability using the best possible methods and data is needed for successful agricultural climate change adaptation. This study presents the application of a multi-criteria evaluation approach to assess climate suitability for maize production in Poland, for a baseline period (BL, 1981–2010) and two future periods 2041–2070 (2050s) and 2071–2100 (2080s) under two RCP (Representative Concentration Pathways) scenarios: RCP4.5 and RCP8.5. The analyses incorporated expert knowledge using the Analytical Hierarchy Process (AHP) into the evaluation of criteria weights. The results showed that maturity and frost stress were the most limiting factors in assessing the climatic suitability of maize cultivation in Poland, with 30% and 11% of Poland classified as marginally suitable or not suitable for maize cultivation, respectively. In the future climate, the area limited by maturity and frost stress factors is projected to decrease, while the area of water stress and heat stress is projected to increase. For 2050 climate projections, water stress limitation areas occupy 7% and 8% of Poland for RCP4.5 and RCP8.5, respectively, while for 2080 projections, the same areas occupy 12% and 32% of the country, respectively. By 2080, heat stress will become a limiting factor for maize cultivation; according to our analysis, 3% of the Polish area under RCP8.5 will be marginally suitable for maize cultivation because of heat stress. The overall analyses showed that most of Poland in the BL climate is in the high suitability class (62%) and 38% is moderately suitable for maize cultivation. This situation will improves until 2050, but will worsen in the 2080s under the RCP8.5 scenario. Under RCP8.5, by the end of the century (2080s), the highly suitable area will decrease to 47% and the moderately suitable area will increase to 53%.

Accurate prediction of electric vehicles mileage for proactive battery management in varied conditions using multi-criteria approach

This research addresses several critical challenges in managing battery aging within Electric Vehicles (EVs). Key challenges include minimizing capacity degradation while maximizing vehicle performance and accurately predicting State of Charge (SoC) under diverse operational conditions. Additionally, integrating factors such as vehicle and battery age, driving cycles, environmental conditions, and regional climate variations posed significant hurdles in achieving comprehensive battery management. To tackle these challenges, the research presents a novel framework integrating an advanced Multihead cross attention-based optimizer with a bidirectional long short-term memory network, further enhanced by the coati optimization algorithm. This innovative approach aims to improve prediction accuracy for critical battery performance metrics, such as SoC and battery lifetime. Implemented and rigorously evaluated using Python, the framework demonstrates a substantial 20% increase in battery performance through detailed SoC and temperature analysis, coupled with an impressive 40% enhancement in battery lifetime. These results highlight the ability to overcome previous challenges and provide robust solutions for effective battery management in real-world EV applications.

Risk identification and mitigation among rice farmers in Pakistan: a fuzzy-AHP multi-criteria decision-making approach

Rice farming in developing countries contributes significantly to economic growth, yet it confronts diverse risks. Strengthening its resilience necessitates systematically identifying and prioritizing these risks, allowing for optimized resource allocation. The majority of published literature, however, focuses primarily on individual risks rather than comparing or ranking them according to their importance. This regime needs to be more helpful for managers and policy-makers in achieving effective management. As a result, frequent management failures are causing substantial economic losses and threatening food security in the most populous regions of the world. This study addresses these existing research gaps by systematically identifying and prioritizing these risks, aligning with the United Nations Sustainable Development Goal (SDG) 2, viz., Zero Hunger, ultimately contributing to enhanced risk management and developing countries’ rice farming sector resilience. Data was obtained from Pakistan through a purposefully designed questionnaire and sourced from a pool of 412 respondents representing a spectrum of stakeholders selected by the snowball technique. Multi-criteria decision analysis (MCDA) was employed to deconstruct and comprehend the data statistically. A key observation is the recognition of the main risks within rice farming, notably revolving around resource-related and external environmental risks. These risks emphasize the critical management of sub-risks of water management and pest and disease. This study also identified several risk sub-factors that are critical but have yet to be extensively discussed in the literature. Furthermore, survey respondents expressed low levels of risk perception and suggested strengthening the management system through policy reforms. To enhance the resilience of rice farming, stakeholders must engage in effective risk communication, capacity building, and policy implementation. Therefore, more comprehensive integrated risk management interventions are urgently needed to address rice farming risks for achieving SDG 2.

Incorporating aquatic biodiversity into sustainability index for hydrographic sub-basins: a multi-criteria stakeholder assessment approach in the Upper Paraná River

Sustainable development assessments in hydrographic sub-basins are powerful tools used to guide stakeholders. However, there remains a gap in incorporating new indicators (e.g., biodiversity indicators) into sustainability indexes, which have generally underestimated the environmental dimension. We propose an update to these indexes by incorporating biodiversity data on hydrographic sub-basins. Our goal was to introduce a new ecological sustainability index for hydrographic sub-basins (ESI-sb) that utilizes aquatic ecological indicators. We selected 16 indicators and aggregated them into four sustainability dimensions: economic, social, eco-environmental and negative environmental. We used a multi-criteria analysis based on the analysis hierarchy process (AHP) method to catch stakeholders' importance level of each indicator. The ESI-sb was calculated using the weighted average between the sub-indexes that represent the dimensions. Finally, we applied Gi* statistics to identify the ecological sustainability hotspots and coldspots regions, and the results showed in the Serra do Mar region, the southern region, and the Federal District had high ecological sustainability values; meanwhile, the hydrographic sub-basins that formed the coldspot clusters were located in the western region of the Upper Paraná River basin. The incorporation of aquatic biodiversity data into the index represents an innovation in sustainability indexes recently proposed to hydrographic sub-basins.Graphical abstract

Multi-Criteria Decision-Making Approach for Pre-Synthesis Selection of the Optimal Physicochemical Properties of TiO2 Photocatalytic Nanoparticles for Biomedical and Environmental Applications.

Nanomaterials are widely used in several biomedical and environmental applications, due to their ideal properties. However, the synthetic and characterization procedure requires significant costs and has a negative environmental impact. Various methods are available in order to control the pre-synthesis design of the produced materials, predicting their behavior and minimizing the series of experiments. Multi-Criteria Decision-Making is proposed in this study in order to determine the best combination of the physicochemical parameters and to define the best alternative among fifteen different samples of nanostructured titanium dioxide. In particular, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was applied to achieve a final ranking of the available alternatives by avoiding several of the trials that would follow testing the biological effect and the photocatalytic degradation of organic pollutants. Thus, this approach helps us to stay environmentally and ethically correct, saving time, money, and energy and also providing an optimization of the nanomaterials that are developed.

Life cycle sustainability assessment of waste-to-electricity plants for 2030 power generation development scenarios in western Lombok, Indonesia under multi-criteria decision-making approach

Globally, the dual challenges of power shortages and waste management underscore the critical need for innovative and sustainable energy solutions. Addressing these issues is essential for achieving environmental sustainability, economic development, and social well-being. This paper proposes an integrated life cycle sustainability assessment (LCSA) of waste-to-electricity (WtE) technologies for the future energy mix in Western Lombok, Indonesia which strongly faces power shortage and waste management challenges. The proposed LCSA assesses three WtE technologies, including gasification, incineration, and landfill gas (LFG) with 5 environmental, 3 social, and 4 economic indicators to estimate the final sustainability score under a multi-criteria decision-making framework. Also, a comparative analysis from the LCSA perspective for different WtE technologies, along with coal, and diesel power plants is conducted as a decision support tool in the 2030 power capacity development scenarios. To indicate the effectiveness and flexibility of the proposed model, the robust decision-making analysis using different important weights on sustainability aspects is investigated. Results show LFG is the most sustainable technology among all options scoring around 0.78. However, coal with a levelized cost of 0.42 $/MWh, and diesel with a payback period of 1.75 years are much more economical options for western Lombok. Sensitivity analysis proves that electricity price is a key parameter strongly affecting the final sustainability score. If supportive policies are adopted associated with electricity produced from WtE, this technology can experience a 16.2 % increase, making it an economic option for Lombok in the future.

Evaluating the Barriers to the Transition to Net-Zero Emissions in Developing Countries: A Multi-Criteria Decision-Making Approach

Many countries have engaged in net-zero emissions achievement by 2050 in line with United Nations Sustainable Development Goals (SDGs) and The Paris Agreement, aiming to foster sustainable social and economic practices while reducing emissions. Some countries have devised sector-specific plans to achieve net-zero emissions, aiming to balance energy consumption and production with current energy sources to meet SDGs. However, these plans will encounter challenges, particularly in reducing reliance on non-renewable fuels and reducing existing emissions. This study, employing the FullEX approach, identifies and analyses five barriers to the transition to net-zero emissions in Africa. The findings revealed financial constraints and ineffective policy and regulatory frameworks as the most critical barriers to address. Sensitivity analysis was applied to show the robustness of our approach.

A spatial ranking of optimal sites for solar-driven green hydrogen production using GIS and multi-criteria decision-making approach: A case of Tunisia

The unpredictable nature of renewable energy sources, such as solar and wind, raises concerns about consistent power supply based on a single type. Green hydrogen presents a promising solution to mitigate the environmental effects of fossil fuels and control the unpredictability of renewable energy sources. Yet, developing green hydrogen systems mandates immense financial commitments, vitally stressing the importance of determining the most appropriate locations before constructing them. As such, the primary purpose of this paper was to propose an initial spatial analysis and prioritize the most suitable locations for installing solar-based green hydrogen systems in Tunisia. For this reason, an exhaustive literature survey was conducted to develop a GIS-based MCDM approach, taking into account technical, topography, environmental, and accessibility criteria. Then, EDAS, ARAS, MOORA, and COPRAS techniques were used to rank the most promising sites resulting from the previous stage. The study revealed that sites of high suitability, primarily in the southeastern and southwestern parts of Tunisia, spanned a total area of 1591 km2 (0.78%). Among such regions, Sfax, Monastir, and Sousse appeared to hold the top ranking as the most suitable locations for solar-based hydrogen. The “most suitable” sites had a hydrogen potential capacity of 9910 Mt per year, with Sfax, Monastir, and Sousse accounting for 14.5% of that capacity. By tapping into this immense potential and embracing the green hydrogen industry, Tunisia has the opportunity to lead in renewable energy adoption. This move could help ensure energy security, reduce dependence on imports, stimulate economic growth, and steer the nation toward a more sustainable and prosperous future.

A Critical Review on Metaheuristic Algorithms based Multi-Criteria Decision-Making Approaches and Applications

A Critical Review on Metaheuristic Algorithms based Multi-Criteria Decision-Making Approaches and Applications

Multi-criteria decision-making approach in assessing the key barriers to the adoption and use of SWH in West Africa–Combination of modified Delphi and Fuzzy AHP

Africa faces a significant energy crisis, with over 600 million people lacking access to affordable and reliable power to meet their energy demands, despite the continent's vast solar energy potential. Several studies have indicated the high potential of solar water heaters (SWH) in various African countries in Africa due to the enormous solar energy resource. However, the diffusion and use of the same in Africa, especially in the West African sub-region, have been very slow compared to other sub-regions. This study thus assesses the barriers that affect the adoption and use of SWH systems using the Delphi method and the Fuzzy Analytical Hierarchy Process to prioritize and rank them for decision-making. The study identified five main barriers and prioritized them in the order of financing, gender, and market-related barriers (51 %), political, policy, and regulatory barriers (19 %), technical barriers (14 %), socio-cultural and public awareness barriers (11 %), as well as lack of standardization and quality assurance (6 %). The high cost of finance as well as difficulties in accessing finance prevent private sector investment, which ultimately affects the large-scale use of the SWH system in the sub-region. Also, the uneducated and a section of the educated do not fully appreciate the merits of the SWH system. This paper is expected to inform academics, practitioners, and regulators on future efforts to adopt for the SWH technologies in Africa, and to improve policies and strategies for their adoption across the continent.

Integrated decision support framework for construction land reduction projects prioritization in China: a multi-criteria decision analysis approach

Integrated decision support framework for construction land reduction projects prioritization in China: a multi-criteria decision analysis approach

Flood Susceptibility Mapping of Greater Hyderabad Municipal Corporation Region of Telangana State, India: A Multi Criteria Approach using GIS and AHP

Flood Susceptibility Mapping of Greater Hyderabad Municipal Corporation Region of Telangana State, India: A Multi Criteria Approach using GIS and AHP

Balancing Sustainability and Decision Maker Preferences in Regional Development Location Selection: A Multi-criteria Approach Using AHP and Fuzzy Goal Programming

Balancing Sustainability and Decision Maker Preferences in Regional Development Location Selection: A Multi-criteria Approach Using AHP and Fuzzy Goal Programming

The economic and environmental assessment of alternative marine fuels and nuclear energy utilization on a floating power plant.

The paper aims to investigate the fuel and system options for a floating power plant (FPP) considering economic performance and the decarbonization goals of the International Maritime Organization. Various case studies have been assessed using a reference FPP, encompassing the instant and future retrofitting scenarios. The ready-to-use scenarios involve alternative fuel and organic Rankine cycle-based waste heat recovery system usage. Nuclear energy systems have been evaluated within the reference FPP since they are suitable candidates for achieving zero-carbon objectives and providing low-cost electricity. A simulation framework created in Python has calculated the fuel consumption regarding the power requirement and organized the approaches used in the study. An environmental model comparing the systems has been built to calculate upstream and operational emissions. The cost projection model for 2030 and 2050 has assessed the economic performance. Technique for Order of Preference by Similarity (TOPSIS) one of the multi-criteria decision-making approaches has ranked the systems considering the outcomes of economic and environmental models over the years. Findings demonstrate that the current fuel usage scenario of the FPP is not suitable both environmentally and economically. The other emissions can be near zero and greenhouse gases can be decreased by up to 15.95% using alternative fuels. Nuclear energy is a strong candidate to meet the 2050 targets, but its viability is largely based on economic performance.

Geotechnical Assessment of Seismic Vulnerability in the Built Environment of Fako Division, South West Region of Cameroon

Introduction: Volcanic earthquakes may trigger severe hazards with considerable impact on the natural and built environment and the surrounding population. Aim: Evaluate the geotechnical characteristics of soils in Fako division to estimate their seismic vulnerability and recommend suitable stabilization techniques to enhance the safety and resilience of built environments. Place and Duration of Study: This study was conducted over a period of 12 months beginning from June 2022 to May 2023. Methodology: It employed a multi-criteria geotechnical approach, involving soil sampling, laboratory analysis, and seismic risk analysis. Soil samples collected from various locations within the study area, were subjected to laboratory tests to determine their plasticity (Atterberg limits) and load-bearing capacity (California Bearing Ratio - CBR). Historical data analysis and seismic hazard mapping were used to assess seismic vulnerability which helped to identify highly susceptible and unstable areas that may require engineered interventions. Results: The findings revealed significant variations in soil plasticity and load-bearing capacity, correlating with observed settlement and instability issues in the region. Historical seismic events, highlighted significant structural damage in areas with high plasticity and low CBR values, underscoring the necessity for robust soil stabilization measures (geotechnical interventions) to mitigate seismic risks and enhance the resilience and safety of structures, reducing the risk of settlement and instability within the Mount Cameroon area, particularly Fako Division.

Identifying managed aquifer recharge and rain water harvesting sites and structures for storing non-conventional water using GIS-based multi-criteria decision analysis approach

In arid climates, conventional water resources are severely limited and stressed in the face of rapid population growth and future climate change. So, it is necessary to find alternative non-conventional water resources for use in drought situations. Additionally, the non-conventional water resources in these areas are not sufficient to meet future water demand. Therefore, non-conventional water resources can be adopted as a strategic reserve to bridge the gap between water supply and demand in case of emergency and drought events. These resources might include rainwater harvesting, treated wastewater, and desalinated seawater. Managed aquifer recharge (MAR) can be applied to store these resources in the hydrogeological system using Geo information System—Multi Criteria Decision Analysis (GIS-MCDA) approach for determining the suitable MAR location for storage. North-west Kingdom of Saudi Arabia area was chosen for this study because it is extremely arid, has high potential for social and economic development, and it has newly constructed non-conventional water infrastructures distributed throughout the area including water desalination plants, Tertiary Sewage Effluent (TSE) waste water plants, and flash-flood storage dams. To identify the suitable MAR site location and structure, different data related to aquifer hydrogeology, surface hydrology, hydrometeorology, and water quality were applied. Then, GIS-MCDA holistic approach was applied with aid of ordered weighting average (OWA) technique. Finally, two maps were created to show the MAR location and structure type. Potential map indicates that ~ 18.85% of the area is suitable for MAR installations. About 0.17% of the total area exhibited very high potential, where infiltration ponds can be applied, 1.86% had high potential for construction of check dams with diversion channels, and 16.82% had moderate potential for installation of recharge wells. Additionally, 56 MAR structures were proposed and a map showing their locations has been created. Thus, results indicated that the study area is promising for MAR installation. These maps could aid the decision makers to propose a sustainable development plan for the future water resources of the area.

Land-use suitability assessment for urban development using Multi-Criteria Decision-Making Analysis in the Himalayan districts of Shimla, Nainital, and Darjeeling, India

After India gained independence in 1947, notable advancements in healthcare and socio-economic sectors resulted in a remarkable population surge, consequently driving substantial expansion in built-up areas throughout the country. The consequences of the rapid and unplanned expansion are adverse in the Himalayas where earthquake, landslides, flash floods are frequent. The already established hill stations saw a rapid surge in tourism and subsequent urbanization. This study delves into assessing the urban suitability of such hill stations namely Nainital, Shimla, and Darjeeling. The surge in tourism led to alarming population growth with haphazard urbanization which is often severely affected by growing natural hazards. The aim of this study is to assess the urbanization suitability of the regions based on multiple factors that affects the choice of suitable place for urban growth. We employed the GIS based Multi-Criteria Decision-Making Approach (MCDMA) considering geological, socio-economical, ecological, and prohibitive factors using the Analytical Hierarchy Process (AHP) method. The obtained land-use suitability maps for urbanization show that in Nainital the most suitable area for urbanization is the foothills of the Himalayas whereas in Shimla it is around the central and southern part. The northern part and the eastern boundary of Darjeeling are the most suitable area for urbanization. Upon comparing the already existing built-up areas with the AHP derived site suitability results we found that the Darjeeling district is most vulnerable as almost half of the built-up area (49%) lies within a very low to moderate suitable zone. Our findings show that Darjeeling has the highest amount of suitable land for urbanization as the bare ground in the region is not suitable for agriculture. Interestingly, the farmlands in Nainital show highest suitability when urbanization is considered. This brings out a serious problem in urbanization trend and it is observed throughout the world. Thus, more careful research is needed ensuring proper land use and food security for the growing population. This study lays a foundational understanding of challenges associated to urbanization in a tectonically active mountain range.

Multi-Criteria Decision-Making for Selecting Solar Window Film Sheets for Energy Saving in Buildings

Recently, there have been several advancements in the field of sustainable energy solutions, particularly in the selection of solar window film sheets. In this research, a multi-criteria decision-making approach was applied to compare three different types of window film sheets, Silver 35, TrueVue 15, and Sterling 40, to aid in selecting the most suitable window film based on the United Arab Emirates market. The primary aim of this work is to provide decision-makers with a structured approach to enhance their choices for selecting window film sheets. The methodology employed involves evaluating various criteria, including visible light transmittance, solar energy rejected, energy transmittance, energy absorptance, cost, glare reduction, visible light reflectance interior, and fade reduction. These criteria are assessed using the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results demonstrate that Sterling 40 is the best choice followed by Silver 35. Based on the final TOPSIS results, the difference between the scores of these two window film sheets was not significant, while they were far from the score of TrueVue 15.

Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making

Skin cancer is a type of cancer disease in which abnormal alterations in skin characteristics can be detected. It can be treated if it is detected early. Many artificial intelligence-based models have been developed for skin cancer detection and classification. Considering the development of numerous models according to various scenarios and selecting the optimum model was rarely considered in previous works. This study aimed to develop various models for skin cancer classification and select the optimum model. Convolutional neural networks (CNNs) in the form of AlexNet, Inception V3, MobileNet V2, and ResNet 50 were used for feature extraction. Feature reduction was carried out using two algorithms of the grey wolf optimizer (GWO) in addition to using the original features. Skin cancer images were classified into four classes based on six machine learning (ML) classifiers. As a result, 51 models were developed with different combinations of CNN algorithms, without GWO algorithms, with two GWO algorithms, and with six ML classifiers. To select the optimum model with the best results, the multicriteria decision-making approach was utilized to rank the alternatives by perimeter similarity (RAPS). Model training and testing were conducted using the International Skin Imaging Collaboration (ISIC) 2017 dataset. Based on nine evaluation metrics and according to the RAPS method, the AlexNet algorithm with a classical GWO yielded the optimum model, achieving a classification accuracy of 94.5%. This work presents the first study on benchmarking skin cancer classification with many models. Feature reduction not only reduces the time spent on training but also improves classification accuracy. The RAPS method has proven its robustness in the problem of selecting the best model for skin cancer classification.

Evaluation of energy economic optimization models using multi-criteria decision-making approach

Achieving high performance in energy systems is crucial for sustainability. Energy economy optimization (EEO) models offer transparent analysis for energy policy decision-making. However, evaluating and benchmarking these models is a complex multicriteria decision making (MCDM) problem. Challenges include multiple criteria, data variation, and the importance of diverse criteria. This study develops an integrated MCDM approach to evaluate and benchmark EEO models. The methodology involves three phases. First, 12 commonly used EEO models and five evaluation criteria (software licenses, public source code, redistribution, public source data, and commercial software) are identified to create an evaluation decision matrix. Second, the fuzzy-weighted zero-consistency method (FWZIC) is used to evaluate and assign weights to the criteria. These weights are utilized in the benchmarking phase. Third, individual and group fuzzy decision by opinion score method (FDOSM) techniques are integrated to benchmark the EEO models based on the weights acquired. The FWZIC weighting reveals that the public source code criterion has the highest weight (0.3347), while redistribution has the lowest weight (0.1021). The group FDOSM results show that the OSeMOSYS model ranks first with the highest score (0.1595), while the DNE21+, MARIA, and MESSAGE models have the lowest score (0.0646), ranking them last. Systematic ranking, sensitivity ranking, and comparative analysis verify the proposed evaluation and benchmarking framework.