Geographic information system-assisted site quality assessment for hazelnut cultivation using multi-criteria decision analysis in the Black Sea region, Turkey.

Developing land suitability models for strategically critical agricultural products to expand sustainable agricultural policies and sensitive agriculture management has become a significant trend. This study aims to improve a unique land suitability model for hazelnut cultivation by applying the criteria set (7 main criteria, 35 sub-criteria) including qualitative and quantitative reasons, integrated fuzzy analytic hierarchy process, inverse distance weighting, multi-criteria decision analysis, geographic information system, and weighted linear combination approaches. The model developed in the present study was applied and tested in Ünye District of Ordu Province, where hazelnut production in the Eastern Black Sea region of Turkey is an important economic activity. While 71.17% of the study area is classified as very highly suitable, highly suitable, and moderately suitable, 28.83% of the study area has marginally suitable and unsuitable properties for hazelnut cultivation. Generally, it was determined that the coastal parts of the study area were the most suitable areas for hazelnut growing. The hazelnut land suitability model's two main criteria impacting the final score values are climatic and topographic conditions, respectively. Heavy metal pollution and physical, chemical, and fertility conditions related to soil properties followed these, respectively. The first ten sub-criteria with the highest weight value were determined as elevation, annual average temperature, annual average precipitation, aspect, annual average relative humidity, nickel (pollution), slope, annual average maximum temperature, lead (pollution), and soil depth, respectively. Existing hazelnut cultivation areas were used to test the model. Of the existing cultivation areas, 75.59% coincided with the very highly suitable, highly suitable, and moderately suitable classes presented in this study, while 17.15% were in marginally suitable and 7.26% in unsuitable classes. The study results reveal that the hazelnut land suitability model developed is suitable in mild climate conditions. Using this model as a general transition model will be beneficial to test it in areas containing similar climatic conditions and various soil properties. This study will create a rational background in ensuring the sustainable food production system and security, agricultural land use planning, strategic planning and management of the hazelnut plant, increasing agricultural productivity and income, and the ecosystem.

Evaluation of criteria and COVID-19 patients for intensive care unit admission in the era of pandemic: A multi-criteria decision making approach.

BACKGROUND AND OBJECTIVE: The COVID-19 pandemic results in an intense flow of patients to hospitals especially to the intensive care units (ICUs) to be treated. The ICUs will therefore be confronted with a massive influx of patients (e.g. Spain and Italy). However, if the number of patients is higher than the resources available in ICUs, rationing decisions such as determining and evaluating the criteria for ICU admission becomes essential. In this case, the decision of which patients will be admitted to the ICUs may put significant pressure on healthcare personnel. The goal of this paper is to determine the criteria to be used in the decision of admission of COVID-19 patients to the ICUs. METHODS: A three-step methodology is applied. In the first step, the evaluation criteria are determined, and then the criteria are prioritized using a fuzzy analytical hierarchy process (AHP) in an uncertain and multiple-criteria environment choice. Finally, COVID-19 patients are ranked using the Multi-Objective Optimization Method by Ratio Analysis to find out which patient is more urgent. RESULTS: According to experts' evaluation of ICU admission criteria, "increment of >2 in SOFA score" seems the most dominant factor among others. The proposed methodology is tested on 10 anonymous COVID-19 positive patients being treated in a public hospital and the ICU admission results are discussed. CONCLUSIONS: Obtained priorities and ranking is in line with the hospitals' behavior that potentially depicts the usefulness and validity of the proposed approach.

Defining business model key performance indicators using intentional linguistic summaries.

To sustain competitiveness in contemporary, fast-paced markets, organizations increasingly focus on innovating their business models to enhance current value propositions or to explore novel sources of value creation. However, business model innovation is a complex task, characterized by shifting characteristics in terms of uncertainty, data availability and its impact on decision making. To cope with such challenges, business model evaluation is advocated to make sense of novel business models and to support decision making. Key performance indicators (KPIs) are frequently used in business model evaluation to structure the performance assessment of these models and to evaluate their strategic implications, in turn aiding business model decision making. However, given the shifting characteristics of the innovation process, the application and effectiveness of KPIs depend significantly on how such KPIs are defined. The techniques proposed in the existing literature typically generate or use quantitatively oriented KPIs, which are not well-suited for the early phases of the business model innovation process. Therefore, following a design science research methodology, we have developed a novel method for defining business model KPIs, taking into account the characteristics of the innovation process, offering holistic support toward decision making. Building on theory on linguistic summarization, we use a set of structured templates to define qualitative KPIs that are suitable to support early-phase decision making. In addition, we show how these KPIs can be gradually quantified to support later phases of the innovation process. We have evaluated our method by applying it in two real-life business cases, interviewing 13 industry experts to assess its utility.

Site suitability analysis for potential agricultural land with spatial fuzzy multi-criteria decision analysis in regional scale under semi-arid terrestrial ecosystem.

The main purpose of this study is to identify suitable potential areas for agricultural activities in the semi-arid terrestrial ecosystem in the Central Anatolia Region. MCDA was performed in fuzzy environment integrated with GIS techniques and different geostatistical interpolation models, which was chosen as the basis for the present study. A total of nine criteria were used, as four terrain properties and five soil features to identify potential sites suitable for agriculture lands in Central Anatolia which covers approximately 195,012.7 km2. In order to assign weighting value for each criterion, FAHP approach was used to make sufficiently sensitive levels of importance of the criteria. DEM with 10 m pixel resolution used to determine the height and slope characteristics, digital geology and soil maps, CORINE land use/land cover, long-term meteorological data, and 4517 soil samples taken from the study area were used. It was identified that approximately 30.7% of the total area (59,921.8 ha) is very suitable and suitable for potential agriculture activities on S1 and S2 levels, 42.7% of the area is not suitable for agricultural uses, and only 27% of the area is marginally suitable for agricultural activities. Besides, it was identified that 34.8% of the area is slightly suitable.

Evaluation of landfill sites using GIS-based MCDA with hesitant fuzzy linguistic term sets.

There are many criteria to be considered in environmental, social, and economic issues for the landfill site selection (LSS). Multi-criteria decision analysis (MCDA) methods are often used to solve complex decision-making problems such as LSS. However, decision-makers (DMs) may hesitate during the evaluation of the landfill sites with possible incorrect evaluation concerns. Therefore, the inclusion of the hesitant fuzzy linguistic term sets (HFLTS), which considers the hesitations in the preferences of DMs, is suitable for the solution of the problem. On the other hand, geographic information systems (GIS) is an important decision support tool that can analyze different types of spatial data. The aim of this study is to evaluate landfill sites. To do so, the applied approach includes the processes of identifying appropriate alternative sites for landfills by combining HFLTS-based MCDA method and GIS and evaluation of alternative sites with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The applicability of the proposed approach is tested on Samsun city, Turkey. As a result, scenario analysis, which is dominated by environmental criteria, provides better results dominated by social-economic criteria. Consequently, 12 alternative locations are selected and evaluated for the LSS. Atakum and Canik districts of Samsun city are determined as very suitable locations for landfill sites.

GIS-based MCDM modeling for landfill site suitability analysis: A comprehensive review of the literature.

One of the cheapest and proper methods for the ultimate disposal of Municipal Solid Waste (MSW) is landfilling. However, determining the location of landfill sites is a difficult and complex task due to depending on social, environmental, technical, economic, and legal factors. To solve the aforementioned challenges related to the landfill site suitability analysis, the combinations of Geographic Information System (GIS) and Multi-Criteria Decision-Making (MCDM) have been studied by academia and applied by experts over the years. This notice is apparent by the large number of academic papers which have been announced in the near future. To provide a framework of the existing literature, and to guide colleagues, a state-of-the-art of recent papers is crucial. The goal of this study is to review all scientific papers in GIS-based MCDM modeling for landfill site suitability analysis in academic journals. A total of 106 studies published between 2005 and 2019 are recorded and surveyed. The studies are then investigated and classified by a generated taxonomy including following categories: GIS software, application area, uncertainty, MCDM techniques, cell sizes in GIS, and criteria. Based on the review conducted, it is observed that while Analytical Hierarchy Process (AHP) and Weighted Linear Combination (WLC) are the most widely used MCDM methods for weighting the criteria and ranking the alternatives, respectively. On the other hand, while environmental dimension is the most commonly preferred main criteria, surface water comes first in the sub-criteria pool. Criteria analysis shows that surface and ground water, geology, land use, distance to fault zone, distance to urban areas, and distance to road and slope are the most commonly used criteria groups among others. These classifications and observations are helpful for identifying research gaps in the current literature and provide insights for future modeling and research efforts in the field.