Profile URL: https://hdl.handle.net/20.500.13091/11810
Job Title:Doç. Dr.
Email Address:fsari@ktun.edu.tr
Main Affiliation:02.08. Department of Geomatic Engineering
Status: Current Staff
ORCID:
0000-0001-8674-9028
0000-0001-8674-9028Scopus ID:
14424453600
14424453600YÖK Akademik: AA6E8F3575AB2DBE
Google Scholar:
sUC1DkwAAAAJ
sUC1DkwAAAAJWeb of Science ID:
ABI-5262-2020
ABI-5262-2020Name Variants:
Sarı, F. Sari, Fatih
24 results
Scholarly Output Search Results
Now showing 1 - 10 of 24
Other Exploration of Potential Geothermal Fields Using Maxent and Ahp: Case Study the Büyük Menderes Graben(2023) Yalçın, Mustafa Alp; Yıldız, Ahmet; Sarı, FatihArticle Natural Disaster Risk Assessments for Pine Honey Apiaries in Muğla, Turkey(2022) Sarı, FatihSince Muğla province has 90% of the world's total pine honey production, ensuring efficiency and economic income requires the determination of measures for apiary locations and estimation of risks. However, ensuring development and productivity requires identifying natural disasters susceptibility such as forest fires and floods to maintain productivity. Muğla province has a high forest fire potential due to its dense forest cover and approximately 200 forest fires occur each year. Forest fires are one of the main factors that threaten apiaries, as there are a lot of apiary places (approximately 15,000) in forests for pine honey. On the other hand, due to the mountainous topography and high precipitation rate of Muğla, the province has a high rate of flood formation (20 per year), which threatens the hive sites by destroying the entire colony. In this study, Apiary Locations Risk Index (ALRI) was carried out to guide the insurance process for apiary locations by applying the Forest Fire Risk Index (FFRI) and the Flood Hazard Risk Index (FHRI). Determination of forest fire risk zones and flood hazard maps requires environmental, forestry, topographic, economic and meteorological parameters to be handled within a decision support platform. For this purpose, Analytical Hierarchy Process (AHP) technique supported by Geographic Information System (GIS) was used in the creation of sensitivity maps. As a result, 1533.40 ha (11.82%) of the study area was determined as extremely risky areas for apiary areas. The results were confirmed with 1454 forest fire sites and 20 flood hazard sites where the Eşen, Dalaman, Çine, Sarıçay, Akçay, Kamiişdere and Namnam rivers were stated to be highly susceptible to flood hazard.Article Citation - WoS: 14Citation - Scopus: 17Exploration of Potential Geothermal Fields Using Maxent and Ahp: a Case Study of the B & Uuml;y & Uuml;k Menderes Graben(Pergamon-Elsevier Science Ltd, 2023-11-01) Yalçın, Mustafa; Sarı, Fatih; Yıldız, AhmetIn today's world, geothermal energy is essential as one of the alternative energy sources because it is renewable and does not harm the environment or the atmosphere. Buyuk Menderes Graben (BMG) has significant geothermal potential in the Aegean Region of Turkey. The Maximum Entropy (MaxEnt) Method and Multi -criteria Decision Analysis (MCDA) were used in this study to define potential geothermal areas. The Geographical Information Systems (GIS) Based Maxent Method is the machine learning method. The decision analysis stage of the MCDA employed the Analytic Hierarchy Process (AHP). A geothermal favorability map was produced by combining weighted layers of standardized data according to the AHP. The Maxent Method created the other geothermal favorability map that self-estimates the weight of criteria. The Natural Breaks Jenks Method was used to categorize both maps. The findings of our study were compared with the locations of the geothermal resources in BMG. Both methods result in high accuracy, but the MaxEnt method has a high sensitivity to the geological parameters (cap rock geology and fault) of the geothermal system. So, the high gain values ensured the target was determined more precisely in the Maxent method. The MaxEnt method in our study is a guide for the exploration of new geothermal fields. Further geothermal explorations in the BMG will increase the potential of thermal tourism, housing heating, greenhouse, and balneological applications and contribute to renewable energy production in Turkey.Article Citation - Scopus: 7Evaluating Sinkhole Formation With Multicriteria Decision Analysis: a Case Study in Karapnar-Konya, Turkey(SPRINGER HEIDELBERG, 2021) Sarı, Fatih; Kahveci, Muzaffer; Altaş, Melis Somay; Tuşat, Ekrem; Somay-Altas, Melis; Somay‐altas, MelisSinkholes (dolines) are considered natural hazards that threaten both human life and agricultural economic income. Due to their characteristic sudden occurrences, sinkholes are almost impossible to avoid. Geology, hydrogeology, irrigation, precipitation, climate, land use changes, and urbanization are the main factors that activate sinkhole occurrences. More than 300 sinkholes have been reported in the Karapnar region situated in Konya Province, Turkey, and this number has increased in the last 5 years. In particular, increasing agricultural activities cause rapid lowering of groundwater levels by excessive pumping for irrigation. A total of 55,267 water wells are in use in the region, which increases the risk factors for sinkholes in Karapnar. The importance of Karapnar region for solar energy, intensive agricultural activities, and a planned thermal power plant to be built soon gives estimating sinkhole probability and investigating ways of predicting and preventing sinkholes vital importance. The main purpose of this study is to predict possible sinkhole formation in the Konya region based on historical occurrences and to ensure reporting to authorities to raise awareness of this problem. Sinkhole susceptibility maps using the AHP, TOPSIS, and VIKOR methods, which are included in the multicriteria decision analysis (MCDA) concept, were prepared for the Karapnar region to achieve this purpose. The elevation, slope, geology, rock strength values, land use, water well density, and distance to settlements and roads were considered criteria to generate the sinkhole susceptibility maps. Strength test results (geological data set), which were not added to susceptibility maps in previous studies, were used in this study. The generated susceptibility maps were verified by correlation analysis and by overlaying existing sinkholes with susceptibility values. Necessary suggestions are presented for the Karapnar region based on the results of this study. The calculated ratios using the AHP, TOPSIS, and VIKOR methods are 3.53%, 2.55%, and 2.86%, respectively, which imply that the study area is highly susceptible to sinkhole occurrences. Based on the correlation analysis, an r value of 0.982 is derived using the AHP and VIKOR methods. When existing sinkhole locations are considered, the AHP method produces the most likely sinkhole locations. In addition, the AHP method can be used to prepare and update the susceptibility maps for any region that is at risk of sinkhole formation.Article Citation - WoS: 5Citation - Scopus: 4Multicriteria Analysis in Apiculture: a Sustainable Tool for Rural Development in Communities and Conservation Areas of Northwest Peru(Multidisciplinary Digital Publishing Institute (MDPI), 2023-10-10) Cotrina-Sanchez, A.; García, L.; Calle, C.; Sari, F.; Bandopadhyay, S.; Rojas-Briceño, N.B.; Meza-Mori, G.; Oliva, ManuelApiculture plays a vital role in maintaining a genetically diverse ecosystem and is an economic activity that contributes to the development of rural communities, thereby enhancing the livelihoods of beekeepers. However, despite the presence of over forty thousand beekeepers in Peru, there is currently no cartographic information available on optimal areas for the development of apiculture. Our study focused on assessing the suitability of land for apiculture development in rural and indigenous communities within the Amazonas Department in northwest Peru. We integrated biophysical and socioeconomic criteria using the Multiple Criteria Evaluation (MCE) technique, in conjunction with state-of-the-art geoinformation and earth observation techniques, to model and validate land suitability for supporting apiculture. It was identified that suitability is influenced by biophysical criteria (65%) and socioeconomic criteria (35%), resulting in highly suitable areas covering 315.6 km2 within the territory of peasant communities, 128.4 km2 within native communities, and an additional 41.4 km2 within conserved areas. Furthermore, to validate our results, we combined the use of high-resolution satellite imagery and visits to artisanal producers. This research provides valuable insights for spatiotemporal land use planning, emphasizing apicultural activity as a driver of rural development and biodiversity conservation. Consequently, this study contributes as a management tool to promote apicultural activities as support for rural development and in local-level decision making. © 2023 by the authors.Article Highway Route Planning Via Least Cost Path Algorithm and Multi Criteria Decision Analysis Integration, a Comparison of Ahp, Topsis and Vikor(2022) Sarı, Fatih; Şen, MehmetHighways are one of the main structures of cities in the field of economic, social, and environmental facilities that connect cities, regions, and people each other. Determining the suitable highway routes includes difficult and complex processes due to the construction costs. Additionally, priorities, expectations, and constraints for economic, social, and environmental parameters must be considered together to provide efficient solutions to requirements. Multi Criteria Decision Analysis (MCDA) techniques such as Analytical Hierarchy Process (AHP), Ideal Solution Similarity Selection Ranking Technique (TOPSIS), and Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and Least Cost Path Algorithm (LCPA) with Geographical Information Systems (GIS) combination are the most suitable way to overcome these complexities. In this study, slope, aspect, geology, elevation, distances to roads, settlements, water bodies, fault lines, buildings, natural disasters, protected sites, population, and land use were selected to determine most suitable highway construction areas and route. The AHP, TOPSIS, and VIKOR methods were applied to calculate cost surfaces for least cost paths generation with LCPA, and the generated three routes were compared. As a result of the comparisons VIKOR route was the most suitable route considering the topographical statistics and all the three methods consistent with each other and current road.Article Citation - WoS: 15Citation - Scopus: 17Investigation of the Importance of Criteria in Potential Wind Farm Sites Via Machine Learning Algorithms(Elsevier Ltd, 2024) Sari F.; Yalcin M.; Yalcin, Mustafa; Sari, FatihWind energy has received greater attention than other energy resources due to its superior economics, low greenhouse gas emissions, and limitless wind resources. As a result, wind energy capacity has significantly increased, and the selection of the best locations for wind farms is an issue that has received extensive research. A significant step toward environmentally responsible land use planning is the site suitability assessment for the placement of wind farms. This study was conducted to determine the best locations for wind farms and to prioritize different locations and alternatives in the West of Turkey by using Maximum Entropy (MaxEnt) and Logistic Regression (LR) Methods based on Geographic Information Systems (GIS). Eight criteria were selected for creating the suitability map: air density, power density, wind speed, capacity factor, elevation, slope, aspect, and land use. Both methods were effective at choosing locations for wind farms because all the results were statistically significant in the consistency tests. MaxEnt calculated the potential wind energy fields with high accuracy and reliability with 0.915 AUC and LR multiple R square values of 0.883. Compared to the current installed power values, the MaxEnt analysis results were more consistent with the recent status. İzmir has been calculated as the province with the highest potential for wind energy area of 663 km2 by MaxEnt and 620.4 km2 by LR. © 2024Article Citation - WoS: 6Citation - Scopus: 3Predicting Future Opportunities and Threats of Land-Use Changes on Beekeeping Activities in Turkey(Springer, 2023-07-10) Sarı, FatihSince beekeeping is directly dependent on land-use patterns, beekeeping is much more affected by land-use changes than other sectors. As land-use changes have accelerated over the past decade, beekeeping is becoming more vulnerable and the need to monitor the response of the honey bee colonies to land-use changes is more important than ever. In this study, land-use changes were identified and future projections were made to determine whether the study area will provide new opportunities for beekeeping or whether valuable land will be irretrievably destroyed. One of the main objectives of this study is to show the future trends of the Turkish apicultural sector and its response to land-use changes along with changes in agriculture, forests, urban and natural plant areas in 2025, 2040 and 2050. The results show that by 2050, a total of 2840 km(2) of fruit tree, 883.2 km(2) of agricultural area will increase, and 1481.4 km(2) of forest area will be lost. Since the largest expansion is expected in fruit trees (citrus in the study area), land-use changes were assessed by analyzing honey statistics for citrus and the contribution of honey bees to citrus pollination to verify the reliability of the predictions.Article Lavender Field Detection via Remote Sensing and Machine Learning for Optimal Hive Placement to Maximize Lavender Honey Production(MDPI, 2025-09-09) Sari, Fatih; Sarvia, FilippoLavender is a plant widely used in the cosmetic, pharmaceutical, and food industries, and it is also well known for producing nectar and pollen that bees use to make honey. However, due to increasingly adverse atmospheric conditions in recent years, characterized by prolonged dry spells or intense rainfall focused in short periods, the production of monofloral honey, such as lavender honey, has become increasingly challenging. Therefore, accurate mapping of monofloral zones in order to support beekeepers in placing their beehives in the best location is required. In this context, the town of Kuyucak in Isparta Province (Turkey), renowned for its extensive lavender fields, was selected. Using true orthophoto images from 2020 with a ground sampling distance (GSD) of 30 cm, machine learning classification methods and deep learning techniques were applied to identify and map the correspondent lavender fields. Lavender plants within the region were detected using Maximum Likelihood (ML), Support Vector Machine (SVM), and Random Forest (RF) classifiers, as well as the Mask R-CNN deep learning method. The classification achieved an overall accuracy of 95% and a kappa coefficient of 0.94. Subsequently, assuming a bee foraging range of 3 km, a moving squared window (sizing 3 x 3 km) was used to estimate local areas with potential forage resources and the corresponding honey production potential. The resulting honey potential production maps then used to identify optimal location for beekeepers' hives in order to maximize lavender honey production.Article Citation - WoS: 32Citation - Scopus: 37Identifying Anthropogenic and Natural Causes of Wildfires by Maximum Entropy Method-Based Ignition Susceptibility Distribution Models(Northeast Forestry Univ, 2022-06-22) Sarı, FatihTurkey has a high potential for wildfires along its Mediterranean coast because of its dense forest cover and mild climate. An average of 250 wildfires occurs every year with more than 10,000 hectares destroyed due to natural and human-related causes. The study area is sensitive to fires caused by lightning, stubble burning, discarded cigarette butts, electric arcing from power lines, deliberate fire setting, and traffic accidents. However, 52% of causes could not be identified due to intense wildfires occurring at the same time and insufficient equipment and personnel. Since wildfires destroy forest cover, ecosystems, biodiversity, and habitats, they should be spatially evaluated by separating them according to their causes, considering environmental, climatic, topographic and forest structure variables that trigger wildfires. In this study, wildfires caused by lightning, the burning of agriculture stubble, discarded cigarette butts and power lines were investigated in the provinces of Aydin, Mugla and Antalya, where 22% of Turkey's wildfires occurred. The MaxEnt method was used to determine the spatial distribution of wildfires to identify risk zones for each cause. Wildfires were used as the species distribution and the probability of their occurrence estimated. Additionally, since the causes of many wildfires are unknown, determining the causes is important for fire prediction and prevention. The highest wildfire occurrence risks were 9.7% for stubble burning, 30.2% for lightning, 4.5% for power lines and 16.9% by discarded cigarette butts. In total, 1,266 of the 1,714 unknown wildfire causes were identified by the analysis of the cause-based risk zones and these were updated by including cause-assigned unknown wildfire locations for verification. As a result, the Area under the ROC Curve (AUC) values were increased for susceptibility maps.
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Research Topics
Domains
Physical SciencesSocial SciencesLife Sciences
Fields
Environmental ScienceDecision SciencesAgricultural and Biological Sciences
Subfields
Management, Monitoring, Policy and LawManagement Science and Operations ResearchInsect ScienceEcology, Evolution, Behavior and SystematicsGlobal and Planetary Change
Specific Research Areas
Soil and Land Suitability Analysis
Multi-Criteria Decision Making
Bee Products Chemical Analysis
Plant and animal studies
Fire effects on ecosystems
Sustainable Development Goals
11SUSTAINABLE CITIES AND COMMUNITIES
6
Research Products
7AFFORDABLE AND CLEAN ENERGY
5
Research Products
2ZERO HUNGER
4
Research Products
13CLIMATE ACTION
2
Research Products
15LIFE ON LAND
2
Research Products
12RESPONSIBLE CONSUMPTION AND PRODUCTION
2
Research Products
3GOOD HEALTH AND WELL-BEING
1
Research Products
17PARTNERSHIPS FOR THE GOALS
1
Research Products
9INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
6CLEAN WATER AND SANITATION
1
Research Products
Documents
24
Citations
377
h-index
9
Documents
25
Citations
412
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Konya Technical University | 18 |
| Selçuk University | 17 |
| Afyon Kocatepe University | 4 |
| Ankara University | 3 |
| Konya Food and Agriculture University | 2 |
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Data obtained from OpenAlex
| Journal | Count |
|---|---|
| Renewable Energy | 2 |
| ARABIAN JOURNAL OF GEOSCIENCES | 1 |
| Doğal Afetler ve Çevre Dergisi | 1 |
| Earth | 1 |
| Environmental Science and Pollution Research | 1 |
Current Page: 1 / 4
Scholarly Output
24
Articles
19
Views / Downloads
56/39
Supervised MSc Theses
3
Supervised PhD Theses
0
WoS Citation Count
102
Scopus Citation Count
105
Patents
0
Projects
0
WoS Citations per Publication
4.25
Scopus Citations per Publication
4.38
Open Access Source
13
Supervised Theses
3
Scopus Quartile Distribution
Competency Cloud
