Evaluation of forest road network planning in landslide sensitive areas by GIS-based multi-criteria decision making approaches in Ihsangazi watershed, Northern Turkey

Buğday, Ender; Akay, Abdullah E.

doi:10.31298/sl.143.7-8.4

Cited by 9 publications

(15 citation statements)

References 25 publications

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“…Picchio et al (2018) implemented alternative road planning method, similar to present study, was used, but the landslide criteria were not considered by them. In another related study, Bugday and Akay (2019) evaluated landslide criteria in forest road planning, but alternative routes were not systematically searched.…”

Section: Discussionmentioning

confidence: 99%

“…(Kavzoglu et al, 2019). Approaches to LSM modeling vary widely, and some of the most common approaches are highlighted in this study: AHP (Kayastha et al, 2013;Roccati et al, 2021;Grozavu and Patriche, 2021), ANFIS (Paryani et al, 2020;Chen et al, 2021), ANN (Chen et al, 2017), PSO-ANN (Moayedi et al, 2019), Weighting Factor (Yalcin, 2008;Hussain et al, 2021), Bayesian (Sun et al, 2021;Lee et al, 2020), Deep Learning (Dao et al, 2020;Ngo et al, 2021), Frequency Ratio (Senanayake et al, 2020;Berhane et al, 2020), Fuzzy Logic (Tsangaratos et al, 2018Razifard et al, 2019), Logistic Regression (Schlögel et al, 2018;Chen et al, 2019), Machine Learning (Ghorbanzadeh et al, 2019, Kavzoglu et al, 2019, Mohammady et al, 2021, M-AHP (Nefeslioglu et al, 2012;Bugday and Akay, 2019), Multilayer Perceptron Neural Network (Li et al, 2019;Hong et al, 2020), SWARA (Dehnavi et al, 2015;Pourghasemi et al, 2019).…”

Section: Introductionmentioning

confidence: 94%

“…Elevation is an effective factor in forest road planning because it increases both landslide sensitivity and cost in the performance of road construction (Akay, 2006). As elevation also means an increase in the distance to settlements, it additionally reduces the costs of periodic maintenance works (Bugday and Akay, 2019). Aspect is one of the factors that affects soil properties and the growing environment.…”

Section: Landslide Susceptibility Factorsmentioning

confidence: 99%

“…Therefore, having identified these susceptible areas, more detailed planning may be conducted, and measures taken to prevent negative outcomes. (Raja et al, 2017;Bugday and Akay, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of alternative forest road routes and landslide susceptibility mapping using machine learning

Buğday

Akay

2022

CERNE

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Background: Forest roads are among the most basic infrastructure used for forestry activities and services. To facilitate the increased amount of biomass harvesting adequately, the existing road network may require modifications to allow forest transportation within harvesting units that are not yet accessed by the roads. The construction of a forest road can trigger landslides, so the necessary constraints should be considered when the road is being planned to preclude such problems. Landslide Susceptibility Mapping (LSM) has become an integral part of the growing process of machine learning (ML), providing a more effective platform for practitioners, planners, and decision-makers. This study aims to reveal the most suitable alternative routes for a forest road, especially in areas susceptible to landslides, and to provide an effective tool for decision-makers.Results: For this purpose, two models were developed through ML: Logistic Regression (LR) and Random Forest (RF). Elevation, slope, aspect, curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from the fault, the road, and the stream, and lithology were considered as the main landslide susceptibility factors in these models. The best model was obtained by the RF approach with an Area Under ROC Curve (AUC) value of 81.9%, while the LR model was 78.2%. LSM data was used as a base, and alternative routes were obtained through CostPath analysis. Conclusion:It has been shown that the ML methods used in this study can positively contribute to decision-making by providing more effective LSM calculations in studies to determine alternative routes in a forest road network.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

Section: Landslide Susceptibility Factorsmentioning

confidence: 99%

“…Therefore, having identified these susceptible areas, more detailed planning may be conducted, and measures taken to prevent negative outcomes. (Raja et al, 2017;Bugday and Akay, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of alternative forest road routes and landslide susceptibility mapping using machine learning

Buğday

Akay

2022

CERNE

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“…As the slope values increase and accordingly the slope stability deteriorates, the balance of the material on the slope deteriorates, and slips along the slope occur. The slope values reclassi ed into ve classes as very low (< 5), low (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15), moderate (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), high (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45), very high (45-60), and extreme (> 60). The vegetation type, microclimate, rainfall, and runoff process can be affected by elevation.…”

Section: #Fig 2 Approximately Here#mentioning

confidence: 99%

Analyzing Landslide Susceptibility of Forest Roads by Analytical Hierarchy Process (AHP) in Of Forest Planning Unit of Turkiye

Küçüker

2023

Preprint

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Forest roads are essential for the management of forest goods and services. However, the forest roads located in areas prone to landslides, particularly in Black Sea Region in Turkiye, can be damaged. Hence, the interest in Landslide Susceptibility Maps (LSMs) an important decision basis has become one of the crucial concerns in landslide risk areas in order to determine where to build new roads or to take necessary precautions on existing roads. This study aims to reveal the potential risk of forest roads for landslide in the Of Planning Unit. For this purpose, LSM was created by combining the MCDA Analytical Hierarchy Process (AHP) related to expert knowledge and Geographic Information Systems (GIS). Twelve landslide-related criteria, such as slope, bedrock type, relative relief, drainage density and frequency, rainfall, and land cover, were fabricated in raster format by ArcGIS domain. After the effects or weights of each main or sub factor were calculated by the pairwise comparison matrix in AHP, each layer was assigned to weight. The potential landslide areas were separated into five different categories, including extremely low, low, moderate, high, and extremely high through overlay analysis in ArcMap. Then overlapping analysis with forest roads and LSM was performed to obtain information on what planned roads are located in landslide-prone areas. The results indicated that this area is greatly susceptible to landslides. In addition, 18.45% of all roads are detected to be under high and extremely high risk, 28.7% of all roads are figured out to be under moderate susceptibility classes, and the remains are found to be under low and extremely low susceptibility classes. With respect to the high performance of AUC value (81%), the AHP technique can be used in landslide hazard risk management.

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Ranking Research and Development Needs of Silvicultural Operations for a Plantation Forestry Cooperative

Sibiya

Häggström

Talbot

2023

Small-scale Forestry

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Forest Owners Organizations can help small- and medium-scale private landowners stay competitive by conducting and sharing research and development (R&D) activities. This study evaluated R&D needs for silvicultural operations to inform an R&D strategy for the cooperative. Individual and group priorities were collected using the Analytical Hierarchy Process via a web survey and web meeting. Consensus in priorities was followed by an assessment of the cooperative's capacity to carry out in-house research. Two regional managers, eight plantation managers and one R&D/Technology manager participated in one or more stages of the process. Participants ranked most silvicultural operations similarly. However, the variation was largest for harvest residue, seedling, and stump management. Minor regional differences were found but both regions (south and north) had “very high” group consensus indicators (86.2% and 89%, respectively). The group decision ranked R&D in harvest residue management as the highest priority, followed by soil preparation and planting methods. The cooperative's strongest capacity for in-house research was in the execution of the experimental design to address the research questions (implementation). The weakest research capacity was found in terms of harvest residue, seedling, and stump management. Hence, the cooperative is dependent on research institutions. These findings can be used to inform and align the cooperative's R&D strategy, investments, and their research collaborations.

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Evaluation of forest road network planning in landslide sensitive areas by GIS-based multi-criteria decision making approaches in Ihsangazi watershed, Northern Turkey

Cited by 9 publications

References 25 publications

Assessment of alternative forest road routes and landslide susceptibility mapping using machine learning

Assessment of alternative forest road routes and landslide susceptibility mapping using machine learning

Analyzing Landslide Susceptibility of Forest Roads by Analytical Hierarchy Process (AHP) in Of Forest Planning Unit of Turkiye

Ranking Research and Development Needs of Silvicultural Operations for a Plantation Forestry Cooperative

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