Risk analysis of forest roads using landslide susceptibility maps and generation of the optimum forest road route: a case study in Macka, Turkey

Kadı, Fatih; Yildirim, Faruk; Saralioğlu, Ekrem

doi:10.1080/10106049.2019.1659424

Cited by 19 publications

(13 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The difference in this study, however, is that the analysis of determining alternative routes was applied to the forest road. In a related study, Kadi et al (2019) used AHP as a multi-criteria decision method and the route planning was made in MATLAB software. Picchio et al (2018) implemented alternative road planning method, similar to present study, was used, but the landslide criteria were not considered by them.…”

Section: Discussionmentioning

confidence: 99%

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

Buğday

Akay

2022

CERNE

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Buğday

Akay

2022

CERNE

View full text Add to dashboard Cite

show abstract

“…As indicated by Hearn et al (2019) it is important that the stability of the road reserve and its associated engineering assets are maintained as a priority for the Uganda National Road Authority including District Local Governments, and that this necessitates consideration of earthworks slopes as well as the wider landscape in which the road is constructed. Susceptibility mapping and zoning can reveal the spatial differentiations of HLDs (Yin et al, 2020) and this combined with use of Algorithm can reveal alternative safer and less risky routes for road construction (Kadi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Road works inside and outside the protected areas are also affected. For instance, forest roads have been reportedly affected by landslides in Turkey (Kadi, et al, 2019). Mountainous regions in developing countries including Uganda frequently suffer from landslides.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Landslide susceptibility and risk implication to road network in Mt Elgon, Uganda

Nakileza

Mugagga

Musali

et al. 2023

Preprint

View full text Add to dashboard Cite

Globally landslides occurrence is reportedly frequent particularly in the mountainous regions causing both direct and indirect effects to various sectors including the road transport. Existing literature reveals limited assessment of road vulnerability to landslides in the mountain regions in Africa. The objective of this study was to investigate the risk to different segments of the road network in the Mt Elgon region. A Fuzzy logic model was used to assess and map the landslide susceptibility of the study area. A total of 478 landslide sites were used in the model development. Ten conditional factors were applied for generating the dataset for training and validation of the model. The results reveal that mid to high altitude steep and rugged areas are more susceptible to landslides. The model performance was good as revealed by high Area Under the Curve (AUC) of 83% and thus can be relied upon in landslide susceptibility mapping. The hotspot segments, which are high risk sections of the road network need to be prioritized for monitoring so as to initiate and strength existing risk mitigation strategies.

show abstract

“…Road works inside and outside the protected areas are also affected. For instance, forest roads have been reportedly affected by landslides in Turkey (Kadi, et al, 2019). Developing countries including Uganda and in particular the mountainous regions frequently suffer from landslides.…”

Section: Introductionmentioning

confidence: 99%

“…As indicated by Hearn et al (2019) it is important that the stability of the road reserve and its associated engineering assets are maintained as a priority for the Uganda National Road Authority as well as District Local Governments, and that this necessitates consideration of earthworks slopes as well as the wider landscape in which the road is constructed. Susceptibility mapping and zoning can reveal the spatial differentiations of HLDs (Yin et al, 2020) and this combined with use of Algorithm can reveal alternative safer and less risky routes for road construction (Kadi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Landslide susceptibility and risk to road network in Mt Elgon, Uganda

Nakileza

Mugagga

Musali

et al. 2022

Preprint

View full text Add to dashboard Cite

Globally landslides occurrence is reportedly frequent particularly in the mountainous regions causing both direct and indirect effects to various sectors including the road transport. Landslides directly cause physical impact on the road network such as deposition of debris and impartial or total erosion of road segments. This leads to increased damage costs. Indirectly landslides cause disruption of the trade and movement whenever roads are blocked and alternative routes are resorted to. Existing literature reveals limited assessment of road vulnerability to landslides in the mountain regions in Africa. This study aimed at closing this information gap by investigating the risk to different segments of the road network in the Mt Elgon region. A fuzzy logic model was used to assess and map the landslide susceptibility into low, moderate, high and very high categories. The results reveal that mid to high altitude steep and rugged areas are more susceptible to landslides. The model performance was good as revealed by high AUC of 83%. Hotspot segments, which are high risk sections of the road network need to be prioritized for monitoring and risk mitigation.

show abstract

Risk analysis of forest roads using landslide susceptibility maps and generation of the optimum forest road route: a case study in Macka, Turkey

Cited by 19 publications

References 40 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

Assessment of Landslide susceptibility and risk implication to road network in Mt Elgon, Uganda

Assessment of Landslide susceptibility and risk to road network in Mt Elgon, Uganda

Contact Info

Product

Resources

About