Shallow Landslide Hazard Mapping for Davao Oriental, Philippines, Using a Deterministic GIS Model

Alejandrino, Ian Kaye; Lagmay, Alfredo Mahar Francisco; Eco, Rodrigo Narod

doi:10.1007/978-3-319-20161-0_9

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…Today, the need for new residential areas and new engineering buildings has increased rapidly because of the increase in population (Yalcin and Bulut, 2007). Unlike ooding which causes damage to structures that more often can be xed, landslides may leave irreparable damage (Alejandrino et al, 2016). Every year, a huge number of lives of citizens and properties are lost due to this landslide phenomenon.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of statistical methods and machine learning algorithms for prediction of landslides in Mizoram state of India through analysis of causative factors using Geo-informatics

VANLALNUNZIRA,

Prakash

2024

Preprint

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The landslide has been one of the most severe and significant natural hazards in the study area, Mizoram, which has rolling hills and deep valleys in almost every landform. A comparative study of landslide hazards in the area was conducted using various statistical analytic techniques and machine learning algorithms. The statistical method includes- Frequency Ratio (FR), Analytic Hierarchical Process (AHP), Shannon’s Entropy (SE), and Weight of Evidence (WOE), while the machine learning algorithms methods comprise basic classifiers such as Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Extreme Gradient Boosting (XGB), and hybrid classifiers using the Logistic Regression (LR) methods viz., GBDT + LR, RF + LR, XGB + LR. The study aims to find out the collinearity of various parameters of landslide-inducing factors and analyse their weight for most contributing factors to least contributing factors. It also aims to develop the Landslide Hazard Zonation (LHZ) map using various parameters weights and layer stacking by weighted sum overlay in a GIS software environment. The generated LHZ map was separated into five classes viz., low, moderate, high, very high, and severe. For statistical analysis, validation of the zonation maps was done by using past landslide inventories. Classification of the number of past landslides point data in each class of the zonation map was done to validate the accuracy of the zonation map. More than 65 per cent of Landslide point data falls in the High to Severe zone in the classification for FR, AHP, and SE which was considered to be in the positive validate zone, whereas only 60 per cent of Landslide point data falls in the High to Severe zone for WOE which was considered to be inadequate and undesirable for applicable LHZ map. For machine learning algorithms, a buffer zone of a 50m radius was created for the application of the seeding technique for preparing landslide inventory. More than 10000 landslide seeds cells and non-landslide cells were taken in which 80% and 20% train-test split was conducted. A series of metrics such as accuracy, precision, recall, f- f-measure, Area Under (receiver operating characteristic) Curve (AUC), kappa index, mean absolute error (MAE), and root mean square error (RMSE) was used to evaluate the accuracy and performance of the seven models. Based on the AUC curve, the XGB model having the highest AUC value (0.9039) was identified as the most efficient model among the machine learning models. It was found that an improvement of more than 15% accuracy was shown by the machine learning models compared to the statistical approach. The results suggest that the machine learning method is propitious for an application in landslide estimation in the study area.

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Section: Introductionmentioning

confidence: 99%

A comparative study of statistical methods and machine learning algorithms for prediction of landslides in Mizoram state of India through analysis of causative factors using Geo-informatics

VANLALNUNZIRA,

Prakash

2024

Preprint

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“…It is not advisable to locate communities or build infrastructure in areas identified by MGB as susceptible to landslides; however, relocation causes displacement of people from their communities and consequent loss of their source of livelihood which would have great social implications (Alejandrino et al, 2016). It is a huge challenge to the government to create a detailed and accurate landslide hazard map, based on the mechanics of slope which can be easily adopted by the local authorities and engineering agencies for land-use planning and slope instability monitoring.…”

Section: Introductionmentioning

confidence: 99%

Slope Stability Analysis for Deterministic Shallow Landslide Assessment and Mapping: A Case Study in Kibawe, Bukidnon

Caingles,

Lorenzo

2020

MJST

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The slope’s stability in mountainous or hilly region is significantly important for the safety and protection of the people and the environment. Intensive field sampling was carried out in the study sites at Kibawe, Bukidnon, Philippines and soil samples were subjected to laboratory tests. The study applied the deterministic method through slope stability analysis to calculate the factor of safety (FOS) for landslide assessment. It also used Geographic Information System (GIS) particularly the ArcGIS software for FOS mapping. The results revealed that the study sites have mostly inorganic but highly expansive fine-grained soils. The values of the FOS varied in each steady-state condition. For fully-saturated and dry conditions, the values ranged from 1.54-9.98 and 1.94-12.66, respectively. For partially-saturated condition where m = 0.75, m = 0.5 and m = 0.25, the values of FOS ranged from 1.65-10.65; 1.74-11.32; and 1.84- 11.99, respectively. In general, the FOS values signified that the study area was stable against sliding; however, there was a considerable decrease in FOS values when the slope changed from completely dry to fully-saturated condition. The created set of FOS maps lay out a visual impression and the variation of slope stability condition in the study area. The findings provide a baseline reference study for the area’s slope stability that is essential in planning or further assessment of the study area. Furthermore, this can be directly used by the local government for land-use planning and future development.

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Regional-scale landslide susceptibility modelling in the Cordillera Blanca, Peru—a comparison of different approaches

et al. 2018

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Shallow Landslide Hazard Mapping for Davao Oriental, Philippines, Using a Deterministic GIS Model

Cited by 7 publications

References 13 publications

A comparative study of statistical methods and machine learning algorithms for prediction of landslides in Mizoram state of India through analysis of causative factors using Geo-informatics

A comparative study of statistical methods and machine learning algorithms for prediction of landslides in Mizoram state of India through analysis of causative factors using Geo-informatics

Slope Stability Analysis for Deterministic Shallow Landslide Assessment and Mapping: A Case Study in Kibawe, Bukidnon

Regional-scale landslide susceptibility modelling in the Cordillera Blanca, Peru—a comparison of different approaches

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