Evaluation of cost and benefit of sediment based on landslide and erosion models

Rangsiwanichpong, Prem; Kazama, So; Ekkawatpanit, Chaiwat; Gunawardhana, Luminda Niroshana

doi:10.1016/j.catena.2018.10.010

Cited by 13 publications

(8 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Landslides, a form of soil erosion, affect sustainable development and land deterioration (Keesstra et al 2018;Visser et al 2019). Landslides can have high economic costs (Rangsiwanichpong et al 2019) and are hazards to people (Gao and Sang 2017;Nachappa et al 2020). They degrade land quality through erosion and sedimentation (Piacentini et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Spatial prediction of shallow landslide: application of novel rotational forest-based reduced error pruning tree

Arabameri

Santosh²,

Saha

et al. 2021

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

Landslides are a form of soil erosion threatening the sustainability of some areas of the world. There is, therefore, a need to investigate landslide rates and behaviour. In this research, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a meta classifier based on reduced error pruning tree (REPTree) as a base classifier called RF-REPTree, for landslide susceptibility mapping (LSM) in the Kalaleh watershed, Golestan Province, Iran. Some benchmark models, including the opensource Java decision tree (J48), naive Bayes tree (NBTree), and REPTree were used to compare the designed model. A total of 249 landslide locations were identified and mapped. The group was split into training (70%) and testing (30%) data for modelling and reliability analysis. Based on a literature review and multi-collinearity tests, 16 landslide conditioning factors (LCFs) were selected. Of the LCFs, the topographical position index (TPI) had the highest correlation with landslide occurrence. The LSM produced by RF-REPTree revealed that nearly 29% of the study areas have high to very high landslide susceptibility (LS). Statistical analysis of the model results included the receiver operating characteristic curve (ROC), the efficiency test, the true skill statistic (TSS), and the kappa index. ROC demonstrated that the AUC values of RF-REPTree, REPTree, J48, and NBTree models were 0.832, 0.700, 0.695, and 0.759 for succession rate curves and 0.794, 0.740, 0.788, and 0.728 for prediction rate curves, respectively. Therefore, ARTICLE HISTORY

show abstract

Section: Introductionmentioning

confidence: 99%

Spatial prediction of shallow landslide: application of novel rotational forest-based reduced error pruning tree

Arabameri

Santosh²,

Saha

et al. 2021

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

show abstract

“…It often associated with triggering factors such as an earthquake, a large storm, erosion or rapid snowmelt [1]. A landslide event usually occurs in multiple incidences that claims human lives, properties or both [1,2]. The term "landslide" encompasses five methods of slope movement: falls, topples, slides, spreads, and flows.…”

Section: Introductionmentioning

confidence: 99%

“…The term "landslide" encompasses five methods of slope movement: falls, topples, slides, spreads, and flows. These are further segmented by the nature of geologic material into; bedrock, debris and earth [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…This signifies that majority of the occupants are leaving in danger of being destroyed in future due to landslides. Similarly, [2] conducted future assessment of landslides and soil erosion events on sediment resources in Thailand from economic outlooks. The results showed huge economic lost to affect the people due to expected annual landslide damages which requires necessary proactive measures.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling Erosion and Landslides Induced by Farming Activities at Hilly Areas, Cameron Highlands, Malaysia

et al. 2019

View full text Add to dashboard Cite

This work was conducted at hilly farms of Cameron Highlands to model the incidences of soil erosion and landslides using historical data and field observations. IfSAR data with spatial resolution of 5 m was used which enable clear observation and delineation of the geographic features within the study area. Field visits were conducted to various places where landslides occurred on agricultural farms in order to validate the model. Also, the rate of soil erosions was evaluated using geospatial techniques. The potential landslide event and its probability of occurrence were combined using bivariate statistical analysis. The results revealed that most of the landslides incidents were occurred at areas with intensive agricultural activities with no proper erosion control measures. It was gathered that more than 75% of landslides occurred in agricultural activities areas are under sheltered farms. The annual soil erosion rates in both Telom and Bertom Catchments ware 38 ton /ha/year and 73.9 ton /ha/year respectively. It was revealed that, there is high risk of erosion-induced landslides in agricultural farms. However, the erosion induced landslide map shows that most the landslide occurred close to the rivers. This indicated that both agricultural operations and proximity to rivers are influencing factors for the incidences.

show abstract

“…Moreover, the brick plant leads approximately THB 2800 million to the rural economy [29]. Therefore, Rangsiwanichpong et al [30], with the aim of broadening people's awareness about the benefits of sediment resources, applied a new RUSLE-based technique to assess the benefit of sediment yield in Thailand.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Benefit-Cost Ratio of Sediment Resources by Remote Sensing Data in the Ping River Basin, Thailand

Rangsiwanichpong

Melesse

2022

Water

Self Cite

View full text Add to dashboard Cite

Sediment is one of the important natural resources on the Earth. Information on sediment resources is key to making decisions regarding soil resources management and mitigation during sediment hazard events. Thus, this research analyzed and mapped the benefit-cost ratio (BCR) of sediment in the Ping River using a Geographic Information System (GIS). Furthermore, the benefit of sediment was analyzed using a new application of the Revised Universal Soil Loss Equation (RUSLE) with a spatial resolution of 1 km2. The results reveal that the potential of annual soil loss and sediment deposition in the Ping River Basin (PRB) were approximately 825 and 530 m3/km2·y, respectively. In addition, the results indicated that there was a higher BCR in the upstream area of the PRB where there was greater sediment deposition. The average benefit of sediment in the PRB is USD 4280/km2·y. It is expected that the BCR of the sediment resources map analyzed in this research will help policy-makers for decision-making on the benefits of sediment resources in Thailand.

show abstract

Evaluation of cost and benefit of sediment based on landslide and erosion models

Cited by 13 publications

References 35 publications

Spatial prediction of shallow landslide: application of novel rotational forest-based reduced error pruning tree

Spatial prediction of shallow landslide: application of novel rotational forest-based reduced error pruning tree

Modelling Erosion and Landslides Induced by Farming Activities at Hilly Areas, Cameron Highlands, Malaysia

Analyzing the Benefit-Cost Ratio of Sediment Resources by Remote Sensing Data in the Ping River Basin, Thailand

Contact Info

Product

Resources

About