scholarly journals GIS-models with fuzzy logic for Susceptibility Maps of debris flow using multiple types of parameters: A Case Study in Pinggu District of Beijing, China

2021 ◽  
Author(s):  
Yiwei Zhang ◽  
Jianping Chen ◽  
Qing Wang ◽  
Chun Tan ◽  
Yongchao Li ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Debris Flow ◽  
Regional Scale ◽  
Land Development ◽  
Development Planning ◽  
Field Investigation ◽  
Assessment Model ◽  
Susceptibility Map ◽  
Insufficient Data ◽  
Mountainous Areas

Abstract. Debris flow is one of the main causes of life loss and infrastructure damage in mountainous areas, so these hazards must be recognized in the early stage of land development planning. According to field investigation and expert experience, a scientific and effective quantitative susceptibility assessment model was established in Pinggu District of Beijing. This model is based on Geographic Information System (GIS), combining with grey relational method, data-driven and fuzzy logic methods. The inherent influence factors, which are divided into two categories, are selected in the model consistent with the system characteristics of debris flow gully and some new factors are proposed. The results of the 17 models are verified by the results published by the authority, and validated by the other two indexes as well as Area Under Curve (AUC). Through the comparison and analysis of the results, the method to optimize is proposed, including reasonable application of field investigation and expert experience, simplification of factors and scientific classification. Finally, the final optimal susceptibility map with full discussion has the potential to help in determining regional-scale land use planning and debris flow hazard mitigation for decision makers, with full use of insufficient data, scientific calculation, and reliable results. The model has advantages in economically backward areas with insufficient data in mountainous areas.

scholarly journals Debris-flow activity in five adjacent gullies in a limestone mountain range

2015 ◽  
Vol 42 (1) ◽  
Author(s):  
Klaus Schraml ◽  
Markus Oismüller ◽  
Markus Stoffel ◽  
Johannes Hübl ◽  
Roland Kaitna
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
National Park ◽  
Regional Scale ◽  
Field Investigation ◽  
Mountain Range ◽  
Sediment Sources ◽  
Aerial Vehicle ◽  
Flow Activity ◽  
Local Variability

Abstract Debris-flows are infrequent geomorphic phenomena that shape steep valleys and can repre-sent a severe hazard for human settlements and infrastructure. In this study, a debris-flow event chro-nology has been derived at the regional scale within the Gesäuse National Park (Styria, Austria) using dendrogeomorphic techniques. Sediment sources and deposition areas were mapped by combined field investigation and aerial photography using an Unmanned Aerial Vehicle (UAV). Through the analysis of 384 trees, a total of 47 debris-flows occurring in 19 years between AD 1903 and 2008 were identified in five adjacent gullies. Our results highlight the local variability of debris-flow activi-ty as a result of local thunderstorms and the variable availability of sediment sources.

scholarly journals Incorporating Rarity and Accessibility Factors into the Cultural Ecosystem Services Assessment in Mountainous Areas: A Case Study in the Upper Reaches of the Minjiang River

2019 ◽  
Vol 11 (8) ◽  
pp. 2203 ◽  
Author(s):  
Yafeng Lu ◽  
Qinwen Li ◽  
Pei Xu ◽  
Yukuan Wang
Keyword(s):  
Ecosystem Services ◽  
Regional Scale ◽  
Management Strategies ◽  
Tourism Industry ◽  
Assessment Model ◽  
Ecological Security ◽  
Cultural Ecosystem Services ◽  
Mountainous Areas ◽  
Minjiang River

Cultural ecosystem services (CES) are not only a key source for supporting the development of economy but also maintain the ecological security in mountainous areas. However, there are limited numbers of studies that focus on establishing the assessment model for the CES at a regional scale. We combined the topographic factors and accessibility factors to quantify the distribution of CES and tested the approach with data on road and topography in the upper reaches of the Minjiang River. The results showed that the areas with high CES were located in the southwestern part of the study area, where it was convenient traffic and rare topography. Results from our approach were likely to support the development of local tourism industry because the distribution of CES was consistent with current hotspots for scenic spots. Meanwhile, we found that the area with high rarity and low accessibility should improve accessibility in order to enhance the capacity of CES. The assumptions applied in our approach highlighted the impacts of complex topography on CES, which could be suitable for the area with a lack of data. Moreover, our approach provided an effective way to assess CES for creating management strategies and enhancing capacity in mountainous areas.

On the criteria to create a susceptibility map to debris flow at a regional scale using Flow-R

2017 ◽  
Vol 14 (4) ◽  
pp. 621-635 ◽  
Author(s):  
Roberta Pastorello ◽  
Tamara Michelini ◽  
Vincenzo D’Agostino
Keyword(s):  
Debris Flow ◽  
Regional Scale ◽  
Susceptibility Map

scholarly journals Morphometry and Debris-Flow Susceptibility Map in Mountain Drainage Basins of the Vallo di Diano, Southern Italy

2021 ◽  
Vol 13 (16) ◽  
pp. 3254
Author(s):  
Salvatore Ivo Giano ◽  
Eva Pescatore ◽  
Vincenzo Siervo
Keyword(s):  
Debris Flow ◽  
Southern Italy ◽  
Regional Scale ◽  
Morphometric Parameters ◽  
Susceptibility Map ◽  
Good Reliability ◽  
Small Catchment ◽  
Susceptibility Maps ◽  
Mountain Basins ◽  
Western Side

In watershed mountain basins, affected in the last decades by strong rainfall events, the role of debris-flow and debris flood processes was investigated. Morphometric parameters have proven to be useful first-approximation indicators in discriminating those processes, especially in large areas of investigation. Computation of morphometric parameters in 19 watershed mountain basins of the western side valley of the Vallo di Diano intermontane basin (southern Italy) was carried out. This procedure was integrated by a semi-automatic elaboration of the potential susceptibility to debris flows, using Flow-R modelling. This software, providing an empirical model of the preliminary susceptibility assessment at a regional scale, was applied in many countries of the world. The implementation of Flow-R modelling requires a GIS application and some thematic base maps extracted using DEMs analysis. A 5-meter-resolution DEM has been used in order to produce the susceptibility maps of the whole study area, and the results are compared and discussed with the real debris flow/flood events that occurred in 1993, 2005, 2010, and 2017 in the studied area. The results have provided a good reliability of Flow-R modelling within small catchment mountain basins.

scholarly journals Erratum to: On the criteria to create a susceptibility map to debris flow at a regional scale using Flow-R

2017 ◽  
Vol 14 (5) ◽  
pp. 1008-1008 ◽  
Author(s):  
Roberta Pastorello ◽  
Tamara Michelini ◽  
Vincenzo d’Agostino
Keyword(s):  
Debris Flow ◽  
Regional Scale ◽  
Susceptibility Map

Exploring the impact of introducing a physical model into statistical methods on the evaluation of regional scale debris flow susceptibility

2021 ◽  
Vol 106 (1) ◽  
pp. 881-912
Author(s):  
Jingbo Sun ◽  
Shengwu Qin ◽  
Shuangshuang Qiao ◽  
Yang Chen ◽  
Gang Su ◽  
...  
Keyword(s):  
Debris Flow ◽  
Physical Model ◽  
Statistical Methods ◽  
Regional Scale ◽  
The Impact ◽  
Debris Flow Susceptibility

scholarly journals A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China

2021 ◽  
Vol 10 (2) ◽  
pp. 93
Author(s):  
Wei Xie ◽  
Xiaoshuang Li ◽  
Wenbin Jian ◽  
Yang Yang ◽  
Hongwei Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Method ◽  
Roc Curve ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Assessment Model ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Susceptibility Map ◽  
Area Index

Landslide susceptibility mapping (LSM) could be an effective way to prevent landslide hazards and mitigate losses. The choice of conditional factors is crucial to the results of LSM, and the selection of models also plays an important role. In this study, a hybrid method including GeoDetector and machine learning cluster was developed to provide a new perspective on how to address these two issues. We defined redundant factors by quantitatively analyzing the single impact and interactive impact of the factors, which was analyzed by GeoDetector, the effect of this step was examined using mean absolute error (MAE). The machine learning cluster contains four models (artificial neural network (ANN), Bayesian network (BN), logistic regression (LR), and support vector machines (SVM)) and automatically selects the best one for generating LSM. The receiver operating characteristic (ROC) curve, prediction accuracy, and the seed cell area index (SCAI) methods were used to evaluate these methods. The results show that the SVM model had the best performance in the machine learning cluster with the area under the ROC curve of 0.928 and with an accuracy of 83.86%. Therefore, SVM was chosen as the assessment model to map the landslide susceptibility of the study area. The landslide susceptibility map demonstrated fit with landslide inventory, indicated the hybrid method is effective in screening landslide influences and assessing landslide susceptibility.

scholarly journals Identifying Dominant Runoff Processes at a Regional Scale – A GIS - Based Approach

2017 ◽  
Vol 11 (2) ◽  
pp. 19-33
Author(s):  
Fagbohun Babatunde Joseph ◽  
Olabode Oluwaseun Franklin ◽  
Adebola Abiodun Olufemi
Keyword(s):  
Overland Flow ◽  
Expert Knowledge ◽  
Regional Scale ◽  
Field Investigation ◽  
Deep Percolation ◽  
Flood Prediction ◽  
Percolation Process ◽  
Least Area ◽  
Expected Increase

Abstract Identifying landscapes with similar hydrological characteristics is useful for the determination of dominant runoff process (DRP) and flood prediction. Several approaches used for DRP-mapping differ in respect to time and data requirement. Manual approaches based on field investigation and expert knowledge are time consuming and difficult to implement at regional scale. Automatic GIS-based approach on the other hand require simplification of data but are easier to implement and it is applicable on regional scale. In this study, GIS-based automated approach was used to identify the DRPs in Anambra area. The result showed that Hortonian Overland Flow (HOF) has the highest coverage of 1508.3 Km2 (33.5%) followed by Deep Percolation (DP) with coverage of 1455.3 Km2 (32.3%). Subsurface Flow (SSF) is the third dominant runoff process covering 920.6 Km2 (20.4%) while Saturated Overland Flow (SOF) covers the least area of 618.4 Km2 (13.7%) of the study area. The result reveal that considerable amount of precipitated water would be infiltrated into the subsurface through deep percolation process contributing to groundwater recharge in the study area. However, it is envisaged that HOF and SOF will continue to increase due to the continuous expansion of built-up area. With the expected increase in HOF and SOF and the change in rainfall pattern associated with perpetual problem of climate change, it is paramount that groundwater conservation practices be considered to ensure continued sustainable utilization of groundwater in the study area.

scholarly journals The temporally varying roles of rainfall, snowmelt and soil moisture for debris flow initiation in a snow-dominated system

2018 ◽  
Vol 22 (6) ◽  
pp. 3493-3513 ◽  
Author(s):  
Karin Mostbauer ◽  
Roland Kaitna ◽  
David Prenner ◽  
Markus Hrachowitz
Keyword(s):  
Soil Moisture ◽  
Debris Flow ◽  
High Intensity ◽  
Debris Flows ◽  
Regional Scale ◽  
Early Summer ◽  
Temporal Separation ◽  
Antecedent Soil Moisture ◽  
Trigger Mechanisms ◽  
Debris Flow Initiation

Abstract. Debris flows represent frequent hazards in mountain regions. Though significant effort has been made to predict such events, the trigger conditions as well as the hydrologic disposition of a watershed at the time of debris flow occurrence are not well understood. Traditional intensity-duration threshold techniques to establish trigger conditions generally do not account for distinct influences of rainfall, snowmelt, and antecedent moisture. To improve our knowledge on the connection between debris flow initiation and the hydrologic system at a regional scale, this study explores the use of a semi-distributed conceptual rainfall–runoff model, linking different system variables such as soil moisture, snowmelt, or runoff with documented debris flow events in the inner Pitztal watershed, Austria. The model was run on a daily basis between 1953 and 2012. Analysing a range of modelled system state and flux variables at days on which debris flows occurred, three distinct dominant trigger mechanisms could be clearly identified. While the results suggest that for 68 % (17 out of 25) of the observed debris flow events during the study period high-intensity rainfall was the dominant trigger, snowmelt was identified as the dominant trigger for 24 % (6 out of 25) of the observed debris flow events. In addition, 8 % (2 out of 25) of the debris flow events could be attributed to the combined effects of low-intensity, long-lasting rainfall and transient storage of this water, causing elevated antecedent soil moisture conditions. The results also suggest a relatively clear temporal separation between the distinct trigger mechanisms, with high-intensity rainfall as a trigger being limited to mid- and late summer. The dominant trigger in late spring/early summer is snowmelt. Based on the discrimination between different modelled system states and fluxes and, more specifically, their temporally varying importance relative to each other, this exploratory study demonstrates that already the use of a relatively simple hydrological model can prove useful to gain some more insight into the importance of distinct debris flow trigger mechanisms. This highlights in particular the relevance of snowmelt contributions and the switch between mechanisms during early to mid-summer in snow-dominated systems.

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