Abstract
Van Yen district in Yen Bai province of Vietnam is one of the most affected areas indicating high and very high potential of landslide occurrences. WebGIS technology is useful for dissemination of the geographical information related to landslides. To this aim, this paper provides a landslide information system for Van Yen district of the Yen Bai province. The paper firstly provides a landslide susceptibility map produced using a Random Forest-based model. Along the process, a landslide inventory from three investigations of 2013, 2017, and landslides along national road 32 and provincial road 163 (collected in November 2021) was considered. Additionally, thirteen factors were used in the model as variables including geological data, DEM (digital elevation model), slope, aspect, plan curvature, profile curvature, stream power index, topographic wetness index, fault network, river network, road network, land use-land cover data, and Sentinel-2 based NDVI (normalized difference vegetation index). The model was validated based on confusion matrix, and gave an excellent accuracy of 91.33%. Finally, WebGIS was created using open-source technologies such as Leaflet, Openlayers, Geoserver, PostgreSQL as the database management system, and PostGIS as it is plugin for spatial database management. WebGIS not only contains information relevant to landslides, but it also combines the landslide susceptibility map with population data in order to assess exposure for warning purposes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Barančoková M, Šošovička M, Barančok P, Barančok P (2021) Predictive modelling of landslide susceptibility in the Western Carpathian Flysch Zone. Land 10:1370
Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology/un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol Sci Bull 24:43–69
Bordoni M, Galanti Y, Bartelletti C et al (2020) The influence of the inventory on the determination of the rainfall-induced shallow landslides susceptibility using generalized additive models. CATENA 193:104630
Bui DT, Lofman O, Revhaug I et al (2011) Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards 59:1413
Carrara A (1983) Multivariate models for landslide hazard evaluation. Math Geol 15:403–426
Chang KT, Merghadi A, Yunus AP et al (2019) Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques. Sci Rep 9:12296
Claessens L, Schoorl JM, Veldkamp A (2007) Modelling the location of shallow landslides and their effects on landscape dynamics in large watersheds: an application for northern New Zealand. Geomorphology 87:16–27
Cui S, Wu H, Pei X et al. (2022) Characterizing the spatial distribution, frequency, geomorphological and geological controls on landslides triggered by the 1933 MW 7.3 diexi earthquake, Sichuan, China. Geomorphology 403:108177
D’Amato Avanzi G, Giannecchini R, Puccinelli A (2004) The influence of the geological and geomorphological settings on shallow landslides. An example in a temperate climate environment: The June 19, 1996 event in northwestern Tuscany (Italy). Eng Geol 73:215–228
Dechkamfoo C, Sitthikankun S, Kridakorn Na Ayutthaya T et al. (2022) Impact of rainfall-induced landslide susceptibility risk on mountain roadside in northern Thailand. Infrastructures 7:17
Du J, Glade T, Woldai T et al (2020) Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley, Tibet. Chin Himalayas Eng Geol 270:105572
Evans S (1972) General Geomorphometry, Derivatives of Altitude, and Descriptive Statistics. In: Chorley RJ (ed) Spatial Analysis in Geomorphology. Methuen and Co., Ltd., London, pp 17–90
FAO: Strengthening capacities to enhance coordinated and integrated disaster risk reduction actions and adaptation to climate change in agriculture in the northern mountain regions of Vietnam, Baseline Survey of Yen Bai Province (2012)
Freeman EA, Frescino TS, Moisen GG (2018) ModelMap: an R package for model creation and map production. R Package Ver 4–6
General Introduction to Van Yen District. https://vanyen.yenbai.gov.vn/gioi-thieu/gioi-thieu-chung
General Statistics Office of Vietnam. Completed Results of the 2019 Viet Nam Population and Housing Census. https://www.gso.gov.vn/wp-content/uploads/2019/12/Ket-qua-toan-bo-Tong-dieu-tra-dan-so-va-nha-o-2019.pdf
Guerriero L, Prinzi EP, Calcaterra D et al (2021) Kinematics and geologic control of the deep-seated landslide affecting the historic center of Buonalbergo. Southern Italy Geomorphology 394:107961
Habumugisha JM, Chen N, Rahman M et al (2022) Landslide susceptibility mapping with deep learning algorithms. Sustainability 14:1734
Harmouzi H, Schlögel R, Jurchescu M, Havenith H-B (2021) Landslide susceptibility mapping in the vrancea-buzău seismic region, southeast Romania. Geosciences 11:495
Highland L, Bobrowsky PT (2008) The landslide handbook: a guide to understanding landslides, US. Geological Survey, Reston, Va
Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ann black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85:347–366
Kulkarni A, Batarseh FA, Yang R (2020) Foundations of data imbalance and solutions for a data democracy. In: Data democracy: at the Nexus of artificial intelligence, software development, and knowledge engineering, Academic Press, an imprint of Elsevier, London, San Diego, CA, pp 83–106
Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41
Liu R, Yang X, Xu C et al (2022) Comparative study of convolutional neural network and conventional machine learning methods for landslide susceptibility mapping. Remote Sens 14:321
Miao Z, Peng R, Wang W et al (2022) Integrating data modality and statistical learning methods for earthquake-induced landslide susceptibility mapping. Appl Sci 12:1760
MONRE: National disaster risk in Vietnam in the Period 2006–2016 and forecasting and warning system (2017)
Montgomery DR, Dietrich WE (1994) A physically based model for the topographic control on shallow landsliding. Water Resour Res 30:1153–1171
Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30
Qi T, Zhao Y, Meng X et al (2021) Distribution modeling and factor correlation analysis of landslides in the large fault zone of the western qinling mountains: a machine learning algorithm. Remote Sens 13:4990
Rabby YW, Li Y, Abedin J, Sabrina S (2022) Impact of land use/land cover change on landslide susceptibility in Rangamati Municipality of Rangamati District. Bangladesh ISPRS Int J Geo Inf 11:89
Rong G, Li K, Su Y et al (2021) Comparison of tree-structured Parzen estimator optimization in three typical neural network models for landslide susceptibility assessment. Remote Sens 13:4694
Shahzad N, Ding X, Abbas S (2022) A comparative assessment of machine learning models for landslide susceptibility mapping in the rugged terrain of northern Pakistan. Appl Sci 12:2280
Sharma A, Prakash C, Manivasagam V (2021) Entropy-based hybrid integration of Random Forest and support vector machine for landslide susceptibility analysis. Geomatics 1:399–416
Tien Bui D, Pradhan B, Lofman O et al (2012) Landslide susceptibility mapping at Hoa Binh Province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199–211
Vietnam Population Data, https://www.worldpop.org/geodata/summary?id=6449
WCDR: National Report on Disaster Reduction in Vietnam, 12 (2005)
Western AW, Grayson RB, Blöschl G et al (1999) Observed spatial organization of soil moisture and its relation to terrain indices. Water Resour Res 35:797–810
Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from kat landslides (Tokat—Turkey). Comput Geosci 35:1125–1138
Yordanov V, Brovelli MA (2021) Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano. Italy Appl Geomat 13:287–309
Yu L, Zhou C, Wang Y et al (2022) Coupling data-and knowledge-driven methods for landslide susceptibility mapping in human-modified environments: a case study from Wanzhou County, three gorges reservoir area. China Remote Sens 14:774
Zevenbergen LW, Thorne CR (1987) Quantitative analysis of land surface topography. Earth Surf Proc Land 12:47–56
Zhao L, Wu X, Niu R et al (2020) Using the rotation and Random Forest models of ensemble learning to predict landslide susceptibility. Geomat Nat Haz Risk 11:1542–1564
Zhao P, Masoumi Z, Kalantari M et al (2022) A GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods. Remote Sens 14:211
Zieher T, Perzl F, Rössel M et al (2016) A multi-annual landslide inventory for the assessment of shallow landslide susceptibility–two test cases in Vorarlberg. Austria Geomorphology 259:40–54
Acknowledgements
The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring”—CUP D19C21000480001 (Italian side) and partially funded by Ministry of Science and Technology of Vietnam (Vietnamese side) by the bilateral scientific research project between Vietnam and Italy, code: NĐT/IT/21/14.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Truong, X.Q. et al. (2023). WebGIS and Random Forest Model for Assessing the Impact of Landslides in Van Yen District, Yen Bai Province, Vietnam. In: Vo, P.L., Tran, D.A., Pham, T.L., Le Thi Thu, H., Nguyen Viet, N. (eds) Advances in Research on Water Resources and Environmental Systems. GTER 2022. Environmental Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-17808-5_27
Download citation
DOI: https://doi.org/10.1007/978-3-031-17808-5_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-17807-8
Online ISBN: 978-3-031-17808-5
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)