scholarly journals DELIVERING GIS TRAINING USING GEOSPATIAL WEB SERVICE – A CASE STUDY OF LANDSLIDE RISK MAPPING IN HONG KONG

2015 ◽  
Vol XL-6/W1 ◽  
pp. 15-16
Author(s):  
J. Huang ◽  
L. You ◽  
Q. Zhou ◽  
H. Wu
Keyword(s):  
Hong Kong ◽  
Web Service ◽  
Early Warning ◽  
Risk Level ◽  
Landslide Risk ◽  
Landslide Prediction ◽  
Web Based ◽  
Warning Messages ◽  
Geospatial Web Service

This paper sketches a prototype of web-based landslide prediction service for delivering web-based training. The results show that the proposed landslide GWSC model can effectively compute the landslide risk level in different location, and consequently allow for early-warning, which starts with the sensor in the field and ending with user-opitmized warning messages and action advice.

Landslide Risk Level Assessment Based on Erosion-Induced Landslide: A Case Study at Uitm Campus, Selangor

2017 ◽  
pp. 29-40
Author(s):  
Mohd Fozi Ali ◽  
Muhammad Solahuddeen Mohd Sabri ◽  
Khairi Khalid ◽  
Nor Faiza Abd Rahman
Keyword(s):  
Risk Level ◽  
Landslide Risk

scholarly journals Assessing the annual risk of vehicles being hit by a rainfall-induced landslide: a case study on Kennedy Road in Wan Chai, Hong Kong

2020 ◽  
Vol 20 (6) ◽  
pp. 1833-1846 ◽  
Author(s):  
Meng Lu ◽  
Jie Zhang ◽  
Lulu Zhang ◽  
Limin Zhang
Keyword(s):  
Hong Kong ◽  
Failure Probability ◽  
Slope Failure ◽  
Failure Time ◽  
Suggested Method ◽  
Risk Level ◽  
Annual Number ◽  
Failure Data ◽  
Moving Vehicles

Abstract. Landslides threaten the safety of vehicles on highways. When analyzing the risk of a landslide hitting moving vehicles, the spacing between vehicles and the types of vehicles on the highway can be highly uncertain and have often been omitted in previous studies. Using a highway slope in Hong Kong as a case study, this paper presents a method for assessing the risk of moving vehicles being hit by a rainfall-induced landslide; this method also allows for the possible number of different types of vehicles hit by the landslide to be investigated. In this case study, the annual failure probability of the slope is analyzed based on historical slope failure data from Hong Kong. The spatial impact of the landslide is evaluated based on an empirical run-out prediction model. The consequences of the landslide are assessed using probabilistic modeling of the traffic, which can consider uncertainties in the vehicle spacing, vehicle types and slope failure time. Using the suggested method, the expected annual number of vehicles and people hit by the landslide can be conveniently calculated. This method can also be used to derive the cumulative frequency–number of fatalities curve for societal risk assessment. Using the suggested method, the effect of factors like the annual failure probability of the slope and the density of vehicles on the risk level of the slope can be conveniently assessed. The method described in this paper can provide a new guideline for highway slope design in terms of managing the risk of landslides hitting moving vehicles.

DTM-based landslide detection using deep learning: A case study in Hong Kong

2020 ◽  
Author(s):  
Haojie Wang ◽  
Limin Zhang
Keyword(s):  
Hong Kong ◽  
Deep Learning ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Digital Terrain Model ◽  
Landslide Inventory ◽  
Landslide Risk ◽  
Visual Interpretation ◽  
Learning Techniques

<p>Landslide detection is an essential component of landslide risk assessment and hazard mitigation. It can be used to produce landslide inventories which are considered as one of the fundamental auxiliary data for regional landslide susceptibility analysis. In order to achieve high landslide interpretation accuracy, visual interpretation is frequently used, but suffers in time efficiency and labour demand. Hence, an automatic landslide detection method utilizing deep learning techniques is implemented in this work to conduct high-accuracy and fast landslide interpretation. As the ground characteristics and terrain features can precisely capture the three-dimensional space form of landslides, high-resolution digital terrain model (DTM) is taken as the data source for landslide detection. A case study in Hong Kong, China is conducted to validate the applicability of deep learning techniques in landslide detection. The case study takes multiple data layers derived from the DTM (e.g., elevation, slope gradient, aspect, etc.) and a local landslide inventory named enhanced natural terrain landslide inventory (ENTLI) as its data sources, and integrates them into a database for learning. Then, a deep learning technique (e.g., convolutional neural network) is used to train models on the database and perform landslide detection. Results of the case study show great performance and capacity of the applied deep learning techniques, which provides valuable references for advancing landslide detection.</p>

scholarly journals Quantitative Risk Assessment of Vehicles Hit by Landslides: A Case Study

2020 ◽  
Author(s):  
Meng Lu ◽  
Jie Zhang ◽  
Lulu Zhang ◽  
Limin Zhang
Keyword(s):  
Risk Assessment ◽  
Hong Kong ◽  
Failure Probability ◽  
Impact Analysis ◽  
Suggested Method ◽  
Quantitative Risk Assessment ◽  
Landslide Risk ◽  
The Road ◽  
On The Road

Abstract. Landslides threaten the safety of vehicles on highways. Nevertheless, a rigorous quantitative highway landslide risk assessment seems difficult. Using a case study in Hong Kong, this paper presents a method for quantitative risk assessment for highway landslides. The suggested method consists of three parts, i.e., analysis of annual failure probability of the slope, the spatial impact analysis and the consequence analysis. In the case study, the annual failure probability of the slope is analyzed based on historical failure data in Hong Kong. The spatial impact of the landslides is estimated based on empirical correlations with the geometry of the slope. The consequence is assessed based on probabilistic modeling of the traffic on the highway. Based on the suggested method, the annual failure probability of the slope, the distance from the slope and the road and the density of vehicles on the road can significantly affect the landslide risk and the suggested method can be used to quantify the effects of these factors. The suggested method can be also potentially used to analyze the highway landslide risk in other regions.

scholarly journals How suitable is Yahoo! Groups for school librarians’ professional networking? A case study from Hong Kong

2016 ◽  
Author(s):  
Samson Choi ◽  
Zvjezdana Dukic
Keyword(s):  
Hong Kong ◽  
Qualitative Data ◽  
Case Study Research ◽  
School Librarians ◽  
Web Based ◽  
Professional Needs ◽  
Professional Networking ◽  
Group Members ◽  
User Friendly

This paper explores how members of Hong Kong based professional association of school librarians (ALESS) use Yahoo! Groups platform for their professional networking and how the platform satisfies the group’s professional needs. The case study research method is applied and both quantitative and qualitative data are collected. It is revealed that ALESS members effectively use Yahoo! Groups platform even though they do not exploit all available features. ALESS members regularly use web-based mail and occasionally Files and Polls. Although some group members think that Yahoo! Groups need to be replaced with a more user- friendly tool, most respondents agree that for the time being Yahoo! Groups platform fairly fulfils ALESS group’s needs. Further analysis of existing social networking software and ALESS group’s needs and preferences are recommended.

scholarly journals INDEX AND RISK LEVEL OF LANDSLIDES ON ROAD AND BRIDGES LOCATED ON NATIONAL ROADS (CASE STUDY: KERINCI DISTRICT AND SUNGAI PENUH CITY, JAMBI PROVINCE)

2021 ◽  
Vol 8 (6) ◽  
pp. 1-11
Author(s):  
Mutiara Sari ◽  
Mutiara Yusnidar ◽  
Febrian Hadinata
Keyword(s):  
Geographical Information ◽  
Risk Level ◽  
Capacity Assessment ◽  
Landslide Risk ◽  
Hazard Exposure ◽  
External Context ◽  
Management Capacity ◽  
National Road ◽  
The City

The regency of Kerinci and The city of Sungai Penuh are among the areas prone to landslides, which can impact roads and bridges on national roads. This study aims to determine the index and risk level of landslides on roads and bridges located on national roads in both regions. The index and risk level assessment are carried out by analyzing disaster risk factors, namely: hazard, exposure, vulnerability, external context and capacity. Assessment methods and variables are taken based on the Guidelines for the Implementation of Risk Analysis for Natural Disasters Affecting Roads and Bridges. The mapping of landslide risk based on the Geographical Information System is compiled based on scoring and weighting all parameters, as well as an overlay among all constituent parameters. Based on the sampling results of ten samples of national road sections in the Kerinci Regency and Sungai Penuh City, the risk level of hazard, exposure, vulnerability, external context and road management capacity is divided into two classes, namely the Low class about 7.19 km (= 8.72 %) with seven short roads located within the city and the Medium class, which is 75.31 Km (= 91.28 %), with three roads connecting the cities.

scholarly journals Diversity of Rainfall Thresholds for early warning of hydro-geological disasters

2017 ◽  
Vol 44 ◽  
pp. 53-60 ◽  
Author(s):  
Davide L. De Luca ◽  
Pasquale Versace
Keyword(s):  
Early Warning ◽  
Southern Italy ◽  
Critical Value ◽  
Exceedance Probability ◽  
Occurrence Probability ◽  
Rainfall Thresholds ◽  
Landslide Prediction ◽  
Emergency Plans ◽  
Geological Disasters

Abstract. For early warning of disasters induced by precipitation (such as floods and landslides), different kinds of rainfall thresholds are adopted, which vary from each other, on the basis on adopted hypotheses. In some cases, they represent the occurrence probability of an event (landslide or flood), in other cases the exceedance probability of a critical value for an assigned indicator I (a function of rainfall heights), and in further cases they only indicate the exceeding of a prefixed percentage a critical value for I, indicated as Icr. For each scheme, it is usual to define three different criticality levels (ordinary, moderate and severe), which are associated to warning levels, according to emergency plans. This work briefly discusses different schemes of rainfall thresholds, focusing attention on landslide prediction, with some applications to a real case study in Calabria region (southern Italy).

scholarly journals The SESAMO early warning system for rainfall-triggered landslides

2015 ◽  
Vol 18 (2) ◽  
pp. 256-276 ◽  
Author(s):  
D. Pumo ◽  
A. Francipane ◽  
F. Lo Conti ◽  
E. Arnone ◽  
P. Bitonto ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Low Cost ◽  
Warning System ◽  
Landslide Risk ◽  
Web Based ◽  
Web Technologies ◽  
Processing Level ◽  
New Frontier ◽  
The Relationship

The development of Web-based information systems coupled with advanced monitoring systems could prove to be extremely useful in landslide risk management and mitigation. A new frontier in the field of rainfall-triggered landslides (RTLs) lies in the real-time modelling of the relationship between rainfall and slope stability; this requires an intensive monitoring of some key parameters that could be achieved through the use of modern and often low-cost technologies. This work describes an integrated information system for early warning of RTLs that has been deployed and tested, in a prototypal form, for an Italian pilot site. The core of the proposed system is a wireless sensor network collecting meteorological, hydrological and geotechnical data. Data provided by different sensors and transmitted to a Web-based platform are used by an opportunely designed artificial neural network performing a stability analysis in near real-time or in forecast modality. The system is able to predict whether and when landslides could occur, providing early warnings of potential slope failures. System infrastructure, designed on three interacting levels, encompasses a sensing level, integrating different Web-based sensors, a processing level, using Web standard interoperability services and specifically implemented algorithms, and, finally, a warning level, providing warning information through Web technologies.

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