scholarly journals Developing a Dynamic Web-GIS Based Landslide Early Warning System for the Chittagong Metropolitan Area, Bangladesh

2018 ◽  
Vol 7 (12) ◽  
pp. 485 ◽  
Author(s):  
Bayes Ahmed ◽  
Md. Rahman ◽  
Rahenul Islam ◽  
Peter Sammonds ◽  
Chao Zhou ◽  
...  
Keyword(s):  
Metropolitan Area ◽  
Early Warning ◽  
Landslide Susceptibility ◽  
Early Warning System ◽  
Warning System ◽  
Web Gis ◽  
Slope Aspect ◽  
Susceptibility Maps ◽  
Landslide Early Warning ◽  
Landslide Susceptibility Maps

This article aims to develop a Web-GIS based landslide early warning system (EWS) for the Chittagong Metropolitan Area (CMA), Bangladesh, where, in recent years, rainfall-induced landslides have caused great losses of lives and property. A method for combining static landslide susceptibility maps and rainfall thresholds is proposed by introducing a purposely-build hazard matrix. To begin with, eleven factor maps: soil permeability; surface geology; landcover; altitude; slope; aspect; distance to stream; fault line; hill cut; road cut; and drainage network along with a detailed landslide inventory map were produced. These maps were used, and four methods were applied: artificial neural network (ANN); multiple regressions; principal component analysis; and support vector machine to produce landslide susceptibility maps. After model validation, the ANN map was found best fitting and was classified into never warning, low, medium, and high susceptibility zones. Rainfall threshold analysis (1960–2017) revealed consecutive 5-day periods of rainfall of 71–282 mm could initiate landslides in CMA. Later, the threshold was classified into three rainfall rates: low rainfall (70–160 mm), medium rainfall (161–250 mm), and high rainfall (>250 mm). Each landslide was associated with a hazard class (no warning vs. warning state) based on the assumption that the higher the susceptibility, the lower the rainfall. Finally, the EWS was developed using various libraries and frameworks that is connected with a reliable online-based weather application programming interface. The system is publicly available, dynamic, and replicable to similar contexts and is able to disseminate alerts five days in advance via email notifications. The proposed EWS is novel and the first of its kind in Bangladesh, and can be applied to mitigate landslide disaster risks.

scholarly journals Adapting the EDuMaP method to test the performance of the Norwegian early warning system for weather-induced landslides

2017 ◽  
Vol 17 (6) ◽  
pp. 817-831 ◽  
Author(s):  
Luca Piciullo ◽  
Mads-Peter Dahl ◽  
Graziella Devoli ◽  
Hervé Colleuille ◽  
Michele Calvello
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Monitoring Network ◽  
Variable Size ◽  
Weather Forecasts ◽  
Susceptibility Maps ◽  
Monitoring And Forecasting ◽  
Landslide Early Warning ◽  
Performance Results

Abstract. The Norwegian national landslide early warning system (LEWS), operational since 2013, is managed by the Norwegian Water Resources and Energy Directorate and was designed for monitoring and forecasting the hydrometeorological conditions potentially triggering slope failures. Decision-making in the LEWS is based upon rainfall thresholds, hydrometeorological and real-time landslide observations as well as on landslide inventory and susceptibility maps. Daily alerts are issued throughout the country considering variable size warning zones. Warnings are issued once per day for the following 3 days and can be updated according to weather forecasts and information gathered by the monitoring network. The performance of the LEWS operational in Norway has been evaluated applying the EDuMaP method, which is based on the computation of a duration matrix relating number of landslides and warning levels issued in a warning zone. In the past, this method has been exclusively employed to analyse the performance of regional early warning models considering fixed warning zones. Herein, an original approach is proposed for the computation of the elements of the duration matrix in the case of early warning models issuing alerts on variable size areas. The approach has been used to evaluate the warnings issued in Western Norway, in the period 2013–2014, considering two datasets of landslides. The results indicate that the landslide datasets do not significantly influence the performance evaluation, although a slightly better performance is registered for the smallest dataset. Different performance results are observed as a function of the values adopted for one of the most important input parameters of EDuMaP, the landslide density criterion (i.e. setting the thresholds to differentiate among classes of landslide events). To investigate this issue, a parametric analysis has been conducted; the results of the analysis show significant differences among computed performances when absolute or relative landslide density criteria are considered.

KAJIAN KEKUATAN SINYAL RADIO (RSSI) XBEE DALAM RANGKA PEMASANGAN LANDSLIDE EARLY WARNING SYSTEM (LEWS) DI KABUPATEN GARUT, TASIKMALAYA DAN MAJALENGKA

2019 ◽  
Vol 3 (2) ◽  
pp. 88
Author(s):  
Riski Fitriani
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Received Signal Strength Indicator ◽  
Landslide Early Warning ◽  
Point To Point

Salah satu inovasi untuk menanggulangi longsor adalah dengan melakukan pemasangan Landslide Early Warning System (LEWS). Media transmisi data dari LEWS yang dikembangkan menggunakan sinyal radio Xbee. Sehingga sebelum dilakukan pemasangan LEWS, perlu dilakukan kajian kekuatan sinyal tersebut di lokasi yang akan terpasang yaitu Garut, Tasikmalaya, dan Majalengka. Kajian dilakukan menggunakan 2 jenis Xbee yaitu Xbee Pro S2B 2,4 GHz dan Xbee Pro S5 868 MHz. Setelah dilakukan kajian, Xbee 2,4 GHz tidak dapat digunakan di lokasi pengujian Garut dan Majalengka karena jarak modul induk dan anak cukup jauh serta terlalu banyak obstacle. Topologi yang digunakan yaitu topologi pair/point to point, dengan mengukur nilai RSSI menggunakan software XCTU. Semakin kecil nilai Received Signal Strength Indicator (RSSI) dari nilai receive sensitivity Xbee maka kualitas sinyal semakin baik. Pengukuran dilakukan dengan meninggikan antena Xbee dengan beberapa variasi ketinggian untuk mendapatkan kualitas sinyal yang lebih baik. Hasilnya diperoleh beberapa rekomendasi tinggi minimal antena Xbee yang terpasang di tiap lokasi modul anak pada 3 kabupaten.

scholarly journals Brief communication "Landslide Early Warning System: toolbox and general concepts"

2013 ◽  
Vol 13 (1) ◽  
pp. 85-90 ◽  
Author(s):  
E. Intrieri ◽  
G. Gigli ◽  
N. Casagli ◽  
F. Nadim
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
End Users ◽  
Flow Chart ◽  
Warning Systems ◽  
Limited Experience ◽  
Practical Guidelines ◽  
Landslide Early Warning

Abstract. We define landslide Early Warning Systems and present practical guidelines to assist end-users with limited experience in the design of landslide Early Warning Systems (EWSs). In particular, two flow chart-based tools coming from the results of the SafeLand project (7th Framework Program) have been created to make them as simple and general as possible and in compliance with a variety of landslide types and settings at single slope scale. We point out that it is not possible to cover all the real landslide early warning situations that might occur, therefore it will be necessary for end-users to adapt the procedure to local peculiarities of the locations where the landslide EWS will be operated.

scholarly journals Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 884 ◽  
Author(s):  
Tingyu Zhang ◽  
Ling Han ◽  
Wei Chen ◽  
Himan Shahabi
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Shaanxi Province ◽  
Slope Aspect ◽  
Support Vector ◽  
Operating Characteristics ◽  
Study Region ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

The main purpose of the present study is to apply three classification models, namely, the index of entropy (IOE) model, the logistic regression (LR) model, and the support vector machine (SVM) model by radial basis function (RBF), to produce landslide susceptibility maps for the Fugu County of Shaanxi Province, China. Firstly, landslide locations were extracted from field investigation and aerial photographs, and a total of 194 landslide polygons were transformed into points to produce a landslide inventory map. Secondly, the landslide points were randomly split into two groups (70/30) for training and validation purposes, respectively. Then, 10 landslide explanatory variables, such as slope aspect, slope angle, altitude, lithology, mean annual precipitation, distance to roads, distance to rivers, distance to faults, land use, and normalized difference vegetation index (NDVI), were selected and the potential multicollinearity problems between these factors were detected by the Pearson Correlation Coefficient (PCC), the variance inflation factor (VIF), and tolerance (TOL). Subsequently, the landslide susceptibility maps for the study region were obtained using the IOE model, the LR–IOE, and the SVM–IOE model. Finally, the performance of these three models was verified and compared using the receiver operating characteristics (ROC) curve. The success rate results showed that the LR–IOE model has the highest accuracy (90.11%), followed by the IOE model (87.43%) and the SVM–IOE model (86.53%). Similarly, the AUC values also showed that the prediction accuracy expresses a similar result, with the LR–IOE model having the highest accuracy (81.84%), followed by the IOE model (76.86%) and the SVM–IOE model (76.61%). Thus, the landslide susceptibility map (LSM) for the study region can provide an effective reference for the Fugu County government to properly address land planning and mitigate landslide risk.

scholarly journals An acoustic emission landslide early warning system for communities in low-income and middle-income countries

Landslides ◽  
2018 ◽  
Vol 15 (8) ◽  
pp. 1631-1644 ◽  
Author(s):  
N. Dixon ◽  
A. Smith ◽  
J. A. Flint ◽  
R. Khanna ◽  
B. Clark ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Low Income ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Middle Income ◽  
Middle Income Countries ◽  
Landslide Early Warning

scholarly journals The Barriers to Landslide Responses over the Mt. Elgon in Bududa District, Uganda

2019 ◽  
Author(s):  
Marrion Namono ◽  
Isaac Mugume ◽  
Radu Negru ◽  
Godfrey Mujuni ◽  
Tao Sulin ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Communication Channel ◽  
Warning System ◽  
Focus Group Discussions ◽  
Group Discussions ◽  
Efficient Communication ◽  
Landslide Early Warning ◽  
Mount Elgon ◽  
Steep Slopes

Landslides are natural disasters that normally cause misery over the Mount Elgon region, especially in Bududa district. A landslide early warning system was developed in collaboration with the community and this study investigated it’s effectiveness in disseminating warnings to the community. The data were collected from 82 respondents (mean age 43) and 4 focus group discussions (one per village). Majority of the respondents lost crops (35.9%); land (29.8%); lives and livestock (6.9%). The frequent occurrence of landslides is due to the changes in landuse patterns; settlement on steep slopes; and prolonged rainfall of low intensities. The study found that, 93.2% of respondents have ever received the warnings and alerts. 78.8% of those who received the warnings evacuated. The use of radios to disseminate warnings is the most efficient communication channel (44.4%) followed by using the clan members (19.5%). Only 40% of the women received the early warning through radios, an indicator that this channel puts women at a disadvantage. The main challenges regarding utilization of early warning system were: poor timing (29.9%); poor coordination (20.7%); and poor sensitization (18.4%). There is need to strengthen the community networks, and with continuous sensitization, the effectiveness of the landslides early warning will improve and this is expected to enhance the resilience of the community to landslides.

scholarly journals GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia

2019 ◽  
Vol 11 (1) ◽  
pp. 708-726
Author(s):  
Zorgati Anis ◽  
Gallala Wissem ◽  
Vakhshoori Vali ◽  
Habib Smida ◽  
Gaied Mohamed Essghaier
Keyword(s):  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Satellite Image ◽  
Susceptibility Mapping ◽  
Weight Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Maps ◽  
North Western ◽  
Landslide Susceptibility Maps

AbstractThe Tunisian North-western region, especially Tabarka and Ain-Drahim villages, presents many landslides every year. Therefore, the landslide susceptibility mapping is essential to frame zones with high landslide susceptibility, to avoid loss of lives and properties. In this study, two bivariate statistical models: the evidential belief functions (EBF) and the weight of evidence (WoE), were used to produce landslide susceptibility maps for the study area. For this, a landslide inventory map was mapped using aerial photo, satellite image and extensive field survey. A total of 451 landslides were randomly separated into two datasets: 316 landslides (70%) for modelling and 135 landslides (30%) for validation. Then, 11 landslide conditioning factors: elevation, slope, aspect, lithology, rainfall, normalized difference vegetation index (NDVI), land cover/use, plan curvature, profile curvature, distance to faults and distance to drainage networks, were considered for modelling. The EBF and WoE models were well validated using the Area Under the Receiver Operating Characteristic (AUROC) curve with a success rate of 87.9% and 89.5%, respectively, and a predictive rate of 84.8% and 86.5%, respectively. The landslide susceptibility maps were very similar by the two models, but the WoE model is more efficient and it can be useful in future planning for the current study area.

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