scholarly journals Rainfall Thresholds for Prediction of Landslides in Idukki, India: An Empirical Approach

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2113 ◽  
Author(s):  
Minu Treesa Abraham ◽  
Deekshith Pothuraju ◽  
Neelima Satyam
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Regional Scale ◽  
Warning System ◽  
Antecedent Rainfall ◽  
Rainfall Forecasting ◽  
Study Region ◽  
Landslide Occurrence ◽  
South Indian ◽  
Landslide Early Warning

Idukki is a South Indian district in the state of Kerala, which is highly susceptible to landslides. This hilly area which is a hub of a wide variety of flora and fauna, has been suffering from slope stability issues due to heavy rainfall. A well-established landslide early warning system for the region is the need of the hour, considering the recent landslide disasters in 2018 and 2019. This study is an attempt to define a regional scale rainfall threshold for landslide occurrence in Idukki district, as the first step of establishing a landslide early warning system. Using the rainfall and landslide database from 2010 to 2018, an intensity-duration threshold was derived as I = 0.9D-0.16 for the Idukki district. The effect of antecedent rainfall conditions in triggering landslide events was explored in detail using cumulative rainfalls of 3 days, 10 days, 20 days, 30 days, and 40 days prior to failure. As the number of days prior to landslide increases, the distribution of landslide events shifts towards antecedent rainfall conditions. The biasness increased from 72.12% to 99.56% when the number of days was increased from 3 to 40. The derived equations can be used along with a rainfall forecasting system for landslide early warning in the study region.

scholarly journals Application of FLaIR model for early warning system in Chibo Pashyor, Kalimpong, India for rainfall-induced landslides

2017 ◽  
Author(s):  
Abhirup Dikshit ◽  
Neelima Satyam
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Risk Mitigation ◽  
Warning System ◽  
Landslide Risk ◽  
Rainfall Forecasting ◽  
Landslide Occurrence ◽  
Mobility Function ◽  
Hourly Rainfall ◽  
Rainfall Model

Abstract. The development of an early warning system for landslides due to rainfall has become an indispensable part for landslide risk mitigation. This paper explains the application of the hydrological FLaIR (Forecasting of Landslides Induced by Rainfall) model, correlating rainfall amount and landslide events. The FLaIR model comprises of two modules: RL (Rainfall-Landslide) which correlates rainfall and landslide occurrence and RF (Rainfall-Forecasting) which allows simulation of future rainfall events. The model can predetermine landslides based on identification of mobility function Y(.) which links actual rainfall and incidence of landslide occurrence. The critical value of mobility function was analyzed using 1st July 2015 event and applying it to 2016 monsoon to validate the results. These rainfall thresholds presented can be improved with intense hourly rainfall and landslide inventory data. This paper describes the details of the model and its performance for the study area.

Updating and tuning a regional-scale landslide early warning system

Landslides ◽  
2012 ◽  
Vol 10 (1) ◽  
pp. 91-97 ◽  
Author(s):  
D. Lagomarsino ◽  
S. Segoni ◽  
R. Fanti ◽  
F. Catani
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Regional Scale ◽  
Warning System ◽  
Landslide Early Warning

scholarly journals Brief communication: Using averaged soil moisture estimates to improve the performances of a regional-scale landslide early warning system

2018 ◽  
Vol 18 (3) ◽  
pp. 807-812 ◽  
Author(s):  
Samuele Segoni ◽  
Ascanio Rosi ◽  
Daniela Lagomarsino ◽  
Riccardo Fanti ◽  
Nicola Casagli
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Early Warning System ◽  
Preliminary Investigation ◽  
Regional Scale ◽  
Back Analysis ◽  
Warning System ◽  
False Alarms ◽  
Rainfall Thresholds ◽  
Landslide Early Warning

Abstract. We communicate the results of a preliminary investigation aimed at improving a state-of-the-art RSLEWS (regional-scale landslide early warning system) based on rainfall thresholds by integrating mean soil moisture values averaged over the territorial units of the system. We tested two approaches. The simplest can be easily applied to improve other RSLEWS: it is based on a soil moisture threshold value under which rainfall thresholds are not used because landslides are not expected to occur. Another approach deeply modifies the original RSLEWS: thresholds based on antecedent rainfall accumulated over long periods are substituted with soil moisture thresholds. A back analysis demonstrated that both approaches consistently reduced false alarms, while the second approach reduced missed alarms as well.

scholarly journals Estimating Rainfall Thresholds for Landslide Occurrence in the Bhutan Himalayas

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1616 ◽  
Author(s):  
Abhirup Dikshit ◽  
Raju Sarkar ◽  
Biswajeet Pradhan ◽  
Saroj Acharya ◽  
Kelzang Dorji
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Rainfall Duration ◽  
The World ◽  
Landslide Data

Consistently over the years, particularly during monsoon seasons, landslides and related geohazards in Bhutan are causing enormous damage to human lives, property, and road networks. The determination of thresholds for rainfall triggered landslides is one of the most effective methods to develop an early warning system. Such thresholds are determined using a variety of rainfall parameters and have been successfully calculated for various regions of the world at different scales. Such thresholds can be used to forecast landslide events which could help in issuing an alert to civic authorities. A comprehensive study on the determination of rainfall thresholds characterizing landslide events for Bhutan is lacking. This paper focuses on defining event rainfall–duration thresholds for Chukha Dzongkhag, situated in south-west Bhutan. The study area is chosen due to the increase in frequency of landslides during monsoon along Phuentsholing-Thimphu highway, which passes through it and this highway is a major trade route of the country with the rest of the world. The present threshold method revolves around the use of a power law equation to determine event rainfall–duration thresholds. The thresholds have been established using available rainfall and landslide data for 2004–2014. The calculated threshold relationship is fitted to the lower boundary of the rainfall conditions leading to landslides and plotted in logarithmic coordinates. The results show that a rainfall event of 24 h with a cumulated rainfall of 53 mm can cause landslides. Later on, the outcome of antecedent rainfall varying from 3–30 days was also analysed to understand its effect on landslide incidences based on cumulative event rainfall. It is also observed that a minimum 10-day antecedent rainfall of 88 mm and a 20-day antecedent rainfall of 142 mm is required for landslide occurrence in the area. The thresholds presented can be improved with the availability of hourly rainfall data and the addition of more landslide data. These can also be used as an early warning system especially along the Phuentsholing–Thimphu Highway to prevent any disruptions of trade.

scholarly journals The optimal rainfall thresholds and probabilistic rainfall conditions for a landslide early warning system for Chuncheon, Republic of Korea

Landslides ◽  
2021 ◽  
Author(s):  
Won Young Lee ◽  
Seon Ki Park ◽  
Hyo Hyun Sung
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Bayesian Model ◽  
Warning System ◽  
Probability Of Detection ◽  
Republic Of Korea ◽  
Maximum Rainfall ◽  
Landslide Occurrence ◽  
Hourly Rainfall ◽  
Landslide Early Warning

AbstractThe purpose of this study is to establish the criteria for a landslide early warning system (LEWS). We accomplished this by deriving optimal thresholds for the cumulative event rainfall–duration (ED) and identifying the characteristics of the rainfall variables associated with a high probability of landslide occurrence via a Bayesian model. We have established these system criteria using rainfall and landslide data for Chuncheon, Republic of Korea. Heavy rainfall is the leading cause of landslides in Chuncheon; thus, it is crucial to determine the rainfall conditions that trigger landslides. Hourly rainfall data spanning 1999 to 2017 from seven gauging stations were utilized to establish the ED thresholds and the Bayesian model. We used three different calibration periods of rainfall events split by 12, 24, 48, and 96 non-rainfall hours to calibrate the ED thresholds. Finally, the optimal threshold was determined by comparing the results of the contingency table and the skill scores that maximize the probability of detection (POD) score and minimize the probability of false detection (POFD) score. In the LEWS, by considering the first level as “normal,” we developed subsequent step-by-step warning levels based on the Bayesian model as well as the ED thresholds. We propose the second level, “watch,” when the rainfall condition is above the ED thresholds. We then adopt the third level, “warning,” and the fourth level, “severe warning,” based on the probability of landslide occurrence determined via a Bayesian model that considers several factors including the rainfall conditions of landslide vs. non-landslide and various rainfall variables such as hourly maximum rainfall and 3-day antecedent rainfall conditions. The proposed alert level predicted a total of 98.2% of the landslide occurrences at the levels of “severe warning” and “warning” as a result of the model fitness verification. The false alarm rate is 0% for the severe warning level and 47.4% for the warning level. We propose using the optimal ED thresholds to forecast when landslides are likely to occur in the local region. Additionally, we propose the ranges of rainfall variables that represent a high landslide probability based on the Bayesian model to set the landslide warning standard that fits the local area’s characteristics.

scholarly journals Brief communication: Using punctual soil moisture estimates to improve the performances of a regional scale landslide early warning system

2017 ◽  
Author(s):  
Samuele Segoni ◽  
Ascanio Rosi ◽  
Daniela Lagomarsino ◽  
Riccardo Fanti ◽  
Nicola Casagli
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Early Warning System ◽  
Regional Scale ◽  
Back Analysis ◽  
Warning System ◽  
Threshold Value ◽  
False Alarms ◽  
Rainfall Thresholds ◽  
Landslide Early Warning

Abstract. We improved a state-of-art RSLEWS (regional scale landslide early warning system) based on rainfall thresholds by integrating punctual soil moisture estimates. We tested two approaches. The simplest can be easily applied to improve other RSLEWS: it is based on a soil moisture threshold value under which rainfall thresholds are not used because landslides are never expected to occur. Another approach deeply modifies the original RSLEWS: thresholds based on antecedent rainfall accumulated over long periods were substituted by soil moisture thresholds. A back analysis demonstrated that both approaches reduced consistently false alarms, while the second approach reduced missed alarms as well.

scholarly journals Development of a Landslide Early Warning System in Indonesia

Geosciences ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 451 ◽  
Author(s):  
Rokhmat Hidayat ◽  
Samuel Jonson Sutanto ◽  
Alidina Hidayah ◽  
Banata Ridwan ◽  
Arif Mulyana
Keyword(s):  
Slope Stability ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Economic Losses ◽  
Precipitation Data ◽  
Threshold Method ◽  
Landslide Occurrence ◽  
Daily Precipitation Data ◽  
Landslide Early Warning

Landslides are one of the most disastrous natural hazards in Indonesia, in terms of number of fatalities and economic losses. Therefore, Balai Litbang Sabo (BLS) has developed a Landslide Early Warning System (LEWS) for Indonesia, based on a Delft–FEWS (Flood Early Warning System) platform. This system utilizes daily precipitation data, a rainfall threshold method, and a Transient Rainfall Infiltration and Grid-based Regional Slope-stability model (TRIGRS) to predict landslide occurrences. For precipitation data, we use a combination of 1-day and 3-day cumulative observed and forecasted precipitation data, obtained from the Tropical Rainfall Measuring Mission (TRMM) and the Indonesian Meteorological Climatological and Geophysical Agency (BMKG). The TRIGRS model is used to simulate the slope stability in regions that are predicted to have a high probability of landslide occurrence. Our results show that the landslides, which occurred in Pacitan (28 November 2017) and Brebes regions (22 February 2018), could be detected by the LEWS from one to three days in advance. The TRIGRS model supports the warning signals issued by the LEWS, with a simulated factor of safety values lower than 1 in these locations. The ability of the Indonesian LEWS to detect landslide occurrences in Pacitan and Brebes indicates that the LEWS shows good potential to detect landslide occurrences a few days in advance. However, this system is still undergoing further developments for better landslide prediction.

scholarly journals A Regional-Scale Landslide Early Warning System Based on the Sequential Evaluation Method: Development and Performance Analysis

2020 ◽  
Vol 10 (17) ◽  
pp. 5788
Author(s):  
Joon-Young Park ◽  
Seung-Rae Lee ◽  
Yun-Tae Kim ◽  
Sinhang Kang ◽  
Deuk-Hwan Lee
Keyword(s):  
Performance Analysis ◽  
Early Warning ◽  
Debris Flows ◽  
Early Warning System ◽  
Evaluation Method ◽  
Regional Scale ◽  
Warning System ◽  
High Class ◽  
Landslide Early Warning ◽  
Sequential Evaluation

A regional-scale landslide early warning system was developed in collaboration with a city government. The structure and distinctive features of the system are described in detail. This system employs the principles of the sequential evaluation method that consecutively applies three different evaluation stages: statistical, physically based, and geomorphological evaluations. Based on this method, the system determines five phases of warning levels with improved levels of certainty and credibility. In particular, the warning levels are systematically derived to enable the discrimination of slope failures and debris flows. To provide intuitive and pragmatic information regarding the warning capabilities of the system, a comprehensive performance analysis was conducted. Early warning level maps were generated and a historical landslide database was established for the study period from 2009 to 2016. As a result, 81% of historical slope failures and 86% of historical debris flows were correctly predicted by high-class warning levels. Miscellaneous details associated to the timing efficiency of warnings were also investigated. Most notably, five high-class warning level events and four landslide events were recorded for a study region during the eight-year period. The four landslide events were all successfully captured by four out of the five warning events.

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.

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