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 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.

Landwarn: An Operative Early Warning System for Landslides Forecasting Based on Rainfall Thresholds and Soil Moisture

2013 ◽  
pp. 627-634 ◽  
Author(s):  
Francesco Ponziani ◽  
Nicola Berni ◽  
Marco Stelluti ◽  
Renato Zauri ◽  
Claudia Pandolfo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Rainfall Thresholds

scholarly journals Definition of 3D rainfall thresholds to increase operative landslide early warning system performances

Landslides ◽  
2020 ◽  
Author(s):  
Ascanio Rosi ◽  
Samuele Segoni ◽  
Vanessa Canavesi ◽  
Antonio Monni ◽  
Angela Gallucci ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Rainfall Thresholds ◽  
Landslide Early Warning ◽  
Definition Of

scholarly journals A Novel Air Quality Early-Warning System Based on Artificial Intelligence

2019 ◽  
Vol 16 (19) ◽  
pp. 3505
Author(s):  
Mo ◽  
Zhang ◽  
Li ◽  
Qu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Prediction Model ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Air Pollutant ◽  
Concentration Data ◽  
Whale Optimization ◽  
The World

The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

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.

scholarly journals IMPROVING MODIFIED ICOLD METHOD WITH LOSS OF LIFE INDEX FOR DAM SAFETY RISK ASSESSMENT

2020 ◽  
Vol 11 (2) ◽  
pp. 79-93
Author(s):  
A. Henrianto ◽  
R.W. Triweko ◽  
D. Yudianto
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Action Plan ◽  
Risk Index ◽  
Warning System ◽  
Regression Equation ◽  
Dam Safety ◽  
Security Risks ◽  
The World ◽  
Level Of Risk

This research paper explains the results of the prediction analysis of the number of lives lost in the event of a catastrophic dam collapse in Indonesia as a further consideration in assessing the level of risk of dam safety. The proposed procedure is to make a new prediction index of the number of lives lost (LoL) as the development of a risk index of evacuation requirements from Risk Affected Populations (PENRIS), on the Modified ICOLD Method which is always used in Indonesia. This study, resulting in a regression equation as a correlation between PENRIS and LoL, takes its source from various catastrophic dam collapse events that have occurred in the world including Indonesia. Furthermore the regression equation is integrated with the standard determination of the level of risk of dam safety used in Indonesia and the world, for conditions with and without a disaster early warning system based on the Graham formula (2010). Further analysis of the Emergency Action Plan (EAP or RTD) of 16 dams in Indonesia as a sample, gives an indication that the implementation of an early warning system will reduce the amount of LoL by almost 100% if implemented according to design. This research, with its focus on developing a prediction index for the number of LoL, proves that in Indonesia, where there are still many dams eventhough they already have RTDs, and have not conducted a disaster-based space arrangement based on predicted LoL numbers,the reduction in the value of dam security risks can only be optimal in the range of 50 % of the total dam studied.

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 Diseño de un sistema de alerta temprana de tsunamis para el cantón General Villamil Playas, provincia del Guayas

2016 ◽  
Vol 3 (3) ◽  
pp. 10-18
Author(s):  
Leonardo Muñoz Montesdeoca ◽  
Josué Pérez Moncayo ◽  
Freddy Villao Quezada
Keyword(s):  
Software Design ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Tsunami Early Warning ◽  
El Sistema ◽  
Naval Base

El presente estudio se refiere al diseño de un sistema de alerta temprana de tsunamis en el cantón General Villamil, Playas, monitorizado y controlado desde el Instituto Oceanográfico de la Armada (INOCAR), ubicado en la Base Naval Sur de la ciudad de Guayaquil. En el mismo se detalla el diseño del sistema, la tecnología de transmisión seleccionada con los terminales más idóneos que serán necesarios instalar, la determinación de los lugares en el cantón General Villamil Playas, provincia del Guayas, donde se colocarán las sirenas electrónicas, así como el tipo de red que se usará para activar los dispositivos acústicos. Adicionalmente, se describen las características principales de las sirenas requeridas para el sistema, se detalla el diseño del software que administra remotamente los terminales acústicos así como la comunicación GSM entre sirenas mediante la utilización de arduino, y se calcula la cobertura de cada una de las sirenas del sistema de alerta temprana.AbstractThis study refersto the tsunami early warning system in canton General Villamil Playas, monitored and controlled by the Navy Oceanographic Institute, located in the Southern Naval Base in Guayaquil city. It details the design of the system, the transmission technology selected with the most suitable terminals that will be necessary to install, the determination of the places in the canton General Villamil Playas, Guayas province, where the electronic horns will be located, as well as the type of network that will be used to activate the acoustic devices. Additionally, the main features of the horns required for the system are described, the software design that remotely manages the acoustic terminals as well as the GSM communication between horns through the use of arduino are detailed, and the coverage of each horn of the tsunami early warning system is calculated. Keywords: Arduino, GSM, GPRS, satellites, tsunamis.

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