scholarly journals Satellite-derived rainfall thresholds for landslide early warning in Bogowonto Catchment, Central Java, Indonesia

2020 ◽  
Vol 89 ◽  
pp. 102093 ◽  
Author(s):  
Elias E. Chikalamo ◽  
Olga C. Mavrouli ◽  
Janneke Ettema ◽  
Cees J. van Westen ◽  
Agus S. Muntohar ◽  
...  
Keyword(s):  
Early Warning ◽  
Rainfall Thresholds ◽  
Central Java ◽  
Landslide Early Warning

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 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 Brief communication: Rainfall thresholds based on Artificial neural networks can improve landslide early warning

2021 ◽  
Author(s):  
Pierpaolo Distefano ◽  
David J. Peres ◽  
Pietro Scandura ◽  
Antonino Cancelliere
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Performance Improvement ◽  
Early Warning ◽  
Rainfall Intensity ◽  
Rainfall Event ◽  
Rainfall Thresholds ◽  
Event Duration ◽  
Landslide Early Warning ◽  
Artificial Neural

Abstract. In this communication we show how the use of artificial neural networks (ANNs) can improve the performance of the rainfall thresholds for landslide early warning. Results for Sicily (Italy), show how performance of a traditional rainfall event duration and depth power law threshold, yielding a true skill statistic (TSS) of 0.50, can be improved by ANNs (TSS = 0.59). Then we show how ANNs allow to easily add other variables, like peak rainfall intensity, with a further performance improvement (TSS = 0.64). This may stimulate more research on the use of this powerful tool for deriving landslide early warning thresholds.

scholarly journals IoT-Based Geotechnical Monitoring of Unstable Slopes for Landslide Early Warning in the Darjeeling Himalayas

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2611 ◽  
Author(s):  
Minu Treesa Abraham ◽  
Neelima Satyam ◽  
Biswajeet Pradhan ◽  
Abdullah M. Alamri
Keyword(s):  
Water Content ◽  
Early Warning ◽  
False Alarms ◽  
Data Logger ◽  
Rainfall Thresholds ◽  
Number Of False Alarms ◽  
The World ◽  
Landslide Early Warning ◽  
Tilt Sensors ◽  
Darjeeling Himalayas

In hilly areas across the world, landslides have been an increasing menace, causing loss of lives and properties. The damages instigated by landslides in the recent past call for attention from authorities for disaster risk reduction measures. Development of an effective landslide early warning system (LEWS) is an important risk reduction approach by which the authorities and public in general can be presaged about future landslide events. The Indian Himalayas are among the most landslide-prone areas in the world, and attempts have been made to determine the rainfall thresholds for possible occurrence of landslides in the region. The established thresholds proved to be effective in predicting most of the landslide events and the major drawback observed is the increased number of false alarms. For an LEWS to be successfully operational, it is obligatory to reduce the number of false alarms using physical monitoring. Therefore, to improve the efficiency of the LEWS and to make the thresholds serviceable, the slopes are monitored using a sensor network. In this study, micro-electro-mechanical systems (MEMS)-based tilt sensors and volumetric water content sensors were used to monitor the active slopes in Chibo, in the Darjeeling Himalayas. The Internet of Things (IoT)-based network uses wireless modules for communication between individual sensors to the data logger and from the data logger to an internet database. The slopes are on the banks of mountain rivulets (jhoras) known as the sinking zones of Kalimpong. The locality is highly affected by surface displacements in the monsoon season due to incessant rains and improper drainage. Real-time field monitoring for the study area is being conducted for the first time to evaluate the applicability of tilt sensors in the region. The sensors are embedded within the soil to measure the tilting angles and moisture content at shallow depths. The slopes were monitored continuously during three monsoon seasons (2017–2019), and the data from the sensors were compared with the field observations and rainfall data for the evaluation. The relationship between change in tilt rate, volumetric water content, and rainfall are explored in the study, and the records prove the significance of considering long-term rainfall conditions rather than immediate rainfall events in developing rainfall thresholds for the region.

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.

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