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Abeykoon, A Gedara Tharindu Bhagya Banda, Gallage, Chaminda, Da-reeju, Biyanvilage, & Trofimovs, Jessica(2018)Real-time monitoring and wireless data transmission to predict rain-induced landslides in critical slopes.Australian Geomechanics Journal, 53(3), pp. 61-76.

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REAL-TIME MONITORING AND WIRELESS DATA TRANSMISSION TO

PREDICT RAIN-INDUCED LANDSLIDES IN CRITICAL SLOPES

Tharindu Abeykoon, Chaminda Gallage, Biyanvilage Dareeju and Jessica Trofimovs

Queensland University of Technology, Brisbane, Australia

ABSTRACT

Real-time landslide monitoring is an effective technique to minimise landslide risks, especially in circumstances where

the potential for structural countermeasures is limited. Rainfall infiltration is considered as one of the most significant

factors triggering slope instability. Hence real-time monitoring of parameters: rainfall, volumetric water content and

surface deformations/displacements in the soil, enable the early detection of landslides, thus reducing the adverse impacts

of landslides. This study involves low cost and simply installable miniature ground inclinometers equipped with MEMS

(Micro Electro Mechanical Systems) tilt sensors, volumetric water content sensors, temperature sensors, a rain gauge and

a wireless data transmission unit (DTU) for the prior identification of possible slope failure. The DTU receives data from

sensor units via radio signal transmission at a higher data acquisition frequency and automatically transmits them via the

mobile network to an internet server, and updates in an online web interface for the determination of slope instability.

The monitoring programme in operation for more than two years in the Lake Baroon Catchment, Maleny plateau,

Australia, accurately captured both creep movement of the slope with wetting and drying cycles and mass movements

triggered by rainfall. The current study analysed the surface deformation and rainfall data produced by the real-time

monitoring system and validated results using published study outcomes. Combination of rainfall data, I-D threshold

equations and ground tilting rate was hence identified as a more suitable measure to detect possible slope failure in

advance. Further, a precaution be issued at tilting rate 0.010/hr, and a warning at 0.10/hr is recommended by this study

along with the consideration of rainfall data.

1. INTRODUCTION

Triggering of landslides depends on external factors such as earthquakes and seismicity, rainfall and ground saturation

and internal factors such as local topographical, geological and hydrological conditions (Chae and Kim 2012). Rainfall is

considered to be the most significant triggering mechanism of landslides in which matric suction of soil is reduced by

rainfall precipitation, reducing the shear strength of soil and increasing the susceptibility of slope failure (Lee, Gofar and

Rahardjo 2009). Numerous prevention and mitigation methodologies have been established to reduce the impact of

rainfall-induced landslides. Even though retaining walls, soil nailing, soil stabilisation, ground anchors and dewatering

techniques are used in the prevention of slope failures; the application is limited to large-scale slopes due to the higher

cost of installation and environmental constraints (Towhata, Uchimura and Gallage 2005; Orense et al. 2004). However,

the historical data claims that majority of landslides take place on small-scale slopes (Towhata and Uchimura 2013).

Therefore enhancing factor of safety of the slope by mechanical reinforcements has lesser adequacy in landslide

mitigation (Towhata and Uchimura 2013). In such circumstances, non-structural mitigation methods such as landslide

monitoring and early warning systems (EWSs) become the foremost countermeasure for landslides (Towhata and

Uchimura 2013).

EWSs focus on real-time observations of specific parameters along with historical data to predict the real-time likelihood

of hazard occurrence, after which warnings are produced to mitigate the possible risks of imminent slope instability based

on pre-established thresholds of calculated risks (Greco et al. 2010). Most of the existing monitoring systems against

rainfall-induced landslides are based on rainfall data, where rainfall thresholds have been defined on diverse geological

and climatic conditions (Martelloni et al. 2012). In such studies, researchers attempted to evaluate the application of real-

time rainfall data along with defined rainfall thresholds to produce landslide warnings, as in the Malaysian Peninsula

(Lee, Gofar and Rahardjo 2009) and Emilia Romagna, Italy (Martelloni et al. 2012). Such studies extensively focused

on the correlation of the slope stability by rainfall related parameters such as rainfall intensity, duration and cumulative

precipitation (Greco et al. 2010). Conversely, physical based EWSs associate the real-time rainfall events to the physical

processes triggered by the rainfall such as infiltration, surface runoff and evapotranspiration (Greco et al. 2010). Such

physical models require an experimental understanding of the soil behaviour under rainwater percolation and logical setup

to identify the susceptibility of slope failure (Towhata, Uchimura and Gallage 2005). However due to the complexity of

parameters involved in the physical based analysis, only simplified physical models can be incorporated in rain-induced

landslide EWSs.

Monitoring rainfall-induced slope failures were further enhanced to include ground movement and pore-water pressure

(soil moisture) to produce more advanced early warning systems (Toll et al. 2011). According to the outcome of a series

of laboratory experiments, Orense et al. (2004) claimed that volumetric water content and the inclination of the slope are

the prime factors in determining slope stability. Remote sensing, extensometer, and inclinometer have been implemented

to measure the ground movement, while piezometers, water content sensors, and suction sensors to monitor the dynamic

change of hydraulic characteristics of soil (Arbanas and Tofani 2017; Sassa, Picarelli and Yueping 2009). Capturing

ground movement by remote sensing techniques such as satellite imagery and aerial photography is easy to implement.

However, the temporal and spatial resolution required in landslide risk reduction may not be provided by such approaches.

Also, use of extensometers in detecting surface deformations can be costly and may require a significantly large area for

installation, enabling detection of tensile stress (Towhata, Uchimura and Gallage 2005).

Hence accounting for the cost of the monitoring system, reliability of the outcome, and limited locations of monitoring,

Uchimura et al. (2010) proposed a simple monitoring system, which incorporates Micro Electro Mechanical System

(MEMS) tilt sensors and volumetric water content sensors. This advanced system can measure the rotation of ground, for

detecting the pre-failure stages of shallow landslides, and the change of volumetric water content and temperature of the

soil. The performance of this new monitoring system was documented on both artificial and natural slopes under artificial

heavy rainfall conditions by Wang et al. (2016) and Uchimura et al. (2015). Lin et al. (2017) further argued that the total

cost of this proposed monitoring system could be reduced by one third, compared with the traditional monitoring systems,

and further, recommended increasing the number of installed sensors, thus increasing the accuracy of the early warning

thresholds and predictions.

Rainfall threshold is also another parameter accounted for in a number of empirical and physical based models(Saito,

Nakayama and Matsuyama 2010). Caine (1980), Jibson (1989), Aleotti (2004), Guzzetti et al. (2007) and Guzzetti et al.

(2008) investigated on the impact of rainfall intensity and duration (I-D), cumulative event rainfall and antecedent rainfall

in landslide initiation. Caine (1980) claimed that predicting landslide occurrence needs to be incorporated not only the

rainfall precipitation but also the rainfall infiltration to the ground. However, Aleotti (2004) and Guzzetti et al. (2008)

presented the applicability of I-D threshold in landslide prediction. Numerous global, regional and local scale I-D

threshold equations were developed and rescaled to foresee possible landslide occurrences and further, equations were

validated to ensure the applicability by associating actual landslide occurrence (Saito, Nakayama and Matsuyama 2010).

This study investigated the applicability of real-time monitoring and wireless data transmission in predicting rain-induced

slope instability in critical slopes. Similar to the studies conducted by Uchimura et al. (2015), Wang et al. (2016) and Lin

et al. (2017), this study also involved simply installable miniature ground inclinometers equipped with MEMS tilt sensors,

volumetric water content sensors, a rain gauge and a wireless data transmission unit (DTU) for real-time slope monitoring.

The study employed a wide range of data collected in the period from 10th May 2016 to 31st May 2018, for the prediction

of the slope failure under rainfall infiltration. Further, the study evaluated landslide events captured by the real-time

monitoring system using the “tilting rate – time before slope failure/stability” relationship developed by Lin et al. (2017)

and I-D threshold equations developed by Caine (1980), Jibson (1989) and Guzzetti et al. (2008). Aforementioned

evaluations and the higher frequency of data acquisition extensively confirmed the applicability of low-cost real-time

monitoring system for accurate and timely determination of rain-induced slope instability.

2. STUDY AREA

Lake Baroon catchment, Maleny (Figure 1) is located approximately 100 km north of Brisbane (26.76 0S 152.85 0E).

Mapleton – Maleny plateau, which contains Lake Baroon catchment have been documented and discussed since the mid

- 1950s as a highly susceptible area for rainfall-induced slope failure. (e.g. Ellison and Coaldrake (1954); Willmott

(1983)). Slope failure and mass movement of sediment into the waterways within the Lake Baroon catchment are

recognised as a significant risk to water quality and the water storage capacity of Lake Baroon, which is used to supply

water to South East Queensland. Approximately 170 mass movement landforms have been identified within the Baroon

catchment, and the study area is one such high-risk slope. This landslide site hosted a voluminous, single-failure rotational

landslide in 2008 following heavy rainfall. The pre-2008 landslide topography was subsequently reset by pushing failed

soil and colluvium back onto the original slope. Vegetation (planting and growing trees) was suggested as an effective

slope stabilisation method for this area. Additionally, the five inclinometer slope monitoring experiment was installed.

The real-time slope monitoring system aimed to measure the efficacy of revegetation as a slope stabilisation method for

this slope.

Figure 1: Study Area

The selection of the monitoring sensor locations was a crucial challenge in the experiment design. A surface ground

survey and a sub-surface ground penetrating radar (GPR) survey were performed to determine surface topography, depth

of soil cover on the site and further to evaluate the optimum positions for the monitoring stations. The GPR surveys were

designed to survey laterally across the Landslide site at its top, middle and toe (Figure 2). A long GPR transect (Line 17

+ Line 18; Figure 2) was also mapped downslope from the head of the main scarp to the landslide toe in an attempt to

profile the sub-surface in the direction of the original mass movement. After characterising the soil profile by determining

the interface between soil and underlying bedrock by GPR survey, four locations were selected to excavate pits for

determining the composition of soil layers, soil layer thicknesses and verification of GPR survey results. Figure 3(A) and

3(B) illustrate the longitudinal GPR profile for Line 17 + Line 18 and the GPR survey transect line and a cross-section of

soil profile along the transect line with the locations of excavation pits, respectively.

- Monitoring Site

Figure 2: GPR survey transect lines and direction of each transect

Figure 3: (A) Longitudinal GPR profile (Lines 17 and 18) (B) Cross-section from the above GPR profile, showing the

position of the white clay/bedrock reflector (dashed line)

The soil profile within the bulk of the landslide (top to base) is generally structure-less, exhibiting a mottled black, to

brown, to orange appearance within the excavated pits, which is consistent with mixing of the original soil profile and

landslide debris during the 2008 landslide remediation work. It was further observed that this clay layer overlies sandstone

bedrock at the top, middle, and base of the landslide, and confirming pre-slope failure was located along the contact

between the soil profile and sandstone bedrock, as shown in Figure 3(B). Therefore, after characterising surface

topography, and sub-surface soil profiles using the GPR and excavation pits, five points were identified as the critical

locations to install sensors to monitor the movement of the slope.

3. REAL-TIME MONITORING SYSTEM AND ITS INSTALLATION

The real-time monitoring system that consists of five sensor units (TS1, TS2, TS3, TS4, and TS5) and a central logging

station was installed in the slope as shown in Figure 4. Each sensor unit consists of a logging and transmission unit,

MEMS tilt sensor, volumetric soil moisture sensor, and temperature sensor. The central unit comprises a central data

logger, power supply unit (solar panel and back-up battery), data receiving unit (from sensor units), rain gauge, and data

transmission unit (DTU). The DTU receives data from sensors units via radio signal transmission at 10-minute intervals

and automatically transmits them via mobile network and to an internet server, as illustrated in Figure 5. Each sensor unit

has a mini-SD card to save its data while sending them to the central logging station. The central unit saves data received

from each sensor unit and rain gauge on its SD card, while also sending them to the server. This method ensures that data

is saved at least 2-3 locations to reduce the risk of data loss due to technical errors. The server receives the data every 10

minutes which can be viewed in real-time via an online web interface.

Figure 4: Locations of the sensors including the orientations of the X- and Y direction of accelerometer movement. X

denotes the local downslope direction, whereas Y denotes the direction perpendicular to downslope

Figure 5: Schematic illustration of sensor data transmission process

Figure 6 depicts components of a sensor unit (TS). The transmission - logging unit was mounted on a steel pipe (Figure

6(A)) at the height of about 150 cm above the ground level to minimise the radio data transmission interruption caused

by grass and vegetation. The bi-axial tilt sensor (accelerometer) was attached to L-steel iron, which has embedded

Internet

Server

approximately 1 m into the ground (Figure 6(C)). An EC-5 sensor which has been calibrated to measure volumetric soil

moisture and soil temperature were installed in soil at about 20-30 cm below the ground surface (Figure 6(B)). Both the

EC-5 sensor and the tilt sensor were wire-connected to the logging unit as shown in Figure 6(A). The bi-axial tilt sensor

has been calibrated to measure the rotation of XY plane (horizontal plane). X-direction follows the downward slope

direction while Y- direction is orthogonal to X-direction as shown in Figure 4. Figure 7 depicts the definitions of +ve and

–ve rotation of X-axis and the way of calculating the horizontal movement along X-direction using the angle of rotations

and the embedment length (D = 1 m). Each sensor unit (TS) records X-rotation, Y-rotation, soil moisture content, soil

temperature, and the battery level of the unit and send them to the central unit.

Figure 6: Sensor unit: (A) transmission and logging unit, (B) Water content sensor, (C) Accelerometer (Tilt sensor), (D)

Batteries used for the sensor unit

(A) (B)

(C)

(D)

Figure 7: Definition of X rotation (+ve & –ve) and surface deformation along the slope

Figure 8 illustrates the central unit consisting a data logger, data transfer device (DTU), rain gauge, 20 W solar panel, and

cabinet to house the backup batteries (Two 12 V car batteries connected parallel). The central unit was mounted at about

1 m above the ground surface. It collected the data from the sensor units and rain gauge and saved them in a SD card

while the DTU transfer the data at every 10 minutes to a server located at Tokyo via a wireless telecommunication network

(Telstra) and internet. The real-time (with 10 minutes delay) data can be viewed online as shown in Figure 9 and arbitrary

data can be downloaded for the post-processing.

Figure 8: Central unit - (A) Final setup, (B) Data logger and DTU, (C) Solar panel and rain gauge

(A) (B)

(C)

Figure 9: Online display of real-time data from the monitoring site

4. DATA ANALYSIS AND FAILURE PREDICTION

4.1 REAL-TIME MONITORING DATA

The real-time data monitoring was started on 10th May 2016 and the data has been received every 10 minutes since then

with some interruption to real-time data transfer to the server due to power failure of the central unit (3 months: 1st

December 2016 to 6th March 2017) and due to the failure of DTU (6 weeks: 7th November 2017 to 20th December 2017).

Therefore, in this study, data from MEMS tilt sensors (TS1, TS2, TS3, TS4 and TS5) and rain-gauge are considered to

determine the applicability of the real-time angle of rotation to predict and warn of rain-induced slope failure.

Figure 10 illustrates the time histories of the angle of rotation (tilt) in “X” and “Y” directions and soil volumetric water

contents captured at the sensor units, together with rainfall during the monitoring period. TS1 and TS2 have been active

in showing both positive and negative rotations (tilts) about both axes (X and Y) in response to drying and wetting (rain).

Such rotations (tilts) can be interpreted as the soil mass movement along the down-slope (X-direction) and the direction

normal to the slope (Y-direction), as shown in Figure 7.

Figure 10: Time histories of X & Y axes inclinations and volumetric water content at the sensor units along with daily

rainfall from 10th May 2010 to 31st May 2018

4.2 FAILURE PREDICTION

Results obtained from real-time monitoring system depicted TS1 and TS2 are the most susceptible regions for slope

instability as three rain-induced slope failures have occurred during this monitoring period on 18th October 2017 (Figure

11), 16th February 2018 (Figure 12) and 7th March 2018 in the vicinity of TS1 and TS2 respectively. Hence TS1 and TS2

sensor data were subjected for determination of failure prediction criterion.

Figure 11: Areal view of the landslide area (28/10/2017)

(A) (B)

(C) (D)

Main landslide

TS1

TS2

TS3

(A)

(B)

(C)

(D)

Figure 12: View of the landslide area (20/02/2018)

(A)

(B)

From the time histories of X and Y axis inclinations at TS1 and TS2, inclination (tilting) rate (0/hr) was calculated.

Subsequently, notable surface deformations captured by the real-time monitoring system were filtered based on the

inclination rates. Table 1 depicts such instances with corresponding volumetric water content and rainfall parameters.

According to the information presented in Table 1, it is evident that the real-time monitoring system had accurately

captured the actual slope failures took place on 18th October 2017, 16th February 2018 and 7th March 2018. However, no

significant ground deformation was associated with failure captured on 7th March 2018. Further, information

corresponding to aforementioned dates possessed significantly higher volumetric water content and cumulative rainfall

over the pre-failure period. Conversely, even though the sensors captured notable inclination rates on 7th November 2017

and 20th December 2017, no actual failures in the slope were identified, which is extensively supported by the rainfall

data since both daily rainfalls, and cumulative rainfall was deficient compared to the instances of actual slope failures.

Together the results provide valuable insights into the fact that, accurate prediction of slope failure has to be associated

with ground deformation along with rainfall parameters, rather than depending on one such parameter.

Table 1: Significant ground displacements captured from real-time monitoring system from 10th May 2016 to 31st May

2018

As shown in Table 1, the site received significant rainfall during the six-day- period (from 13th to 18th of October 2017),

and it caused the landslide shown in Figure 11. TS1, which is located within the failed area, tilted (rotated) more than 20

in X-direction and more than 10 in Y-direction during this period (Figure 13). TS2 which is located outside the failed area

did not respond to the failure of the slope. However, TS2 started showing minor rotation with the reactivation of the

failure above its location, which could be due to overloading the area of TS2 by the failed soil mass above its location

(Figure 14).

Figure 13: Time histories of X & Y axes inclinations and volumetric moisture content at TS1 during 14th – 23rd of

October 2017

Figure 14: Time histories of X & Y axes inclinations and volumetric moisture content at TS2 during 14th – 23rd of

October 2017

Similarly, sudden massive rainfall during 16th February 2018 triggered a slope instability (Figure 12), which was captured

by TS2 as more than 1.50 inclination in X-direction was recorded (Figure 15). Compared to the slope failure on 18th

October 2017, 16th February 2018 failure was instantaneous. A drier period of almost three months followed by a

significantly substantial rainfall triggered the slope failure, the increasing volumetric water content of the soil as well.

Also due to the spontaneous nature of the failure, no pre-failure was captured by the monitoring system.

Pre-failure Main failure Reactivation

Minor deformation

Figure 15: Time histories of X & Y axes inclinations and volumetric moisture content at TS2 during 12th – 18th of

February 2018

During the period of 7th to 17th March 2018, more than 20 X-axis inclination was recorded in TS2 (Figure 16). However,

the tilting process expanded throughout the ten-day period. Hence the observed tilt rate was comparatively smaller to

other slope failures captured by the monitoring system. Similar to the earlier cases of slope failure, significant rainfall

(Table 1) was recorded before the failure.

Figure 16: Time histories of X & Y axes inclinations and volumetric moisture content at TS2 during 7th – 17th of March

2018

The saturated volumetric water content of the soil in the site is approximately 60%, and the degree of saturation of soil at

about 20 cm below the surface reaches about 90 - 95% (volumetric water content of about 54% - 57%) during rainfall to

initiate the movement of the soil in the slope.

Main failure

Analysis of rainfall intensity during actual slope failures also indicate that significantly higher rainfall intensities have

been encountered in the pre-failure period. These heavy rainfalls had developed a positive pore water pressure, reducing

the shear strength and thus increasing the susceptibility of slope failure. Therefore significantly higher rainfall intensities

can also be used in identifying possible forthcoming slope failure. Guzzetti et al. (2007) and Guzzetti et al. (2008)

concluded that I-D thresholds often used to predict landslide occurrences and as a measure of landslide early warning. In

studies by Caine (1980), Jibson (1989) and Guzzetti et al. (2008) developed I-D threshold equations to predict global

landslide occurrences (Table 2). Hence this study validated the applicability of I-D threshold equations for real-time

monitoring data at the Lake Baroon Catchment, Maleny plateau, Australia (Figure 17).

Table 2: I-D threshold for the world

Figure 17: Validation of the applicability of I-D threshold equation using real-time monitoring data

Figure 17 illustrates that the I-D threshold equations defined by Guzzetti et al. (2008) accurately determine the three

landslide events occurred in the study area on 18th October 2017 (LS-1), 16th February 2018 (LS-2) and 7th March 2018

(LS-3). However, I = 0.48D-0.11 threshold equation did not correspond to LS-2 as the duration limit did not fall within the

predefined range. Compared to LS-1 and LS-3, LS-2 was instantaneous as the duration of the failure process is small,

which also depicted by the variation of the tilt angle (Figure 15). Even though LS-1 almost behaved at the threshold

conditions of Caine (1980), overall I-D threshold equations developed by Caine (1980) and Jibson (1989) failed to predict

the Maleny landslide events.

As shown in Figure 18 (Lin et al. 2017), presented the use of the inclination (tilting) and the time to failure/stabilisation

to predict the failure or to warn against the rain-induced slope failures. According to this study, the slopes tend to fail

during rainfall when the tilting rate is greater than 0.1o/hr. The tilting rate of 0.01o/hr to 0.1o/hr is considered as “caution”

for the slope failure. In this study, an attempt was made to verify the applicability of this method to predict and warn of

failure of the monitored slope. The time histories of tilt in X and Y directions at TS1 in the period from the 14th – 23rd of

October 2017 (Figure 13), time histories of tilt in X and Y directions at TS2 in the period from the 12th – 18th of February

2018 (Figure 15) and the time histories of X-direction at TS2 in the period from the 7th – 17th of March 2018 (Figure 16)

were used to calculate the maximum rate of tilt and corresponding time to failure/stabilisation for captured events: pre-

failure, main failure, and reactivation and the results are included in Figures 19 to 22 respectively. The calculated

maximum tilt rate at the pre-failure was 0.05o/hr, and it is consistent with the observation of Lin et al. (2017) which the

slope was about to fail (“caution”). The maximum tilting rates for 18th October 2017 failure during the main failures and

the reactivation were calculated as 0.25 o/hr and 0.16o/hr, respectively, and were corresponding to the observed slope

failures. Even though the failure captured on 7th March limited to “caution” stage (Figure 22), slope failure on 16th

February was also consistent with the summarised case studies illustration by Uchimura et al. (2015), and Lin et al. (2017).

However, the limitation of 7th March failure to “caution” stage was further supported by the minimal ground movement

encountered at the failure. Therefore, this study further verifies that the rate of tilt of 0.1o/hr as the acceptable value to

issue warning against the rain-induced slope failures.

Figure 18: Graphical illustration of the tilting rate as a function of time before slope failure/stabilisation ((Lin, et al.,

2017))

Figure 19: Graphical illustration of the tilting rate of X-Axis as a function of time before slope failure/stabilisation for

the failure period from 14th -23rd October 2017

Figure 20: Graphical illustration of the tilting rate of Y-Axis as a function of time before slope failure/stabilisation for

the failure period from 14th -23rd October 2017

Figure 21: Graphical illustration of the tilting rate as a function of time before slope failure/stabilisation for the failure

period from 12th -18th February 2018

Figure 22: Graphical illustration of the tilting rate of X-Axis as a function of time before slope failure/stabilisation for

the failure period from 7th – 17th March 2018

5. CONCLUSIONS

In this study, a real-time slope monitoring system consisting of tilt sensors, soil moisture sensors, and rain gauge was

installed to monitor the stability of a critical slope. Slope failures occurred during the monitoring period and were

considered as a case study for the use of the real-time measuring tilt (rotation) of slope and the soil moisture content for

predicting and warning of rain-induced slope failure. The study leads to the following conclusions:

Accurate determination of possible slope failure has to be incorporated with ground deformation and rainfall

parameters. Mono parameter dependent systems have a higher probability of producing faulty warnings, thus

disrupting human lives and yielding economic losses. Associating higher number of real-time measuring

parameters will increase the accuracy and reliability of the predictions of warnings. However, results derived

from study proves that the volumetric water content, rainfall and the inclination of the slope are the prime factors

in determining slope stability. Hence cost-effective slope instability is feasible by the proposed method.

A slope failure will initiate when the degree of soil saturation reaches 90% or above.

The rate of tilt angle could be an effective parameter to issue warnings for precaution and evacuation against

rain-induced slope failures.

The slopes tend to fail due to rainfall when its rate of tilt angle is greater than 0.1o/hr.

The rainfall intensity-duration thresholds for initiation of slope failure (I-D thresholds) based on the historical

slope failure data can also be used to assess slope failure, with an exception to instantaneous slope failures

triggered by intense rainfall events in shorter duration. However, the applicability of proposed I-D threshold

equations has to be determined by considering more landslide events. Therefore further studies on I-D thresholds

are recommended to derive a local scale I-D threshold equation for Maleny plateau.

6. ACKNOWLEDGEMENTS

This research was supported by an Australian Government Research Training Program scholarship, and authors

acknowledge Queensland University of Technology (QUT) for giving the opportunity to conduct the research. Also,

authors appreciate Seqwater, Australia, for their financial support to this study. They thank Mark Amos (Manager, Lake

Baroon Catchment Care Group) and Craig Ling, (the property owner of the monitoring site) for facilitating the installation

of the monitoring system and providing site access at the convenience QUT researchers. The management of Chuo

Kaihatsu Corporation, Japan is greatly acknowledged for providing and installing the monitoring system for free of charge

as research support to QUT.

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