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Understanding landslide risks is important for people living in hilly areas in India. A promising way of communicating landslide risks is via simulation tools, where these tools integrate both human factors (e.g., public investments to mitigate landslides) and environmental factors (e.g., spatial geology and rainfall). In this paper, we develop an interactive simulation model on landslide risks and use it to design a web-based Interactive Landslide Simulator (ILS) microworld. The ILS microworld is based on the assumption that landslides occur due to both environmental factors (spatial geology and rainfall) as well as human factors (lack of monetary investments to mitigate landslides). We run a lab-based experiment involving human participants performing in ILS and we show that the ILS performance helps improve public understanding of landslide risks. Overall, we propose ILS to be an effective tool for doing what-if analyses by policymakers and for educating public about landslide risks.
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Interactive Landslide Simulator: A Tool
for Landslide Risk Assessment
and Communication
Pratik Chaturvedi, Akshit Arora and Varun Dutt
Abstract Understanding landslide risks is important for people living in hilly areas
in India. A promising way of communicating landslide risks is via simulation tools,
where these tools integrate both human factors (e.g., public investments to mitigate
landslides) and environmental factors (e.g., spatial geology and rainfall). In this
paper, we develop an interactive simulation model on landslide risks and use it to
design a web-based Interactive Landslide Simulator (ILS) microworld. The ILS
microworld is based on the assumption that landslides occur due to both environ-
mental factors (spatial geology and rainfall) as well as human factors (lack of
monetary investments to mitigate landslides). We run a lab-based experiment
involving human participants performing in ILS and we show that the ILS per-
formance helps improve public understanding of landslide risks. Overall, we pro-
pose ILS to be an effective tool for doing what-if analyses by policymakers and for
educating public about landslide risks.
Keywords Early warning systems Interactive landslide simulator (ILS)
Landslide risk communication Feedback Learning
P. Chaturvedi (&)
Defence Terrain Research Laboratory (DTRL), Metcalfe House,
Delhi 110054, India
e-mail: prateek@dtrl.drdo.in
P. Chaturvedi V. Dutt
Applied Cognitive Science Laboratory, Indian Institute of Technology (IIT) Mandi,
Mandi 175001, H.P, India
e-mail: varun@iitmandi.ac.in
A. Arora
Thapar University, Patiala 147004, Punjab, India
e-mail: akshit.arora1995@gmail.com
©Springer International Publishing Switzerland 2017
V.G. Duffy (ed.), Advances in Applied Digital Human Modeling
and Simulation, Advances in Intelligent Systems and Computing 481,
DOI 10.1007/978-3-319-41627-4_21
231
1 Introduction
Over the past few decades, catastrophic and disastrous effects of landslides have
caused extensive damage to life, property, and public utility services world over.
Thus, ensuring effective Early Warning Systems (EWSs) for landslides is essential
for the survivability of people in case of occurrence of a disastrous event. To be
effective, EWSs need to have not only a sound scientic and technical basis, but
also a strong focus on people, who are actually exposed to risk. Unfortunately, such
risk communication systems only address part of the existing challenge; the other
important part being related to the properties of human perceptual-cognitive factors
(cooperation; attitude; and effects of economic and educational background) [13].
Moreover, recent surveys in developing countries (like India) show only mediocre
knowledge and awareness about causes and consequences of landslide disasters
among the general public [46]. For example, a recent survey conducted in Mandi
town of Himachal Pradesh, India, showed a big gap between experts and general
public on understanding of hazard zonation maps and probability of landslides [4].
The existence of this gap is problematic because zonation maps, developed by
landslide experts, are currently the common medium to communicate the suscep-
tibility of a region to landslides.
An important aspect of EWSs is related to the development, evaluation, and
improvement of risk communication, which helps in transferring risk related
knowledge (like causes, consequences and what to do in case a disaster event takes
place) and warnings in a manner easily understandable to the local community.
A promising way of improving existing risk communication among EWSs is via
simulation tools (also called microworlds), which are able to integrate human factors
in landslide risk mitigation in addition to physical factors. Such simulation tools, and
the models they are built upon, could help risk managers since personal experience
and the visibility of processes are the two main inuencing factors for improving
peoples mental models about natural disasters. Promising recent research has shown
that regular feedback from a system likely provides an effective tool for people to
improve their understanding about the system (Dutt and Gonzalez 2011, 2012). For
example, research has documented some benets of repeated feedback in computer-
based microworlds in reducing peoples misconceptions about Earths climate [7].
Dutt and Gonzalez (2012) developed Dynamic Climate Change Simulator (DCCS)
microworld and used it as an intervention to help participants understand basic
characteristics of the climate system [8]. DCCS helped provide feedback to people
about their decisions and enabled them to reduce their misconceptions compared to
no DCCS intervention. As DCCS-like tools seem to be effective in improving
peoples understanding on problems, there is a need to develop simulation models
that are able to integrate human factors in landslide risk mitigation in addition to
physical factors. Such simulation models could be helpful for the risk managers since
personal experience and the visibility of processes are the two main inuencing
factors explaining the content of peoplesmentalmodels[9,10].
232 P. Chaturvedi et al.
Furthermore, affect or emotional response to stimuli is seen to inuence risk
perception and decision making [11,12]. For example, Finucane et al. [12] have
provided the affect heuristic,where this heuristic allows people to make decisions
and solve problems quickly and efciently, in which current emotions of fear,
pleasure, and surprise inuences decisions [12]. According to Finucane et al. [12],
the orientation of ones feelings (negative or positive) could be an effective tool for
risk communication [12].
In the present work, an interactive simulation model of landslide is developed for
understanding the inuence of monetary contributions for landslide risk mitigation.
Furthermore, the interactive simulation model is used to design a web-based
Interactive Landslide Simulator (ILS) too. The ILS tool is based on the assumption
that landslides occur due to the presence of both physical factors (spatial geology
and rainfall) as well as human factors (monetary investments made for landslide
risk mitigation). Thus, even in the presence of physical factors (which are outside of
ones control), the landslide risk could be reduced by increasing community
investments towards landslide mitigation. Beyond considering human factors in the
landslide problem, the ILS also models the damages due to the occurrence of
landslide events in terms of fatality, injury, and loss of property. It considers how
such damages might impact ones daily income as well as property wealth. In
summary, the ILS tool allows participants to make decisions on the landslide risk
mitigation, observe the consequences of their decisions (via real-time feedback),
and enable participants to try new decisions.
In this paper, we highlight the use of the interactive landslide model as well as
the ILS tool in educating the general public about landslide risks. Specically, we
use affect heuristic in the ILS by creating affect-rich feedback to enable people to
perceive risks and benets for investments made against landslides. This will also
enable them to develop a deeper causal understanding about landslide disasters and
their consequences. Our hypothesis is that monetary contributions against land-
slides (which is an indicator of improved understanding) will be larger when
affective feedback about monetary losses is high compared to low. Based on results
of a lab-based experiment involving human participants, we propose a number of
benets of the ILS tool for educating people and for policymaking in terms of
generating what-ifanalyses. As part of our outreach activities, we plan to pop-
ularize the use of the ILS tool among students in K-12 schools and colleges in
mountain areas in India that are prone to landslide risks.
2 Interactive Simulation Model of Landslides
2.1 Interactive Landslide Simulator (ILS) Model
The ILS model focuses on calculation of total probability of landslides (due to
natural factors and due to anthropogenic factors, i.e., investments made by people
Interactive Landslide Simulator: A Tool for Landslide 233
against landslides). The model is also capable of simulating types of damages
caused by landslides and their effects on peoples earnings.
Figure 1shows the model of ILS proposed in the present research work. In this
model, the probability of landslide is calculated as a weighted sum of probability of
landslide due to environmental factors and probability of landslide due to peoples
investments. Probability of landslide due to environmental (natural) factors is a
combination of probability of landslide due to rainfall and probability of landslide
due to slope and soil properties. Model also simulated the losses caused due to
occurrence of landslide events.
The Calculation of Total Probability of Landslides.
Total probability of landslide = W * P(I) + 1 - WðÞ* P(E)ðÞð1Þ
where W is the weight factor, which is between [0, 1]. In the model, W have been
assigned a value = 0.8, which indicates that investments against landslides will
cause the system to respond rapidly and reduce the probability of landslide.
Fig. 1 Probabilistic model of the interactive landslide simulator microworld
234 P. Chaturvedi et al.
The total probability formula involves calculation of two probabilities, P(I) and
P(E), which is described below:
Probability of Landslide Due to Investment P(I). The calculation used here is
based on expected payoff equation used in Hasson (2009) [13], i.e.,
PðIÞ¼1MPn
i¼1xi
nBð2Þ
where,
B Budget available towards addressing landslide (if a person earns a daily
income or salary, then B is the same as this daily income or salary).
n Number of time periods (days). In the default formulation of the game, n = 60
simulated days, i.e., the game is played for 60 simulated days.
x
i
Investments made by a person at each day i to mitigate landslides; x
i
B,
M Return to Mitigation, which captures the lower bound probability of P(I)
when P
n
i¼1
xi¼nB, i.e., people invest their entire daily income in mitigating
landslides.
P(I) Probability of landslide after an investment is made.
Probability of Landslide Due to Natural Factors P(E). Natural factors include
rainfall, soil type, slope prole, etc. These can be categorized into two parts:
Probability of landslide due to rainfall (P(T))
Probability of landslide due to soil type, slope prole etc. [spatial probability,
P(S)]
The approach used to calculate both of them is based on a research paper [14].
Equation used for calculation of probability of landslide due to rainfall (P(T)):
z¼3:817 + DR * 0:077 + 3 DCR * 0:058 + 30 DAR * 0:009 ð3Þ
fðzÞ=1
1+ezð4Þ
z: 1to þ1;P:0to1
The logistic regression retains the daily (DR), 3-day cumulative (3 DCR) and
30-day antecedent rainfall (30 DAR) as signicant predictors inuencing slope
failure. P(T) = f(z), that is the temporal probability of landslide. The rainfall data
was collected as raw data from NASAs TRMM project, from January 1, 2004 to
April 30, 2013.
Now,
P(E) = P(T) * P(S) = f (z) * P(S) ð5Þ
Interactive Landslide Simulator: A Tool for Landslide 235
Damage Modeling. The damage caused can be classied into 3 categories:
(a) Property Loss
(b) Injury
(c) Fatality
All 3 of them have different kinds of effects on the players wealth and income in
the simulator. The data used for calculating probabilities of the above damage has
been obtained from Parkash [15]. The stochastic nature of landslide occurrence and
damages caused by it have thus also been considered. The exact assumptions about
damages are detailed ahead in this manuscript.
2.2 Interactive Landslide Simulator (ILS) Microworld
Computer-based decision-making tasks have spread across disciplines and different
levels of education [16]. Furthermore, these decision-making tasks have been long
used in the study of dynamic decision making behaviour (also called Microworld,
see Gonzalez et al. [17]), and many more specialized tasks have been created to
provide decision makers with practice and training in organizational systems
control; also called Management Flight Simulators [18,19].
ILS microworld is a computer-based task, where a decision makersgoalisto
maximize ones economic level. The economic level (dened by wealth due to
income and property in ILS microworld) is inuenced by exogenous environment
circumstances (spatial and temporal conditions) and the past decisions made by
humans. The economic level may decrease (by damages caused due to landslides,
like injury, death or property damage) or increase (due to daily income and property
wealth). However, the exact functional form governing these increases or decreases
was unknown to decision makers. Decision makers could only observe the values
that occurred in the previous time period. The level of wealth at time t depends
upon the previous time period t 1, a characteristic of dynamic systems called
interdependency [20]. Also inherent in dynamic systems are feedback loops, where
one observes the effect that one variable has on itself and others. Feedback loops
can be positive or negative, self-reinforcingor self-correcting[21]. Both types
of loops are present in ILS because decision makers make repeated investments so
as to increase or decrease their economic level.
Figure 2represents graphical user interface of ILS, which requires a decision
maker controlling her economic level and keep it up as much as possible. The
economic level is represented graphically as curve of Property wealthand Total
income not invested in landslidesversus number of times investment decision has
been made (since the decision made is one per day the graph is plotted against
number of days passed in the simulator). The plot of Total probability of landslide
236 P. Chaturvedi et al.
versus number of days describes the cumulative effect of this variable on probability
of landslide. The Game Parameterstable on the right hand side describes specic
values like daily income, property wealth, probability of landslide, and damages
due to landslide.
A decision-maker must enter the investment input in the text eld specied on
top left of the screen. The investment can only be made between zero (minimum) to
the players current daily income (maximum). Once investment is made, the
decision-maker can observe changes in the daily income, property wealth, and
damages caused due to landslide [loss of daily income (due to death and injury) and
loss of property wealth].
After a decision-maker enters the investment decision and clicks on the Invest
button, the system provides feedback on whether a landslide occurred or not. If a
landslide occurs, then the system decides what kind of damage the landslide has
caused and the resulting economic level is shown as a loss via a negative feedback
screen (see Fig. 3). If landslide did not occur, however, a positive feedback screen
is shown to the decision maker (see Fig. 4). The user can get back to investment
decision screen by clicking on Return To Gamebutton.
Fig. 2 ILS microworld graphical user interface [game] (source http://pratik.acslab.org)
Interactive Landslide Simulator: A Tool for Landslide 237
Returning to game causes the player to come back to the main graphical user
interface of ILS (see Fig. 2). Once a player has played multiple days in ILS (where
the end-point is not known), the interface shows the amount of income and property
wealth left at the end of the game. Although ILS is made to capture the dynamics of
landslides, the tool can actually be deployed for other natural calamities so long as
the geological data related to those calamities is available. Lastly, we have setup
ILS as a web-application; therefore, it is accessible anywhere in the world at any
time and on any web-browser compatible computing device.
Fig. 3 ILS microworlds negative feedback screen where a landslide has occurred (source http://
pratik.acslab.org)
Fig. 4 ILS microworlds positive feedback screen where a landslide did not occur (source http://
pratik.acslab.org)
238 P. Chaturvedi et al.
3 ILS Experiment: Testing Affective Feedback in ILS
In order to showcase the effectiveness of the ILS tool, we performed a lab-based
experiment where we used ILS with human participants. In this experiment, we
manipulated the feedback, i.e., the effect of landslides on a persons income and
property wealth using two different conditions: high-affect condition (i.e., high
probability of death, injury, and property due to a landslide) and low-affect con-
dition (low probability of death, injury, and property due to a landslide). The
expectation was that participants will invest more and improve their understanding
about landslides in the high-affect condition compared to the low-affect condition.
These conditions and results are explained in greater detail below.
3.1 Methods
Experimental Design. Participants were randomly assigned to one of the two
between-subjects conditions: high-affect and low-affect. In both conditions, par-
ticipants were given daily income and were as asked to make daily investment
decisions. In high-affect condition, the probability of property damage, fatality and
injury were set as 10, 3, and 30 %, respectively. In low-affect condition, the
probability of property damage, fatality and injury were 3, 1, and 10 %, respec-
tively. The goal was to maximize the net wealth (coming from property wealth and
daily income combined) over multiple rounds of ILS (where the end-point was not
known to participants). The nature of functional forms used in ILS were unknown
to participants, and participants simply observed the values of the probability of
human factors and natural factors and all the damages occurring in an event of a
landslide.
The amount of damage (in terms of daily income and property wealth) that
occurs in an event of fatality, injury and property damage was kept constant in both
the affect conditions. The property wealth decreased to ½of its value every time
property damage occurred in an event of a landslide. The daily income was reduced
by 10 % of its latest value in case of injury and 20 % of its latest value in case of
fatality loss. The initial property wealth was xed to INR 2 million, which is the
expected property wealth in Mandi district of Himachal Pradesh. The initial daily
income of the person was kept 292 INR (taking into account the GDP and
per-capita income of Himachal Pradesh, India where the study was carried out). The
time duration of the simulation was 30 days (this duration was not known to
participants). Weight of human factors in probability of landslide (W) was xed
to 0.8 and that of natural factors (1 W) was xed to 0.2. The W value was known
to participants on the graphical user interface.
Interactive Landslide Simulator: A Tool for Landslide 239
We used decision makers average investment ratio as a dependent variable for
the purpose of data analysis. The average investment ratio was dened as the ratio
of investment made to total investment possible averaged across all participants and
days. On account of Affect heuristic [12], we expected the average investment ratio
to be greater in the high-affect condition compared to in the low-affect condition.
Participants. Forty-three participants at Indian Institute of Technology Mandi
from diverse elds of study participated in the experiment. There were 20 partic-
ipants in high-affect condition and 23 participants in low-affect condition to yield a
medium to large effect size (= 0.5) in our results (for Alpha = 0.05 and a
Power = 0.80). All participants were students from Science, Technology,
Engineering, and Mathematics (STEM) backgrounds and their ages ranged in
between 21 and 28 years (Mean = 23.54; Standard Deviation = 4.08).
Twenty-eight participants were Masters students, 5 were Ph.D. students, and 10
were B. Tech. students. When asked about their previous knowledge about land-
slides, 20 participants mentioned having a basic understanding, 16 having little
understanding, 5 being knowledgeable, and 3 having no idea. All participants
received a base payment of INR 50 and an additional bonus according to their
performance in the task.
Procedure. Participants were recruited via an online advertisement circulated
via an email at Indian Institute of Technology Mandi. Experimental sessions about
30 min long per participant. Participants were randomly assigned to one of the two
conditions, and they were given instructions on the computer before entering the
ILS microworld. Participants were encouraged to ask questions after reading
instructions. Participants were not given any information concerning the nature of
environment or conditions in the microworld. They were told that their goal was to
maximize their nal income and they were then asked to play ILS for 30 days.
3.2 Results
Data were analyzed for all participants in terms of their average investment ratio in
both the high-affect as well as low-affect condition. The result was as per our
expectation: Average investment ratios were signicantly higher in high-affect
condition compared to those in the low-affect condition (see Fig. 5).
As shown in Fig. 5, participants had lesser investment ratios in low-affect
condition(M = 0.38, SE = 0.05) compared to those in high-affect condition
(M = 0.67, SE = 0.045) [t(41) = 4.1, p< 0.05, r= 0.54]. Thus, our hypothesis
related to affect heuristic was satised with these results from ILS.
240 P. Chaturvedi et al.
4 Discussion and Conclusions
One way of improving existing risk communication practices for landslides is by
training people about these risks via simulation tools. That is because personal
experience and the visibility of processes are the two main factors for improving
peoples understanding and seriousness about natural disasters. Interactive
Landslide Simulator (ILS) is an interactive simulation model, which could be used
by policymakers to do what-if analyses. ILS can also be used as an educational tool
for the general public to increase their understanding and awareness about
landslides.
In order to showcase the performance of ILS in improving publics perception
towards landslide risk, we conducted an experiment involving 43 students of dif-
ferent educational backgrounds and made them invest against landslides for
30 days in different affective conditions. As expected, the result from the experi-
ment suggested that participants making investment decisions in high-affect con-
dition invested much higher than the participants doing the same task in low-affect
condition. This result can be explained by previous lab-based research on use of
repeated feedback or experience (Cronin et al. 2009; Dutt and Gonzalez 2011) and
affect heuristic (Fischoff 2001; Finucane et al. [12]. Affect heuristic allows people to
make decisions and solve problems quickly and efciently, in which current
emotions of fear, pleasure, and surprise inuences decisions. As the emotional
feedback is higher in high-affect condition, participants have made higher monetary
contributions in this case. Thus, ILS has exhibited success in terms of improving
publics seriousness and awareness towards landslide risk. In future, various other
system-response parameters (e.g. w or M), feedback (e.g. numbers, text messages
and images for damage) will be varied to study their effect on publics decision
making. Here, we would like to evaluate affect and its ability to increase public
contributions in the face of other system-response parameters.
Other uses of ILS include packaged education material for classroom and
workshops that accounts for factors like feedback, affect, and social norms. We
believe that such material, when tailored to specic individuals, will help improve
decision making of individuals against landslides. ILS tool can be used for
Fig. 5 Average investment
ratio in low- and high-affect
conditions
Interactive Landslide Simulator: A Tool for Landslide 241
communicating landslide risk in other landslide prone states by customizing the
spatial probability (based on geology, soil properties etc.) and temporal probability
(based on rainfall) of landslides in such areas. In future, we also plan to use ILS to
understand the effects of social norms on peoples investment decisions towards
mitigation of landslide risk.
Acknowledgments This research was partially supported by Thapar University, Patiala and
Indian Institute of Technology, Mandi, India. The authors thank Akanksha Jain and Sushmita
Negi, Centre for Converging Technologies, University of Rajasthan for their contribution in
collection of human data.
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Interactive Landslide Simulator: A Tool for Landslide 243
... Educating people about landslide causes-and-consequences is likely to be an effective way to manage landslide risks in affected areas (Gwee, Shaw, & Takeuchi, 2011;Chou, Yang, & Ren, 2015;Coles, 2011;Xia et al., 2016). However, prior research involving communities in landslide-prone areas have shown a lack of awareness and understanding among people about landslide risks (Chaturvedi & Dutt, 2015). For example, Chaturvedi and Dutt (2015) surveyed common people in Mandi, India, a Himalayan township frequented by landslides. ...
... However, prior research involving communities in landslide-prone areas have shown a lack of awareness and understanding among people about landslide risks (Chaturvedi & Dutt, 2015). For example, Chaturvedi and Dutt (2015) surveyed common people in Mandi, India, a Himalayan township frequented by landslides. These authors found that 60% of people surveyed were not able to answer questions on landslide susceptibility maps, which were prepared by experts. ...
... These authors found that 60% of people surveyed were not able to answer questions on landslide susceptibility maps, which were prepared by experts. Recent research in system dynamics shows experiential feedback in simulation tools to improve people's understanding of the dynamics of physical systems (Chaturvedi, Arora, & Dutt, 2017;Kumar & Dutt, 2018;. For example, Dutt and Gonzalez (2012) developed a dynamic climate change simulator (DCCS) tool, which was based upon a more generic stock-and-flow task ( Gonzalez & Dutt, 2011). ...
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Landslide disasters cause massive damages in hilly areas around the world. Thus, improving understanding of landslides among people is critical. Prior research has investigated the effectiveness of computer games in causing learning from feedback. However, an evaluation of the effectiveness of computer games for landslide education has been less explored. The primary objective of this research was to examine the efficacy of a landslide snakes-and-ladders (LSL) game for landslide education. In a field experiment in Mandi, India (a Himalayan town), participants were randomly divided into two between-subject conditions: control (N = 13) and intervention (N = 21). In the control condition, participants were given a set of 15-questions concerning causes-and-effects of landslides. In the intervention condition, participants played two rounds of an LSL game in quick succession. In each game round in LSL, at the encounter of a snake or a ladder, a randomized question from the set of 15-questions would appear. If the question was answered correctly, then participants climbed the ladder or were not affected by the snake bite. If participants responded to the question wrong, then they were told that their answer was “wrong” and they could not climb the ladder, or the snake bit them. Results revealed that performance was significantly better in the intervention condition compared to the control condition and performance improved across the two rounds of LSL in the intervention condition. We highlight the potential of using games like the LSL as pedagogical tools that help enhance understanding about landslides among ordinary people.
... This feedback enabled people to reduce their misconceptions about climate change. In a similar way, references [8][9] developed an Interactive Landslide Simulator (ILS) tool based on the hypothesis that experience and recency of events through a simulation exercise, may be influential in improving the public's awareness and perceptions about landslide disasters [10,11]. Results revealed that repeated feedback in ILS improved the people's decision-making against landslide risks [8][9]. ...
... In a similar way, references [8][9] developed an Interactive Landslide Simulator (ILS) tool based on the hypothesis that experience and recency of events through a simulation exercise, may be influential in improving the public's awareness and perceptions about landslide disasters [10,11]. Results revealed that repeated feedback in ILS improved the people's decision-making against landslide risks [8][9]. ...
... Although the use of feedback in improving learning in simulation tools has been explored across a variety of domains [6][7][8][9][10][11], there is less literature on how social norms, i.e., an acceptable behavior in a particular group, influences learning in simulation tools. Prior literature [15,20,22] suggests that social norms may play a significant role in shaping people's decision-making in the real-world. ...
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Landslide disasters, i.e., movement of hill mass, cause significant damages to life and property. People may be educated about landslides via simulation tools, which provide simulated experiences of cause-and-effect relationships. The primary objective of this research was to test the influence of social norms on people’s decisions against landslides in an interactive landslide simulator (ILS) tool. In a lab-based experiment involving ILS, social norms were varied across two between-subject conditions: social (N = 25 participants) and non-social (N = 25 participants). In social condition, participants were provided feedback about investments made by a friend against landslides in addition to their investments. In non-social condition, participants were not provided feedback about friend’s investments, and they were only provided feedback about their investments. People’s investments were significantly higher in the social condition compared to the non-social condition. We discuss the benefits of using the ILS tool for educating people about landslide risks.
... Catastrophic and disastrous effects of landslides cause extensive damage to life, property, and public-utility services [1]. Landslides and associated debris flows are a major concern for disaster-prevention groups in regions with steep terrain, especially in Himalayan mountains [2][3][4]. Several physical factors like ground slope, soil depth, and rainfall may precipitate landslides. Besides these physical causes of the landslide, land development and other man-made activities also aggravate landslide disasters [2][3][4][5]. ...
... Several physical factors like ground slope, soil depth, and rainfall may precipitate landslides. Besides these physical causes of the landslide, land development and other man-made activities also aggravate landslide disasters [2][3][4][5]. The consequences of extreme natural events like landslides are a combination of both physical factors as well as the actions taken by people before landslides occur (human factors). ...
... The ILS tool is an interactive dynamic system for studying people's decisions against landslide risks [2,3]. Details about the ILS tool were already discussed by [2,3], and here we briefly cover the tool's working. ...
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Landslides cause extensive damages to property and life and there is an urgent need to increase community awareness against landslide risks. Interactive simulations help to provide people with experience of landslide disasters and increase community awareness. However, it would be interesting to evaluate the influence of contextual feedback via messages and images in people's decision-making in these simulations. The main objective of this paper was to evaluate the role of contextual feedback in an interactive landslide simulator (ILS) tool. ILS considers both human and environmental factors to influence landslide risks. Fifty participants randomly participated across two between-subject conditions in the experiment: feedback-rich (messages and images present) and feedback-poor (numeric feedback only; messages and images absent). Participants made repeated monetary decisions against landslides in ILS. Investments were greater in the feedback-rich condition compared to feedback-poor condition. We highlight the implications of our results for awareness against landslide risks.
... landscape grading, crop cultivation) [5,16]; changing rainfall patterns and intensity over the region [5,17,18]. In spite of the challenge, it is important to understand the landslide risk exposure for people living in such hilly areas [17,19,20], because, this helps them in planning and increases their preparedness. It is also necessary to raise awareness in order to reduce the adverse impacts to lives (e.g. ...
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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.
... Landslides are uncertain geological events and they pose great dangers to life and infrastructure ( Parkash, 2011). In India, especially in the Himalayan region, landslides are more frequent than any other geological phenomena causing more than 200 deaths and, on average, $82 million in damages to infrastructure yearly ( Chaturvedi, Arora, & Dutt, 2017;Chaturvedi & Dutt, 2015). Because of large costs and many deaths due to landslides, there is a need to design and develop frameworks that monitor landslides and alert people before they occur. ...
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A landslide, that is, collapse of a mass of earth or rock from a mountain or cliff, is a common phenomenon in hills. Landslides pose a large threat to life and infrastructure and there is a need to develop low‐cost sensing frameworks that could help in monitoring landslides and alert people before they occur. Certain existing technologies have been used for monitoring landslides (e.g., the use of unmanned aerial vehicle‐based remote sensing). The Internet of Things (IoT) technologies could provide alternate solutions for monitoring landslides. However, existing IoT technologies are diverse and expensive to use. Thus, there is a need for developing low‐cost IoT frameworks for monitoring landslides, especially in developing countries. This chapter proposes a microelectromechanical system (MEMS)‐based IoT framework for sensing landslides at the lab‐scale. The proposed IoT framework offers a promising low‐cost solution for monitoring landslides with a large deployment potential in landslide‐prone areas.
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Research indicates that people continue to exhibit “wait-and-see” preferences towards climate change, despite constant attempts to raise awareness about its cataclysmic effects. Experiencing climatic catastrophes via simulation tools have been found to affect people's perception of climate change and promote pro-environmental behaviors. However, not much is known about how experiential feedback and probability of climate change in a simulation influences people’s decision. We developed a web-based tool called Interactive Climate Change Simulator (ICCS) to study the impact of different probabilities of climate change and the availability of feedback on the monetary actions (adaptation or mitigation) taken by individuals. A total of 160 participants from India voluntarily played ICCS across four between-subject conditions (N= 40 in each condition). The conditions differed based on the probability of climate change (low or high) and availability of feedback (absent or present). Participants made mitigation and adaptation decisions in ICCS over multiple years and faced monetary consequences of their decisions. There was a significant increase in mitigation actions against climate change when the feedback was present compared to when it was absent. The mitigation and adaptation investments against climate change were not significantly affected by the probability of climate change. The interaction between probability of climate consequences and availability of feedback was significant: In the presence of feedback, the high probability of climate change resulted in higher mitigation and adaptation investments against climate change. Overall, the experience gained in the ICCS tool helped alleviate peoples 'wait-and-see' preferences and increased the monetary investments to counter climate change. Simulation tools like ICCS have the potential to increase peoples' understanding of climatic disasters and can act as a useful aid for educationalists and policymakers.
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Prior research has used an Interactive Landslide Simulator (ILS) tool to investigate human decision-making against landslide risks. It has been found that repeated feedback in ILS tool about damages due to landslides causes an improvement in human decisions against landslide risks. However, little is known on how theories of learning from feedback (e.g., reinforcement learning) would account for human decisions in the ILS tool. The primary goal of this paper is to account for human decisions in the ILS tool via computational models based upon reinforcement learning and to investigate the underlying cognitive processes involved when people make decisions in the ILS tool. Four different reinforcement-learning models were developed and evaluated in their ability in capturing human decisions in an experiment involving two conditions in the ILS tool. The parameters of an Expectancy-Valence (EV) model, two Prospect-Valence-Learning models (PVL and PVL-2), a combination EV-PU model, and a random model were calibrated to human decisions in the ILS tool across the two conditions. Later, different models with their calibrated parameters were generalized to data collected in an experiment involving a new condition in ILS. When generalized to this new condition, the PVL-2 model’s parameters of both damage-feedback conditions outperformed all other RL models (including the random model). We highlight the implications of our results for decision-making against landslide risks.
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Feedback via simulation tools is likely to help people improve their decision-making against natural disasters. However, little is known on how differing strengths of experiential feedback and feedback's availability in simulation tools influence people's decisions against landslides. We tested the influence of differing strengths of experiential feedback and feedback's availability on people's decisions against landslides in Mandi, Himachal Pradesh, India. Experiential feedback (high or low) and feedback's availability (present or absent) were varied across four between-subject conditions in a tool called the Interactive Landslide Simulation (ILS): high damage with feedback present, high damage with feedback absent, low damage with feedback present, and low damage with feedback absent. In high-damage conditions, the probabilities of damages to life and property due to landslides were 10 times higher than those in the low-damage conditions. In feedback-present conditions, experiential feedback was provided in numeric, text, and graphical formats in ILS. In feedback-absent conditions, the probabilities of damages were described; however, there was no experiential feedback present. Investments were greater in conditions where experiential feedback was present and damages were high compared to conditions where experiential feedback was absent and damages were low. Furthermore, only high-damage feedback produced learning in ILS. Simulation tools like ILS seem appropriate for landslide risk communication and for performing what-if analyses.
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To investigate how differing amounts of experiential feedback and feedback’s availability in an interactive simulation tool influences people’s decision-making against landslide risks. Feedback via simulation tools is likely to help people improve their decisions against disasters; however, currently little is known on how differing amounts of experiential feedback and feedback’s availability in simulation tools influences people’s decisions against landslides. We tested the influence of differing amounts of experiential feedback and feedback’s availability on people’s decisions against landslide risks in an Interactive Landslide Simulation (ILS) tool. In an experiment, in high-damage conditions, the probabilities of damages to life and property due to landslides were 10-times higher than those in the low-damage conditions. In feedback-present condition, experiential feedback was provided in numeric, text, and graphical formats in ILS. In feedback-absent conditions, the probabilities of damages were described; however, there was no experiential feedback present. Investments were greater in conditions where experiential feedback was present and damages were high compared to conditions where experiential feedback was absent and damages were low. Furthermore, only high-damage feedback produced learning in ILS. Experience gained in ILS enables people to improve their decision-making against landslide risks. Simulation tools seem appropriate for landslide risk communication and for performing what-if analyses.
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Landslide experts have developed very detailed landslide hazard maps for different parts of Himalayas in India. These maps indicate the types of damage possible and the probabilities of adverse events. Six categories of risk severity are defined on the maps, ranging from severe risk to very low risk. Based on the existing maps, we selected respondents for a survey, some from areas high in risk and others from low-risk regions. Respondents answered several questions related to landslide risk perception and preparedness. Survey results showed a lack of awareness about the scientific causes of landslides among Mandi residents. Most of the respondents were of the belief that they lived at a safe place. Survey results suggested that many inhabitants did not know that landslide hazard maps existed for their region and most of them were not able to understand them. People overestimated the risks associated with landslides. Consequently, some people were more worried of landslides than was justified by the facts. Another important finding was that since catastrophic landslides are rare events, most of the people were risk averse. These people did not show prevention behaviour, and they were not well prepared for an adverse event. Furthermore, results suggest that respondents’ experiences with landslides were positively related to their perceptions of landslide risk. Thus, findings of the present study comply with the concept of availability heuristic.
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