the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A Scenario-based Case Study: AI to analyse casualties from landslides in Chittagong Metropolitan Area, Bangladesh
- 1Federal Government, Melbourne, Australia, VIC 3000
- 2Business Continuity Management & Integrated Emergency Management, Rabdan Academy, Abu Dhabi, UAE
- 3Department of Geography and Environmental Studies, University of Chittagong, Chittagong-4331, Bangladesh
- 4Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh
- 1Federal Government, Melbourne, Australia, VIC 3000
- 2Business Continuity Management & Integrated Emergency Management, Rabdan Academy, Abu Dhabi, UAE
- 3Department of Geography and Environmental Studies, University of Chittagong, Chittagong-4331, Bangladesh
- 4Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh
Abstract. Understanding the complex dynamics of landslides is crucial for disaster planners to make timely and effective decision that saves lives and reduces the economic impact on society. Using the landslide inventory of Chittagong Metropolitan Area (CMA), we created a new Artificial Intelligence (AI) based insight system for the town planners and senior disaster recovery strategists of Chittagong, Bangladesh. Our system generates dynamic AI-based insights for a range of complex scenarios created from 7 different landslide feature attributes. The users of our system can select a particular kind of scenario out of the exhaustive list of 1.054X1041 possible scenario sets and our AI-based system will immediately predict how many casualties are likely to occur based on the selected kind of scenario. Moreover, an AI-based system shows how landslide attributes (e.g., rainfall, area of mass, elevation, etc.) correlate with landslide casualty by drawing detailed trend lines performing both linear and logistic regressions. According to literature and the best of our knowledge, our CMA scenario-based AI insight system is the first of its kind providing the most comprehensive understanding of landslide scenarios and associated deaths and damages in CMA. The system was deployed on a wide range of platforms including Android, iOS, and Windows systems so that it could be easily adapted to strategic disaster planners. The deployed solutions were handed down to 12 landslide strategists and disaster planners for evaluations whereby 91.67 % of users found the solution easy to use, effective and self-explanatory while using via mobile.
Fahim Sufi et al.
Status: open (until 19 Jun 2022)
-
AC1: 'Comment on nhess-2022-90', Edris Alam, 05 Apr 2022
reply
An AI-based system shows how landslide attributes (e.g., rainfall, area of mass, elevation, etc.) correlate with landslide casualty by drawing detailed trend lines performing both linear and logistic regressions
-
CC1: 'Comment on nhess-2022-90', Fatima Zohra, 09 Apr 2022
reply
It looks like a very interesting paper. Specially, I concur with the motivation for this paper which is to allow the strategic decision makers on the run to make evidence based instant decisions using their mobile devices. Provided the fact that most of the AI based insight generation research focused primarily on traditional desktop platforms, I can see the usability of the presented system on mobile Apps based solution. This brings me to the following enquiries:
- Are there any other mobile App and AI based landslide analysis systems in existing literature?
- While traditional desktop platforms could handle resource intensive computational demands by AI algorithms, how does miniature mobile devices perform on executing AI algorithms, described within this paper?
- Figure 11 to Figure 14 shows several mobile deployed solutions. Are these web applications (i.e., cloud-based solutions being executed through mobile browser) or deployed mobile apps in iOS/ Android?
-
CC3: 'Reply on CC1', Fahim Sufi, 11 Apr 2022
reply
We are grateful that you found our paper interesting. You are absolutely right about the motivation of this paper. Traditionally, the decision makers had to rely on data scientists to analyze data and to generate AI driven insights (which is time consuming). However, our unique solution allows the mobile phone to automatically select the right AI algorithms to execute on the right set of scenario-driven data. The insights are presented in the decision makers mobile phone in a natural language (meaning data scientists are not required to interpret the AI driven insights).
This research would enable decision makers to harness the power of AI on their mobile devices. Hence, using the AI driven insights, the decision makers can make informed and timely decisions.Â
I appreciated all your valid queries. Following are responses to your three queries:
1) There are very few studies on mobile App based disaster management (e.g., landslide, tornado, flood etc.). However, these studies mainly focused on using GPS capability of mobile for data collection. Following is an example:
•   Sujeet Kumar Sharmaa, Santosh K. Misrab, Jang Bahadur Singha, "The role of GIS-enabled mobile applications in disaster management: A case analysis of cyclone Gaja in India", International Journal of Information Management, Vol. 51, No. 102030, 2020
However, the existing studies reported in literature didn't use AI capability on mobile Apps for generating AI based insights. In our most recent studies, we have reported AI based insights on Mobile Apps. Following are some examples our related publications:
•   F. K. Sufi and M. Alsulami, "Knowledge Discovery of Global Landslides Using Automated Machine Learning Algorithms," in IEEE Access, vol. 9, pp. 131400-131419, 2021, doi: 10.1109/ACCESS.2021.3115043.
•   F. K. Sufi and I. Khalil, "Automated Disaster Monitoring From Social Media Posts Using AI-Based Location Intelligence and Sentiment Analysis," in IEEE Transactions on Computational Social Systems, doi: 10.1109/TCSS.2022.3157142.
In this paper, we used local landslide data of CMA, Bangladesh and made the AI driven insights available to senior decision makers for the very first time to measure the usability of such system.Â2) Computational loads are shared between the Mobile Apps (called Microsoft Power BI Mobile App) and Microsoft Cloud (called Microsoft Power BI Service). The seamless integration between Microsoft Power BI Mobile App and Microsoft Power BI Service provide good experience to the senior decision maker on their mobile app. With increase in computational load, the fully scalable Microsoft Power BI Service (i.e., Microsoft Cloud) dynamically assign more computational resources to provide faster AI driven sights to the decision makers. Further technical details on Microsoft Power BI Service is located at https://docs.microsoft.com/en-us/power-bi/fundamentals/service-basic-concepts.Â
3) Figure 11 to Figure 14 demonstrated the usability of the proposed system via deployed mobile apps both in iOS and Android platform through Microsoft Power BI Mobile App. In other words, these are not web applications running through the mobile web browsers.
Microsoft Power BI Mobile App is available in all mobile platforms like windows, iOS, and Android within their respective app stores. More details on Microsoft Power BI Mobile App could be found at https://docs.microsoft.com/en-us/power-bi/consumer/mobile/mobile-apps-for-mobile-devices.Â
-
CC2: 'Comment on nhess-2022-90', Abu Reza Md. Towfiqul Islam, 10 Apr 2022
reply
Its an intersting topic in the field of natural hazard. A scenario-based study will help us to make a quick decision in disaster management. Both linear and logistic regressions have used to get clear picture in landslide inventories.
-
CC4: 'Comment on nhess-2022-90', Disaster Development, 13 Apr 2022
reply
The techniques applied in developing this AI-based landslide analysis are unique and innovative. Does the presented system support Lift-n-Shift or Plug-n-Play mechanism to the different datasets to obtain AI-driven insights of different locations? What would be the process if someone wants to use the same system on landslide data in another location (e.g., Loess Plateau in China)?Â
Can I use the same techniques for other disasters like earthquakes, tornados, Flood, etc.?
It would be very useful if the authors can discuss this issue within a discussion or any other relevant sections of the paper.-
CC5: 'Reply on CC4', Fahim Sufi, 25 Apr 2022
reply
Many thanks for your interest in our paper. The methods described in this paper can be applied to other disaster databases (i.e., for any disaster types, for any location). As seen from Figure 6 of this paper, the process starts with obtaining the required data from a data store (i.e., Chittagong Metropolitan Area Landslide Data). After obtaining the data from the data store, Transformation, Decomposition Analysis, Regression Analysis are performed for generating the aggregated results.Â
This data store could be replaced with any other data stores, be it of other disaster types (e.g., earthquake, flood, cyclone, bushfire etc.), or of other locations of landslide incidence (e.g., e.g., Loess Plateau in China). When Transformation, Decomposition Analysis, and Regression Analysis are performed on the new data sets, AI driven insights are generated for those specific records. Hence, the external validity of the proposed experimentation is very high.
We will be more than happy to add these explanations in the discussion part of our updated manuscript. Again, many thanks for your interest in our paper. Please feel free to let me know if you have any suggestions or if you require any clarifications on this paper.
-
CC5: 'Reply on CC4', Fahim Sufi, 25 Apr 2022
reply
Fahim Sufi et al.
Fahim Sufi et al.
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 264 | 61 | 19 | 344 | 5 | 6 |
- HTML: 264
- PDF: 61
- XML: 19
- Total: 344
- BibTeX: 5
- EndNote: 6
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1