Rule-Based Actionable Intelligence for Disaster Situation Management

Rule-Based Actionable Intelligence for Disaster Situation Management

Sarika Jain, Sumit Sharma, Jorrit Milan Natterbrede, Mohamed Hamada
Copyright: © 2020 |Pages: 16
DOI: 10.4018/IJKSS.2020070102
OnDemand:
(Individual Articles)
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

Managing natural disasters is a social responsibility as they might cause a gloomy impact on human life. Efficient and timely alert systems for public and actionable recommendations for decision makers may well decrease the number of casualties. Web semantics strengthen the description of web resources for exploiting them better and making them more meaningful for both human and machine. In this work, the authors propose a semantic rule-based approach for disaster situation management (DSM) to reach the next level of decision-making power and its architecture for providing actionable intelligence in the domain of the earthquake. The system itself is based on a data pre-processing layer, a computation layer, and the middle layer relies on an extensive rule base of experts' advice stored over time and a disaster ontology along with its inherent semantics. The rule-based reasoning approach uses this knowledge base in combination with the expert rule base, written in SWRL rules, to infer recommendations for the response to an earthquake.
Article Preview
Top

Introduction

A disaster is an uncertain situation over which human has no control. United States Geological Survey (USGS), MTA CSFK GGI Kövesligethy Radó Seismological Observatory Budapest, Hungary (BUD), Ministry of Statistics and Program Implementation and many more have taken initiatives for the collection of data in respect of different disasters (Tanyaş et al., 2019). The number of earthquakes in last few years based on the dataset of USGS (“The United States Geological Survey,” 2019) has been shown in Figure 1, in which any earthquake with magnitude 5 or higher is considered to be devastating. Possible prevention is always the best measure to tackle with this increasing number. However, this is not always possible; and in these cases, it is important to respond in a way that primarily minimizes loss of life and injuries, and that material and infrastructural damage is kept low.

Figure 1.

The number of earthquakes occurred in previous years based on dataset of USGS

IJKSS.2020070102.f01

So far, it has been the government’s responsibility to gather emergency supplies in advance, and to identify and reduce possible impacts of natural hazards in any locality, city or country. Government advisory committee practices what to do during and after a natural hazard like earthquake. Getting to know timely what actions to take helps dramatically in remaining safe in the event of an earthquake. Experts are a scarce resource (Jain, 2018). To predict, on the fly, the correct estimates of resources required is next to impossible. The saving of human lives includes many things, like providing medical help to injured, uncovering entrapped survivors as well as medical help to people who suffer from other medical issues that might be due to the disaster. Helpful recommendations could include estimations of: required ambulances, required doctors, required temporary hospitals, required medical supplies, and required bulldozers/cranes. These estimations are again mainly based on estimations, e.g., the number of injured or entrapped people. The recommendations not directly related to saving lives might also be important, viz., which roads have to be cleared to be able to deploy assistance, which regions are probably affected worst, is there a risk of aftershock or other related disasters like landslides. But apart from this, important actions in the first 24 hours also include information sharing, communicating both ways with the affected population, and transportation of relief goods. Also, it is important to ensure a working communication system between the different response actors to enable a good cooperation, activating emergency protocols if available and reaching out/coordinating outside help. To make a good decision on most of these aspects, it is very important to have data/information on which the decision can actually be based. Unfortunately, this information is normally not available directly after the disaster as the situation is hectic and reliable data is rare. At this point of time, automated assessments (based on the map data, population, etc.) must be carried out for an effective response. Based on the previous historical data, we find some specific gap and lack of information, communication and time management between different organizations who help to handle disaster situations.

The recommendation for the actions in the next 24 hours after the shock includes: Food/Water/Energy supply, continued search and rescue, registration of the affected population, casualty count and debris management. Again, estimations of required goods are really helpful to use resources effectively. At the same time the logistical aspect becomes more important as some requirements might not be feasible from a logistical standpoint. If this is not taken into account resources might be wasted at logistical bottlenecks without doing any good. Also, environmental factors should be considered to plan the further procedure: is the winter coming, is it monsoon time, is the region cut off from the rest of the civilization, are there cultural tensions etc. All these factors will influence the next steps. This is a task that can be well managed by rule-based approach. Also based on the first assessments and estimations of the situation, requirements to provide shelter can be determined.

Complete Article List

Search this Journal:
Reset
Volume 15: 1 Issue (2024)
Volume 14: 1 Issue (2023)
Volume 13: 4 Issues (2022): 2 Released, 2 Forthcoming
Volume 12: 4 Issues (2021)
Volume 11: 4 Issues (2020)
Volume 10: 4 Issues (2019)
Volume 9: 4 Issues (2018)
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing