Analysis and prediction of natural disaster using spatial data mining technique

Refonaa, J.; Lakshmi, M.; Vivek, V.

doi:10.1109/iccpct.2015.7159379

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…Hua et al [24] proposed a semi-supervised method for spatio-temporal event detection in Twitter, such as earthquakes and disease outbreaks. On the other hand, productive works exploded the mobility pattern [25][26][27][28] by smartphone datasets to analyze people's disaster responses. For example, Song et al [29] developed a general probabilistic model to simulate population evacuation on complex geographic features in Japan in response to future disasters.…”

Section: Emergency Managementmentioning

confidence: 99%

Human Decision-making Strategy Analysis during Large-scale Disaster

Zhang

Wang

Fan

et al. 2022

Preprint

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In recent decades, the frequency and intensity of natural disasters have increased significantly. Following these unexpected disasters, understanding and predicting human decision-making strategies during disasters will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, such research is challenging due to the unavailability of reliable and large-scale human mobility data. In addition, these personal strategies during disasters become hard to predict due to the influences of multiple factors. In the decision-making process, what key (sometimes hidden) factors do people consider when making a particular mobility decision following a large-scale disaster? In this study, we construct a large human mobility database collected from mobile devices (GPS records of 66,598 users in the Greater Tokyo area), and analyze the human decision-making strategies following Japan’s 2011 Tohoku earthquake. Except for users’ location-based information, we also extract the grid-based stay areas from historical check-ins (normal days before disasters) and discover the functions of those areas combined with points of interest (POI) located in an area. In the final, we jointly model the users’ location-based factors and historical check-ins information, and develop an empirical prediction for decision-making strategies (i.e., destination, travel mode, and departure time from their workplaces) following the Tohoku earthquake. An explainable analysis is conducted to explore the fundamental laws that govern human mobility following disasters. The results are encouraging to support future interventions in analyzing decision-making strategies during disasters, and understanding the relationship between decision making and considered multiple factors/features (as well as the relationship between feature values and possible predicted values).

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Section: Emergency Managementmentioning

confidence: 99%

Human Decision-making Strategy Analysis during Large-scale Disaster

Zhang

Wang

Fan

et al. 2022

Preprint

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“…On the other hand, the IoT is presently the object of significant research as it will be implemented on a large scale within the next decade or so. Presently, several investigations have developed IoT applications for natural disasters [ 34 , 35 ], generalized and specialized industries [ 36 , 37 , 38 ], smart homes [ 39 , 40 , 41 ], medicine [ 42 , 43 , 44 ], agriculture [ 45 , 46 , 47 ], urban planning and intelligent cities [ 48 , 49 , 50 ] and the design of Intelligent Transport Systems (ITS) [ 51 , 52 , 53 ]. In the fields of ITS and smart cities, allowing vehicles to be interconnected, forming vehicular ad-hoc networks (VANETs), has enormous applications related to safety, internet connectivity, entertainment, file transfer and importantly, the detection and avoidance of dangerous or problematic traffic conditions [ 54 ].…”

Section: Related Workmentioning

confidence: 99%

Traffic Congestion Detection System through Connected Vehicles and Big Data

Cárdenas-Benítez

Aquino-Santos

Magaña-Espinoza³

et al. 2016

Sensors

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This article discusses the simulation and evaluation of a traffic congestion detection system which combines inter-vehicular communications, fixed roadside infrastructure and infrastructure-to-infrastructure connectivity and big data. The system discussed in this article permits drivers to identify traffic congestion and change their routes accordingly, thus reducing the total emissions of CO2 and decreasing travel time. This system monitors, processes and stores large amounts of data, which can detect traffic congestion in a precise way by means of a series of algorithms that reduces localized vehicular emission by rerouting vehicles. To simulate and evaluate the proposed system, a big data cluster was developed based on Cassandra, which was used in tandem with the OMNeT++ discreet event network simulator, coupled with the SUMO (Simulation of Urban MObility) traffic simulator and the Veins vehicular network framework. The results validate the efficiency of the traffic detection system and its positive impact in detecting, reporting and rerouting traffic when traffic events occur.

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“…Management of landslides draws attention by research from various fields of disciplines such as computer science, the science environment, meteorologists, geologists and scientists from various other disciplines who make joint efforts to determine the place and severity of landslides [4]. In the field of computer science, data mining aims to extract information from a data set so it can be read by users [5]. In addition, data mining can be applied by disciplines such as artificial neural networks, pattern recognition, spatial analysis of data, image databases and signal processing [6].…”

Section: Introductionmentioning

confidence: 99%

The Implementation of The Neuro Fuzzy Method Using Information Gain for Improving Accuracy in Determination of Landslide Prone Areas

Astuti

Muslim

Sugiharti³

2019

SJI

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The accuracy of information is increasing rapidly as technological development. For the example, the information in determination of disaster severity. The disasters that can be determined is landslide. This determination can be conducted using the fuzzy method. One of method is neuro fuzzy. Neuro fuzzy is a combined method of two systems, fuzzy logic and artificial neural network. The accuracy of neuro fuzzy method can be increased by applying the information gain. The purpose of this study is to implement and to know the accuracy of the implementation of information gain as the selection of landslide data features. It conducted to the neuro fuzzy method in determining landslide prone areas. The distribution of training data and testing data was using 20 k-fold cross validation. The implementation of the neuro fuzzy method on landslide data was obtained an accuracy of 81.9231%. In the implementation of the neuro fuzzy method with information gain was conducted in classification process. The process will stop when the accuracy has decreased. The highest accuracy result was obtained of 88.489% by removing an attribute. So, it can be concluded the accuracy increase of 6.5659% in the implementation of the neuro fuzzy method and information gain in determination of landslide prone areas.

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Analysis and prediction of natural disaster using spatial data mining technique

Cited by 11 publications

References 10 publications

Human Decision-making Strategy Analysis during Large-scale Disaster

Human Decision-making Strategy Analysis during Large-scale Disaster

Traffic Congestion Detection System through Connected Vehicles and Big Data

The Implementation of The Neuro Fuzzy Method Using Information Gain for Improving Accuracy in Determination of Landslide Prone Areas

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