Dynamic and Robust Wildfire Risk Prediction System

Salehi, Mahsa; Rusu, Laura-Cristina; Lynar, Timothy; Phan, A.

doi:10.1145/2939672.2939685

Cited by 21 publications

(6 citation statements)

References 12 publications

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“…In recent years, data-driven approaches have been applied to detect and analyze the risk issues. For example, [9] predicted wildfire risk using dynamic temporal weather data. [10] leveraged heterogeneous big data to deal with the issue in dangerous goods transportation and [11] utilized dynamically updated data to identify the likelihood of a fire event.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Urban Sensing for Gas Leakage Risk Assessment

et al. 2023

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In order to mitigate gas leakage damage, it is important to assess systematic risk of gas leakage in utility networks. In practice, leakage detection systems heavily rely on tedious and expensive human efforts, such as manually checking and assessing the risk, and thus only a very limited portion of the gas pipeline network can usually be assessed during a period of time. Therefore, it is critical to develop tools for automatically and systematically evaluating the whole gas pipeline network and revealing high-risk sites for checking in a timely fashion. To this end, in this paper, we develop an intelligent gas leakage risk assessment system based on the analysis of large-scale multi-source data, such as gas pipeline data, Point of Interests (POIs) data, and human mobility data. However, it is a non-trivial endeavor to design such a risk assessment system due to the unclear leakage mechanism, complex environmental conditions, and large size of the pipeline network. To address these challenges, both internal features of gas pipelines and external environmental features should be exploited. Specifically, we design a novel urban sensing technique to extract environmental features by analyzing human mobility data. Then, a joint learning neural network is developed to assess the leakage risk by integrating both internal and external features. Moreover, an intelligent risk assessment system is implemented and deployed for experiments in real-world scenarios. The results demonstrate the effectiveness of our system for citywide gas leakage risk assessment. Indeed, the proposed system could help to locate the potential leakage sites in a timely way.INDEX TERMS Human mobility, urban sensing, risk management, gas leakage assessment I. INTRODUCTION

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Section: Related Workmentioning

confidence: 99%

Intelligent Urban Sensing for Gas Leakage Risk Assessment

et al. 2023

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“…The major trend in literature is the use of satellite data, local meteorological sensors and wildfire records to predict the risk of fire occurrence. While there are some prior studies proposing unsupervised context-based models (Salehi et al 2016), the mainstream of the literature proposes supervised classification including mostly binary classification and in a few cases multi-label classification approaches (Sakr, Elhajj, and Mitri 2011;Özbayoglu and Bozer 2012). For example, ( Özbayoglu and Bozer 2012) proposed an estimation of the burned area based on historical data from Turkey using a multi-layer perceptron, radial basis function networks, and support vector machines.…”

Section: Related Workmentioning

confidence: 99%

Where there's Smoke, there's Fire: Wildfire Risk Predictive Modeling via Historical Climate Data

Gholami

Kodandapani²,

Wang

et al. 2021

AAAI

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Wildfire is a growing global crisis with devastating consequences. Uncontrolled wildfires take away human lives, destroy millions of animals and trees, degrade the air quality, impact the biodiversity of the planet and cause substantial economic costs. It is incredibly challenging to predict the spatio-temporal likelihood of wildfires based on historical data, due to their stochastic nature. Crucially though, the accurate and reliable prediction of wildfires can help the stakeholders and decision-makers take timely, strategic and effective actions to prevent, detect and suppress the wildfires before they become unmanageable. Unfortunately, most previous studies developed predictive models that suffer from some shortcomings: (i) they do not take the temporal aspects into account precisely and they assume the independent and identically distributed random variables in the evaluation phase; (ii) they do not evaluate their approaches comprehensively, thus it is not clear if their proposed predictions and selected models are reliable across different locations and time steps for practical deployment; and (iii) for the supervised learning models, they use predictor features and fire observations from the same time step in the training phase, which makes the inference task infeasible for future fire prediction. In this paper, we revisit the wildfire predictive modeling, explore the inherent challenges from a practical perspective and evaluate our modeling approach comprehensively via historical burned areas, climate and geospatial data from three vast landscapes in India.

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“…Here, prioritization of service demand locations based on future risk prediction can be helpful [17]. Fire risk prediction has been a primary focus of many researchers; however, this work has mostly pertained to forest and wild land fires [18][19][20][21][22]. [23] presents an extensive study on the effect of climate on fire prediction along with an analysis of the impact of different seasons on burned area from global perspective.…”

Section: Related Workmentioning

confidence: 99%

Urban Fire Station Location Planning using Predicted Demand and Service Quality Index

Dey¹,

Andrew²,

England³

2021

Preprint

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In this article, we propose a systematic approach for fire station location planning. We develop a machine learning model, based on Random Forest, for demand prediction and utilize the model further to define a generalized index to measure quality of fire service in urban settings. Our model is built upon spatial data collected from multiple different sources. Efficacy of proper facility planning depends on choice of candidates where fire stations can be located along with existing stations, if any. Also, the travel time from these candidates to demand locations need to be taken care of to maintain fire safety standard. Here, we propose a travel time based clustering technique to identify suitable candidates. Finally, we develop an optimization problem to select best locations to install new fire stations. Our optimization problem is built upon maximum coverage problem, based on integer programming. We present a detailed experimental study of our proposed approach in collaboration with city of Victoria Fire Department, MN, USA. Our demand prediction model achieves true positive rate of 70% and false positive rate of 22% approximately. We aid Victoria Fire Department to select a location for a new fire station using our approach. We present detailed results on improvement statistics by locating a new facility, as suggested by our methodology, in the city of Victoria.

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Dynamic and Robust Wildfire Risk Prediction System

Cited by 21 publications

References 12 publications

Intelligent Urban Sensing for Gas Leakage Risk Assessment

Intelligent Urban Sensing for Gas Leakage Risk Assessment

Where there's Smoke, there's Fire: Wildfire Risk Predictive Modeling via Historical Climate Data

Urban Fire Station Location Planning using Predicted Demand and Service Quality Index

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