Study of Driving Factors Using Machine Learning to Determine the Effect of Topography, Climate, and Fuel on Wildfire in Pakistan

Rafaqat, Warda; Iqbal, Mansoor; Kanwal, Rida; Song, Weiguo

doi:10.3390/rs14081918

Cited by 9 publications

(14 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The same factors can affect estimates in different ways, depending on the location and scale of the analysis. For example, in Pakistan, precipitation, soil moisture, unemployment rate, livestock density, and density of local roads are important [8]; in the European Mediterranean region, precipitation, soil moisture, road density, vegetation type [34]; in the Daxing'an Mountains, forest type, and distances to railways. However, gross national product, unemployment, and population density did not play a decisive role in the occurrence of fires [37].…”

Section: Territory Accuracymentioning

confidence: 99%

“…In this study, meteorological data were obtained from the Federal State Budgetary Institution "Irkutsk Department of Hydrometeorology and Environmental Protection" [73]. The selected average daily indicators describe air temperature, atmospheric pressure, relative humidity, wind direction (points), wind speed (determined on the 12-point F. Beaufort scale: calm (0-0.2), quiet (0.3-1.5), light (1.6-3.3), light (3.4-5.4), moderate (5.5-7.9), fresh (8.0-10.7), heavy (10.8-13.8)), amount of precipitation (light rain (0.0-2), rain (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14), heavy rain (15-49), very heavy rain (more than 50)), and weather phenomena (thunderstorm, fog, rain, haze, snow, cloudy, drizzle, dust, hail) (Table 3). Maps in Figure 4 show average monthly weather indicators.…”

Section: Datamentioning

confidence: 99%

“…Wildfires remain a serious problem throughout the world [1][2][3][4][5][6]; they negatively affect biodiversity and air, soil, and water quality [7][8][9], lead to ecosystem degradation [10], and pose a threat to the safety of people and infrastructure [11,12]. Wildfires can be caused by climatic conditions, careless handling of fire by the local population, or other factors that depend on the characteristics of the regions [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wildfire Susceptibility Mapping in Baikal Natural Territory Using Random Forest

Nikolaychuk,

Pestova,

Yurin

2024

Forests

View full text Add to dashboard Cite

Wildfires are a significant problem in Irkutsk Oblast. They are caused by climate change, thunderstorms, and human factors. In this study, we use the Random Forest machine learning method to map the wildfire susceptibility of Irkutsk Oblast based on data from remote sensing, meteorology, government forestry authorities, and emergency situations. The main contributions of the paper are the following: an improved domain model that describes information about weather conditions, vegetation type, and infrastructure of the region in the context of the possible risk of wildfires; a database of wildfires in Irkutsk Oblast from 2017 to 2020; the results of an analysis of factors that cause wildfires and risk assessment based on Random Forest in the form of fire hazard mapping. In this paper, we collected and visualized data on wildfires and factors influencing their occurrence: meteorological, topographic, characteristics of vegetation, and human activity (social factors). Data sets describing two classes, “fire” and “no fire”, were generated. We introduced a classification according to which the probability of a wildfire in each specific cell of the territory can be determined and a wildfire risk map built. The use of the Random Forest method allowed us to achieve the following risk assessment accuracy indicators: accuracy—0.89, F1-score—0.88, and AUC—0.96. The comparison of the results with earlier ones obtained using case-based reasoning revealed that the application of the case-based approach can be considered the initial stage for deeper investigations with the use of Random Forest for more accurate forecasting.

show abstract

Section: Territory Accuracymentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wildfire Susceptibility Mapping in Baikal Natural Territory Using Random Forest

Nikolaychuk,

Pestova,

Yurin

2024

Forests

View full text Add to dashboard Cite

show abstract

“…This study delved into ML techniques such as decision trees (DTs), RFs, K-nearest neighbors (KNNs), and support vector machines (SVMs) to map wildfires, with the goal to identify the most effective approach for model training and validation. Furthermore, to augment the features used in modeling, CNNs were employed-both 1D and 2D-to demonstrate their capabilities in wildfire assessment [37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Approaches for Wildfire Mapping and Prediction Assessment Using a Convolutional Neural Network (CNN)

Kanwal,

Rafaqat,

Iqbal

et al. 2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

As wildfires become increasingly perilous amidst Pakistan’s expanding population and evolving environmental conditions, their global significance necessitates urgent attention and concerted efforts toward proactive measures and international cooperation. This research strives to comprehensively enhance wildfire prediction and management by implementing various measures to contribute to proactive mitigation in Pakistan. Additionally, the objective of this research was to acquire an extensive understanding of the factors that influence fire patterns in the country. For this purpose, we looked at the spatiotemporal patterns and causes of wildfires between 2000 and 2023 using descriptive analysis. The data analysis included a discussion on density-based clustering as well as the distribution of the data across four seasons over a period of six years. Factors that could indicate the probability of a fire occurrence such as weather conditions, terrain characteristics, and fuel availability encompass details about the soil, economy, and vegetation. We used a convolutional neural network (CNN) to extract features, and different machine learning (ML) techniques were implemented to obtain the best model for wildfire prediction. The majority of fires in the past six years have primarily occurred during the winter months in coastal locations. The occurrence of fires was accurately predicted by ML models such as random forest (RF), which outperformed competing models. Meanwhile, a CNN with 1D and 2D was used for more improvement in prediction by ML models. The accuracy increased from an 86.48 to 91.34 accuracy score by just using a CNN 1D. For more feature extraction, a CNN 2D was used on the same dataset, which led to state-of-the-art prediction results. A 96.91 accuracy score was achieved by further tuning the RF model on the total data. Data division by spatial and temporal changes was also used for the better prediction of fire, which can further be helpful for understanding the different prospects of wildfire. This research aims to advance wildfire prediction methodologies by leveraging ML techniques to explore the benefits and limitations of capturing complex patterns and relationships in large datasets. Policymakers, environmentalists, and scholars studying climate change can benefit greatly from the study’s analytical approach, which may assist Pakistan in better managing and reducing wildfires.

show abstract

“…Wild and man-made fires remain a serious problem all over the world [1][2][3][4][5][6]. They have a detrimental effect on the composition and structure of fauna and flora and on the quality of air, soil, and water [7,8], which generally leads to degradation of ecosystems [9]. In addition, they are a serious threat to people and infrastructure [10,11].…”

Section: Introductionmentioning

confidence: 99%

Forest Fire Risk Forecasting with the Aid of Case-Based Reasoning

et al. 2022

View full text Add to dashboard Cite

Forest fire is one of the serious threats to the population and infrastructure of Irkutsk Oblast because its territory is heavily forested. This paper discusses the main stages of solving the problem of forecasting the risk of forest fires via a case-based approach, including data preprocessing, formation of a case model, and creation of a prototype of a case-based expert system. The main contributions of the paper are the following: a case model that provides a compact representation of information about weather conditions, vegetation type, and infrastructure of the region in relation to the possible risk of a wildfire; a case-base containing information about wildfires in Irkutsk Oblast for the period from 2017 to 2020; and a methodology for creating prototypes of case bases providing the transformation of decision tables of a special type. The approbation of the approach was carried out for separate forest districts, namely Bodaibinsk and Kazachinsk-Lena. The accuracy score was used for the evaluation of the results of forecasting the risk of wildfires. The average score value reached 0.51. The evaluation results revealed that application of the case-based approach can be considered as the initial stage for deeper investigations with the use of different methods (data mining, neural networks) for more accurate forecasting.

show abstract

Study of Driving Factors Using Machine Learning to Determine the Effect of Topography, Climate, and Fuel on Wildfire in Pakistan

Cited by 9 publications

References 88 publications

Wildfire Susceptibility Mapping in Baikal Natural Territory Using Random Forest

Wildfire Susceptibility Mapping in Baikal Natural Territory Using Random Forest

Data-Driven Approaches for Wildfire Mapping and Prediction Assessment Using a Convolutional Neural Network (CNN)

Forest Fire Risk Forecasting with the Aid of Case-Based Reasoning

Contact Info

Product

Resources

About