Modelling the climate factors affecting forest fire in Sumatra using Random Forest and Artificial Neural Network

Shabrina, Ayu; Palupi, Irma; Wahyudi, Bambang; Wahyuni, Intan; Murti, Mulya Diana; Latifah, Arnida Lailatul

doi:10.1145/3575882.3575920

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…This performance measure ranges in [0, ∞), with 0 indicates a perfect match. Meanwhile, the EVS is estimated in (8):…”

Section: Performance Assessmentmentioning

confidence: 99%

“…The need for a forest fire prediction model is considered necessary to reduce its impact on society, such as death of flora and fauna, haze which affects the health of local residents, and deforestation which has longterm impacts. Researchers have developed models of forest fires, including the development of a probabilistic multilayer perceptron model utilizing fifth-generation seasonal forecasting system (SEAS5) from ECMWF [7], modeling of carbon emissions based on climate indicators in Sumatra with random forests and artificial neural networks [8], and modeling of hotspots in Kalimantan using Bayesian inference based on precipitation, relative dry spells, ENSO and IOD [9]. However, of the various models offered, not many have conducted a deeper analysis of the models obtained, such as analysis of the sensitivity and feature importance of each predictor or climate indicator used.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity and feature importance of climate factors and evaluation of different machine learning models for predicting fire hotspots in Kalimantan, Indonesia

Nurdiati,

Nugrahani,

Bukhari

et al. 2024

IJ-AI

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<p>Hotspots as indicators of forest fires capable of quickly monitoring large areas are often predicted using various machine learning methods. However, there is still little research that analyzes the sensitivity and feature importance of each predictor that forms a machine learning prediction model. This study evaluates and compares several machine learning methods to predict hotspots in Kalimantan. Using the most accurate machine learning model, each climate factor used as a predictor is analyzed for its sensitivity and feature importance. Some of the machine learning methods used include random forest, gradient boosting, Bayesian regression, and artificial neural networks. Meanwhile, several measures of sensitivity and feature importance used are variance-based, density-based, and distribution-based sensitivity indices, as well as permutation and Shapley feature importance. Evaluation of the ML model concluded that the Bayesian linear regression model outperformed other ML models, based on RMSE and explained variance score. Meanwhile, tree-based models, such as random forest and gradient boosting, are indicative of overfit. Based on the results of sensitivity analysis and feature importance, the number of dry days is the most important feature for the Bayesian linear regression model in predicting the number of hotspots in Kalimantan.</p>

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“…This performance measure ranges in [0, ∞), with 0 indicates a perfect match. Meanwhile, the EVS is estimated in (8):…”

Section: Performance Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensitivity and feature importance of climate factors and evaluation of different machine learning models for predicting fire hotspots in Kalimantan, Indonesia

Nurdiati,

Nugrahani,

Bukhari

et al. 2024

IJ-AI

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Advancing Climate Modeling through High-Performance Computing: Towards More Accurate and Efficient Simulations

Kulkarni,

Manoharan,

Gaddi

2024

EAI Endorsed Trans Energy Web

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A crucial branch of science called climate modeling uses mathematical equations and computer simulations to study and forecast the Earth's climate sys- tem. The main elements of climate modeling, such as general circulation models (GCMs), data assimilation methods, and numerical formulations, are outlined in this paper. GCMs, which include grid point and spectral models, are effective instruments for examining the behavior of the climate. Four-Dimensional Data Assimilation (4D-Var) is one example of a data assimilation technique that in- corporates observational data into models to improve their correctness. Numeri cal methods, ocean dynamics, heat transport, radiative transfer, and atmospheric dynamics are all included in numerical formulations. The simulation of different climate processes is possible because to these mathematical representations. Fur thermore, the detection of precipitation patterns within climate modeling is using machine learning techniques like Random Forest more frequently. This paper highlights the importance of high-performance computing (HPC) in climate modeling, boosting efficiency and simulations, in the context of research technique. Advanced data assimilation and validation techniques are also examined, as well as the influence of high-resolution modeling on small-scale climatic processes. On HPC platforms, accessibility to climate modeling is addressed. It is shown how climate modeling crosses physics, mathematics, computer science, and engineering to be interdisciplinary. A comprehensive understanding of the Earth's intricate climate system gains from the integration of all its parts, from atmospheric dynamics to data assimilation. We explore the consequences of these research approaches, their contribution to enhancing climate prediction models, and the influence of various factors on climatic variables in the debate. Climate modeling becomes an essential tool for studying precipitation patterns and climate change, ultimately improving our comprehension of the complex cli- mate system on Earth.

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Modelling the climate factors affecting forest fire in Sumatra using Random Forest and Artificial Neural Network

Cited by 2 publications

References 21 publications

Sensitivity and feature importance of climate factors and evaluation of different machine learning models for predicting fire hotspots in Kalimantan, Indonesia

Sensitivity and feature importance of climate factors and evaluation of different machine learning models for predicting fire hotspots in Kalimantan, Indonesia

Advancing Climate Modeling through High-Performance Computing: Towards More Accurate and Efficient Simulations

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