Fine-Tuning LightGBM Using an Artificial Ecosystem-Based Optimizer for Forest Fire Analysis

Abstract

This study’s main objective is to propose a hybrid machine learning model based on a gradient boosting algorithm named LightGBM and an artificial ecosystem-based optimization to improve the accuracy of forest fire susceptibility assessment. Four hundred twenty-six historical forest fires from the NASA portal and thirteen conditional factors including elevation, aspect, slope, curvature, normalized difference vegetation index, normalized difference water index, distance to residence, distance to road, distance to river, temperature, rain, humidity, and wind were used to train the model. The model performance was evaluated and compared with other benchmark models using root mean square error, area under receiver operating characteristics (AUC), and overall accuracy. The results show that the proposed model (AUC = 0.9705) outperforms others, such as Random Forest (AUC = 0.958), AdaBoost (AUC = 0.905), Bagging (AUC = 0.945), and Random Subspace (AUC = 0.938), respectively. The final model was interpreted to better understand the most influential factors of forest fire hazards. Study Implications: This study’s main objective is to propose a hybrid machine learning model based on a gradient boosting algorithm called LightGBM and an artificial ecosystem-based optimization to improve the accuracy of forest fire susceptibility assessment. The final model was interpreted using the Shapley (SHAP) values to understand how input factors are related to the fire susceptibility level. This study fills a gap in the research literature, searching for optimal algorithms to improve forest fire susceptibility mapping.

Modeling and Mapping of Forest Fire Occurrence in the Lower Silesian Voivodeship of Poland Based on Machine Learning Methods

“…In contrast, these two variables showed the highest contributions to the probability of the occurrence of forest fires among the topographic features in the RF model. In general, topographic features modify the fuel and its ability to burn [86][87][88][89][90].…”

Akdeniz bölgesi’ndeki orman yangınlarının uzaktan algılama ve coğrafi bilgi sistemleri kullanılarak değerlendirilmesi: Mersin ili Silifke ilçesi örneği

“…Ancak, bilimsel araştırmaların tutarlılığı ve güvenirliği için doğrulama yapılması son derece önemlidir (Kalantar et al, 2020;Orhan, 2021;Naghibi et al, 2016). Bu işlemde farklı yöntemler kullanılmakla birlikte sıklıkla alıcı işletim karakteristik (receiver operating characteristic -ROC) eğrisi tercih edilmektedir (Da et al, 2023;Nguyen et al, 2023;Si et al, 2022;Pourtaghi et al, 2016). Eğri altındaki alan (area under the curve -AUC) değeri tahminin doğruluğunu ifade etmektedir (Golkarian et al, 2018).…”