Development of an atmosphere temperature measurement system based on computational fluid dynamics and neural network algorithms

Guo, Hongyang; Yang, Jie; Zhu, Huanan; Lin, Zhifang; Yun, Yuhan

doi:10.1016/j.buildenv.2023.110094

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…EFB reduces the dimensionality of features by bundling exclusive features (i.e., features that do not take on values simultaneously). These innovations allow LightGBM to achieve faster training speeds and lower memory consumption when processing large-scale datasets, compared to other gradient boosting methods, while maintaining or enhancing model performance [54][55][56].…”

Section: Light Gradient Boosting Modelmentioning

confidence: 99%

Predicting the Occurrence of Forest Fire in the Central-South Region of China

Hai,

Han,

Vandansambuu

et al. 2024

Preprint

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Grasping the spatial and temporal patterns of forest fires, along with the key factors that drive them and the ability to forecast such events accurately, is essential for the successful management of forests. In China's southern region, forest fires significantly endanger the ecological system, public safety, and economic well-being. Through the application of Geographic Information Systems (GIS) and LightGBM (Light Gradient Boosting Machine) model, this study investigates the determinants of fire incidents and formulates a predictive model for forest fire occurrences, alongside a zoning strategy, within the Central-South area. The results indicate: (i) Spatially, fire points exhibit significant clustering and autocorrelation characteristics; (ii) The Central-South Forest Fire Prediction Model shows exceptional accuracy, reliability, and predictive power, with high performance metrics across training and validation sets, including over 85% accuracy, precision, recall, and F1 scores, along with AUC values above 89%, underscoring its efficacy in forecasting forest fires and distinguishing between fire events; (iii) Throughout the year, forest fire risks in the Central-South region of China vary by region and season, with risk spikes from March to May in Guangdong, Guangxi, Hunan, Hubei, and Hainan. From June to August, localized risks are observed in Heyuan and Huangshi, while from September to November, an increase in risk is noted in Guangdong (Meizhou, Heyuan, Shaoguan), Guangxi (Nanning, Hezhou, Yulin), and Hunan (Binzhou, Yongzhou, Hengyang) due to cooler, drier conditions and leaf litter accumulation. From December to February, the risk continues in specific areas across Guangdong, Guangxi, Hunan, and Hubei.

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Section: Light Gradient Boosting Modelmentioning

confidence: 99%

Predicting the Occurrence of Forest Fire in the Central-South Region of China

Hai,

Han,

Vandansambuu

et al. 2024

Preprint

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“…So the hyper parameters must be chosen correctly. The hyperparameter choosing algorithms are grid search (Grid), random search, covariance matrix adaptive evolution strategy and tree-structured parzen estimator [47].…”

Section: Gradient Boost Machine Light Gbm and Xgboost Regressionmentioning

confidence: 99%

Analysis of The Travelling Time According to Weather Conditions Using Machine Learning Algorithms

CANBULUT

2023

Preprint

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Nowadays, a large part of the world’s population lives in urban areas. The increase in the population living in cities makes it difficult to live in cities.Local administrators seek solutions to these problems in order to make cities more livable. In order to increase the quality of transportation, which is among the important issues for local governments, studies are carried out to establish smart transportation systems for public transportation companies. Estimating travel times is an important tool in managing transportation operations. In addition, operations managers use these forecasts to coordinate future transportation operations. The public transport travel time prediction has been a well-researched topic; various researchers have predicted travel time using mathematical, statistical, and machine learning based models. Researchers also have compared the performance of these models. It can be seen that studies in this field can be divided into statistical methods and machine learning methods. And number of researchers use one of these methods. But in this study, we use both statistical and machine learning techniques together. Also, the parameters of this study are different from the other studies in this literature. The effect of the weather on the travel time will be analyzed. In this respect it is thought that the study will contribute to the literature. In the study, in addition to model tuning, sensitivity analyzes are also carried out according to the change of the k value in the KNN algorithm and the change of the threshold values used to determine outliers. we can say that, as can be seen from the analysis, the Catboost algorithm appears to be the algorithm that gives the best results in almost every situation.

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