Applications of XGBoost in water resources engineering: A systematic literature review (Dec 2018–May 2023)

Niazkar, Majid; Menapace, Andrea; Brentan, Bruno; Piraei, Reza; Jimenez, David; Dhawan, Pranav; Righetti, Maurizio

doi:10.1016/j.envsoft.2024.105971

Cited by 28 publications

(1 citation statement)

References 82 publications

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“…Based on the dataset constructed in this paper, leave-one-out cross-validation and fullsample validation are used to test the regression effects of five of the most commonly used machine learning models (RR, LR, SVMR, RF, XGR) [43][44][45][46][47][48][49]. In the leave-one-out method, the number of each test set is one, at which time RMSE = MAE is used.…”

Section: Bright Temperature Threshold Identification Experimental Res...mentioning

confidence: 99%

Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Detection Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brightness Temperature Thresholds

Ding,

Wang,

et al. 2024

Forests

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Satellite remote sensing plays a significant role in the detection of smoke from forest fires. However, existing methods for detecting smoke from forest fires based on remote sensing images rely solely on the information provided by the images, overlooking the positional information and brightness temperature of the fire spots in forest fires. This oversight significantly increases the probability of misjudging smoke plumes. This paper proposes a smoke detection model, Forest Smoke-Fire Net (FSF Net), which integrates wildfire smoke images with the dynamic brightness temperature information of the region. The MODIS_Smoke_FPT dataset was constructed using a Moderate Resolution Imaging Spectroradiometer (MODIS), the meteorological information at the site of the fire, and elevation data to determine the location of smoke and the brightness temperature threshold for wildfires. Deep learning and machine learning models were trained separately using the image data and fire spot area data provided by the dataset. The performance of the deep learning model was evaluated using metric MAP, while the regression performance of machine learning was assessed with Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The selected machine learning and deep learning models were organically integrated. The results show that the Mask_RCNN_ResNet50_FPN and XGR models performed best among the deep learning and machine learning models, respectively. Combining the two models achieved good smoke detection results (Precisionsmoke=89.12%). Compared with wildfire smoke detection models that solely use image recognition, the model proposed in this paper demonstrates stronger applicability in improving the precision of smoke detection, thereby providing beneficial support for the timely detection of forest fires and applications of remote sensing.

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Section: Bright Temperature Threshold Identification Experimental Res...mentioning

confidence: 99%

Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Detection Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brightness Temperature Thresholds

Ding,

Wang,

et al. 2024

Forests

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Estimating Colebrook-White Friction Factor Using Tree-Based Machine Learning Models

Niazkar,

Menapace,

Righetti

2024

Lecture Notes in Networks and Systems

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Prediction of non-stationary daily streamflow series based on ensemble learning: a case study of the Wei River Basin, China

Ma,

Zhang,

Xie

et al. 2024

Stoch Environ Res Risk Assess

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Applications of XGBoost in water resources engineering: A systematic literature review (Dec 2018–May 2023)

Cited by 28 publications

References 82 publications

Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Detection Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brightness Temperature Thresholds

Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Detection Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brightness Temperature Thresholds

Estimating Colebrook-White Friction Factor Using Tree-Based Machine Learning Models

Prediction of non-stationary daily streamflow series based on ensemble learning: a case study of the Wei River Basin, China

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