A Machine-Learning Approach Combining Wavelet Packet Denoising with Catboost for Weather Forecasting

Niu, Dan; Li, Diao; Zang, Zengliang; Che, Hongshu; Zhang, Tianbao; Chen, Xisong

doi:10.3390/atmos12121618

Cited by 25 publications

(6 citation statements)

References 29 publications

(46 reference statements)

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“…Assuming we have a dataset S = {(X i , Y i )} i=1 ... n , where X i = (x i , 1 , ..., x i , m ) represents a vector of m features, and Y i ∈ R is the label value [102]. In the Greedy TS method, the categorical features are replaced with the average label value of the entire training set [103]. Therefore, x i , k is replaced with…”

Section: Algorithm 3 Lightgbmmentioning

confidence: 99%

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

2022

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Ensemble learning techniques have achieved state-of-the-art performance in diverse machine learning applications by combining the predictions from two or more base models. This paper presents a concise overview of ensemble learning, covering the three main ensemble methods: bagging, boosting, and stacking, their early development to the recent state-of-the-art algorithms. The study focuses on the widely used ensemble algorithms, including random forest, adaptive boosting (AdaBoost), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). An attempt is made to concisely cover their mathematical and algorithmic representations, which is lacking in the existing literature and would be beneficial to machine learning researchers and practitioners.

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Section: Algorithm 3 Lightgbmmentioning

confidence: 99%

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

2022

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“…Second, compared with the single crop coefficient model, the machine learning model can use the entire data set for training, minimize information loss, and still provide high prediction accuracy in the case of missing variables. Kim et al [32] proposed a CNN-CatBoost hybrid model solar radiation prediction method and concluded that the prediction accuracy and stability of this hybrid model is better than the single model of CNN and CatBoost; Niu et al [33] introduced a machine learning method based on wavelet packet denoising and CatBoost for weather forecasting. Using a feature selection and spatio-temporal feature addition to improve forecasting performance, the results show that the CatBoost model combined with wavelet packet denoising can achieve shorter convergence time and higher forecasting accuracy than forecasting models using deep learning or machine learning algorithms alone.…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Prediction Model for CatBoost Tomato Transpiration Rate Based on Feature Extraction

Tong,

Zhang,

et al. 2023

Agronomy

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The growth and yield of crops are highly dependent on irrigation. Implementing irrigation plans that are tailored to the specific water requirements of crops can enhance crop yield and improve the quality of tomatoes. The mastery and prediction of transpiration rate (Tr) is of great significance for greenhouse crop water management. However, due to the influence of multiple environmental factors and the mutual coupling between environmental factors, it is challenging to construct accurate prediction models. This study focuses on greenhouse tomatoes and proposes a data-driven model configuration based on the Competitive adaptive reweighted sampling (CARS) algorithm, using greenhouse environmental sensors that collect six parameters, such as air temperature, relative humidity, solar radiation, substrate temperature, light intensity, and CO2 concentration. In response to the differences in crop transpiration changes at different growth stages and time stages, the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm was used to identify three characteristic intervals: florescence stage, fruiting stage daytime, and fruiting stage night-time. Based on this, a greenhouse tomato Tr prediction model (CARS-CatBoost model) based on the CatBoost machine learning algorithm was constructed. The experimental verification shows that the coefficient of determination (R2) of the constructed CARS-CatBoost single model for the whole growth stage is 0.92, which is higher than the prediction accuracy of the traditional single crop coefficient model (R2 = 0.54). Among them, the prediction accuracy at night during the fruiting stage is the highest, and the Root Mean Square Error (RMSE) drops to 0.427 g·m−2·h−1. This study provides an intelligent prediction method based on the zonal modeling of crop growth characteristics, which can be used to support precise irrigation regulation of greenhouse tomatoes.

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“…Furthermore, machine learning (ML) architectures, applied to weather forecasting, can be grouped into static and dynamic (recurrent) in terms of whether the prediction is generated sequentially [48]- [51], or not. Static ML architectures include clustering (K-means, PCA) [52], [53], artificial neural networks (ANN) [49], [54], [55], graph neural networks (GNN) [56], clustering + neural networks [57], [58], decision trees such as Gradient Boosting (XGBoost [59], AdaBoost [50], CatBoost [50], [60], [61]), and Random Forest [62], [63]. On the other hand, dynamic ML architectures for weather forecasting make use of the sequential nature of training data in generating the forecast.…”

Section: Related Workmentioning

confidence: 99%

A Novel Transformer Network With Shifted Window Cross-Attention for Spatiotemporal Weather Forecasting

Bojesomo,

AlMarzouqi,

Liatsis

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Earth Observation is a growing research area that can capitalize on the powers of AI for short time forecasting, a now-casting scenario. In this work, we tackle the challenge of weather forecasting using a video transformer network. Vision transformer architectures have been explored in various applications, with major constraints being the computational complexity of attention and the data-hungry training. To address these issues, we propose the use of Video Swin-Transformer, coupled with a dedicated augmentation scheme. Moreover, we employ gradual spatial reduction on the encoder side and crossattention on the decoder. The proposed approach is tested on the Weather4Cast2021 weather forecasting challenge data, which requires the prediction of 8 hours ahead future frames (4 per hour) from an hourly weather product sequence. The dataset was normalized to 0-1 to facilitate the use of the evaluation metrics across different datasets. The model results in an MSE score of 0.4750 when provided with training data, and 0.4420 during transfer learning without using training data, respectively.

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A Machine-Learning Approach Combining Wavelet Packet Denoising with Catboost for Weather Forecasting

Cited by 25 publications

References 29 publications

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

A Hybrid Prediction Model for CatBoost Tomato Transpiration Rate Based on Feature Extraction

A Novel Transformer Network With Shifted Window Cross-Attention for Spatiotemporal Weather Forecasting

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