Application of a Fusion Model Based on Machine Learning in Visibility Prediction

Zhen, Maochan; Yi, Mingjian; Luo, Tao; Wang, Feifei; Yang, Kaixuan; Ma, Xuebin; Cui, Shengcheng; Li, Xuebin

doi:10.3390/rs15051450

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…The Stacking fusion model is a heterogeneous ensemble method that can effectively combine the advantages of multiple models and obtain better results than a single model. Its advancement has been verified in the landslide susceptibility assessment [17], biomass estimation [18], visibility prediction [19] and other remote sensing related fields. However, to our knowledge, the potential of this method in the field of SM downscaling has not been fully explored.…”

Section: Introductionmentioning

confidence: 90%

A Spatial Downscaling Framework for SMAP Soil Moisture Based on Stacking Strategy

Xu,

Su,

et al. 2024

Remote Sensing

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Soil moisture (SM) data can provide guidance for decision-makers in fields such as drought monitoring and irrigation management. Soil Moisture Active Passive (SMAP) satellite offers sufficient spatial resolution for global-scale applications, but its utility is limited in regional areas due to its lower spatial resolution. To address this issue, this study proposed a downscaling framework based on the Stacking strategy. The framework integrated extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) to generate 1 km resolution SM data using 15 high-resolution factors derived from multi-source datasets. In particular, to test the influence of terrain partitioning on downscaling results, Anhui Province, which has diverse terrain features, was selected as the study area. The results indicated that the performance of the three base models varied, and the developed Stacking strategy maximized the potential of each model with encouraging downscaling results. Specifically, we found that: (1) The Stacking model achieved the highest accuracy in all regions, and the performance order of the base models was: XGBoost > CatBoost > LightGBM. (2) Compared with the measured SM at 87 sites, the downscaled SM outperformed other 1 km SM products as well as the downscaled SM without partitioning, with an average ubRMSE of 0.040 m3/m3. (3) The downscaled SM responded positively to rainfall events and mitigated the systematic bias of SMAP. It also preserved the spatial trend of the original SMAP, with higher levels in the humid region and relatively lower levels in the semi-humid region. Overall, this study provided a new strategy for soil moisture downscaling and revealed some interesting findings related to the effectiveness of the Stacking model and the impact of terrain partitioning on downscaling accuracy.

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Section: Introductionmentioning

confidence: 90%

A Spatial Downscaling Framework for SMAP Soil Moisture Based on Stacking Strategy

Xu,

Su,

et al. 2024

Remote Sensing

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“…It doesn't just use the best-performing model as the meta-classifier; it collaboratively combines the strengths and insights of all models in a synergistic manner. This approach creates a final predictive model that benefits from the diverse perspectives and capabilities of the base models, thus producing a more robust and accurate prediction within the DSE framework 47 . It's like bringing together a team of experts to solve a complex problem, with each expert contributing their unique insights and skills.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Predictive modelling and identification of key risk factors for stroke using machine learning

Hassan,

Gulzar Ahmad,

Ullah Munir

et al. 2024

Sci Rep

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Strokes are a leading global cause of mortality, underscoring the need for early detection and prevention strategies. However, addressing hidden risk factors and achieving accurate prediction become particularly challenging in the presence of imbalanced and missing data. This study encompasses three imputation techniques to deal with missing data. To tackle data imbalance, it employs the synthetic minority oversampling technique (SMOTE). The study initiates with a baseline model and subsequently employs an extensive range of advanced models. This study thoroughly evaluates the performance of these models by employing k-fold cross-validation on various imbalanced and balanced datasets. The findings reveal that age, body mass index (BMI), average glucose level, heart disease, hypertension, and marital status are the most influential features in predicting strokes. Furthermore, a Dense Stacking Ensemble (DSE) model is built upon previous advanced models after fine-tuning, with the best-performing model as a meta-classifier. The DSE model demonstrated over 96% accuracy across diverse datasets, with an AUC score of 83.94% on imbalanced imputed dataset and 98.92% on balanced one. This research underscores the remarkable performance of the DSE model, compared to the previous research on the same dataset. It highlights the model's potential for early stroke detection to improve patient outcomes.

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“…Su et al [16] established an AQI prediction model based on genetic algorithms and BP neural networks in 2020, which provides certain guidance for the predictive study of AQI. Currently, the academic community utilizes mainstream machine learning models such as neural networks, support vector machine regression, and random forests for air quality prediction [17][18][19]. These models could make relatively accurate predictions of air quality.…”

Section: Introductionmentioning

confidence: 99%

Air Quality Class Prediction Using Machine Learning Methods Based on Monitoring Data and Secondary Modeling

Liu,

Cui,

Liu

2024

Atmosphere

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Addressing the constraints inherent in traditional primary Air Quality Index (AQI) forecasting models and the shortcomings in the exploitation of meteorological data, this research introduces a novel air quality prediction methodology leveraging machine learning and the enhanced modeling of secondary data. The dataset employed encompasses forecast data on primary pollutant concentrations and primary meteorological conditions, alongside actual meteorological observations and pollutant concentration measurements, spanning from 23 July 2020 to 13 July 2021, sourced from long-term air quality projections at various monitoring stations within Jinan, China. Initially, through a rigorous correlation analysis, ten meteorological factors were selected, comprising both measured and forecasted data across five categories each. Subsequently, the significance of these ten factors was assessed and ranked based on their impact on different pollutant concentrations, utilizing a combination of univariate and multivariate significance analyses alongside a random forest approach. Seasonal characteristic analysis highlighted the distinct seasonal impacts of temperature, humidity, air pressure, and general atmospheric conditions on the concentrations of six key air pollutants. The performance evaluation of various machine learning-based classification prediction models revealed the Light Gradient Boosting Machine (LightGBM) classifier as the most effective, achieving an accuracy rate of 97.5% and an F1 score of 93.3%. Furthermore, experimental results for AQI prediction indicated the Long Short-Term Memory (LSTM) model as superior, demonstrating a goodness-of-fit of 91.37% for AQI predictions, 90.46% for O3 predictions, and a perfect fit for the primary pollutant test set. Collectively, these findings affirm the reliability and efficacy of the employed machine learning models in air quality forecasting.

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Application of a Fusion Model Based on Machine Learning in Visibility Prediction

Cited by 9 publications

References 27 publications

A Spatial Downscaling Framework for SMAP Soil Moisture Based on Stacking Strategy

A Spatial Downscaling Framework for SMAP Soil Moisture Based on Stacking Strategy

Predictive modelling and identification of key risk factors for stroke using machine learning

Air Quality Class Prediction Using Machine Learning Methods Based on Monitoring Data and Secondary Modeling

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