Prediction Skill of Extended Range 2-m Maximum Air Temperature Probabilistic Forecasts Using Machine Learning Post-Processing Methods

Peng, Ting; Zhi, Xiefei; Ji, Yan; Ji, Luying; Tian, Ye

doi:10.3390/atmos11080823

Cited by 47 publications

(29 citation statements)

References 36 publications

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“…In this study, various machine learning algorithms were used for survival rate prediction according to mortality, survival time, and treatment method. The algorithms are voting ensembles [ 8 – 11 ], Logistic Regression (LR) [ 12 ], K-nearest neighbors (KNN) [ 13 , 14 ], Decision Tree (DT) Classifier [ 15 – 17 ], Support Vector Machine (SVM) [ 18 – 21 ], Random Forest (RF) [ 22 , 23 ], Extreme gradient boosting trees (XG Boost) [ 24 ], Light GBM [ 25 , 26 ], and Natural Gradient Boosting (NG Boost) [ 27 , 28 ]. Its prediction results are compared in Tables 6 , 8 , and 10 .…”

Section: Methodsmentioning

confidence: 99%

Machine learning-based survival rate prediction of Korean hepatocellular carcinoma patients using multi-center data

et al. 2022

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Aim To predict survival time of Korean hepatocellular carcinoma (HCC) patients using multi-center data as a foundation for the development of a predictive artificial intelligence model according to treatment methods based on machine learning. Methods Data of patients who underwent treatment for HCC from 2008 to 2015 was provided by Korean Liver Cancer Study Group and Korea Central Cancer Registry. A total of 10,742 patients with HCC were divided into two groups, with Group I (2920 patients) confirmed on biopsy and Group II (5562 patients) diagnosed as HCC according to HCC diagnostic criteria as outlined in Korean Liver Cancer Association guidelines. The data were modeled according to features of patient clinical characteristics. Features effective in predicting survival rate were analyzed retrospectively. Various machine learning methods were used. Results Target was overall survival time, which divided into approximately 60 months (= /< 60 m, > 60 m). Target distribution in Group I (total 514 samples) was 28.8%: (148 samples) less than 60 months, 71.2% (366 samples) greater than 60 months, and in Group II (total 757 samples) was 66.6% (504 samples) less than 60 months, 33.4% (253 samples) greater than 60 months. Using NG Boost method, its accuracy was 83%, precision 84%, sensitivity 95%, and F1 score 89% for more than 60 months survival time in Group I with surgical resection. Moreover, its accuracy was 79%, precision 82%, sensitivity 87%, and F1 score 84% for less than 60 months survival time in Group II with TACE. The feature importance with gain criterion indicated that pathology, portal vein invasion, surgery, metastasis, and needle biopsy features could be explained as important factors for prediction in case of biopsy (Group I). Conclusion By developing a predictive model using machine learning algorithms to predict prognosis of HCC patients, it is possible to project optimized treatment by case according to liver function and tumor status.

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Section: Methodsmentioning

confidence: 99%

Machine learning-based survival rate prediction of Korean hepatocellular carcinoma patients using multi-center data

et al. 2022

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“…NGBoost has already been used successfully to predict Evaporation and Evapotranspiration (Başagaoglu et al, 2020), air temperature (Peng et al, 2020), short-term solar irradiance (Zelikman et al, 2020) and, short-term prediction model of wind power (Li et al, 2020).…”

Section: Probabilistic Machine Learningmentioning

confidence: 99%

Application of Probabilistic Machine Learning to the prediction of Remotely Sensed Vegetation Health

Hammad¹

2021

Preprint

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Every prediction of the future carries some level of uncertainty; making this explicit is challenging. This paper introduces a Probabilistic Machine Learning algorithm, namely the Natural Gradient Boosting algorithm, as a modelling tool for predicting the Vegetation Health Index, a proxy for monitoring vegetation stress in response to changing weather conditions. It then elaborates on the time heterogeneity of the probabilistic predictions generated by the model over one year on West Java, the most populated Indonesia region, providing important consideration for Food Security. The results highlight that while 94% of the observed values fall within the model's 95% prediction interval, during February and March, the probability of reaching critically low levels of VHI below 40 is above 75%. The paper foster the multidisciplinary literature linking the use of remotely sensed vegetation health data with Food Security by incorporating uncertainty in predicting the impact of changing weather conditions on Food Security.

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“…To build a complete set of forecasting system from deterministic to probabilistic forecast, we focus on probabilistic forecasting at sub-seasonal scales. In the subseasonal domain, the integrated probabilistic model (Vigaud et al, 2019) and the machine learning (Peng et al, 2020) model achieve good results. After reflecting on the previous studies, we establish the connection between it and the probability distribution based on the error correction results in deterministic forecasts, and finally, build probabilistic forecast models for the next 30 days at the sub-seasonal scale.…”

Section: Introductionmentioning

confidence: 99%

Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast

Jin

Zhang²,

et al. 2022

Front. Earth Sci.

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The high temperature forecast of the sub-season is a severe challenge. Currently, the residual structure has achieved good results in the field of computer vision attributed to the excellent feature extraction ability. However, it has not been introduced in the domain of sub-seasonal forecasting. Here, we develop multi-module daily deterministic and probabilistic forecast models by the residual structure and finally establish a complete set of sub-seasonal high temperature forecasting system in the eastern part of China. The experimental results indicate that our method is effective and outperforms the European hindcast results in all aspects: absolute error, anomaly correlation coefficient, and other indicators are optimized by 8–50%, and the equitable threat score is improved by up to 400%. We conclude that the residual network has a sharper insight into the high temperature in sub-seasonal high temperature forecasting compared to traditional methods and convolutional networks, thus enabling more effective early warnings of extreme high temperature weather.

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Prediction Skill of Extended Range 2-m Maximum Air Temperature Probabilistic Forecasts Using Machine Learning Post-Processing Methods

Cited by 47 publications

References 36 publications

Machine learning-based survival rate prediction of Korean hepatocellular carcinoma patients using multi-center data

Machine learning-based survival rate prediction of Korean hepatocellular carcinoma patients using multi-center data

Application of Probabilistic Machine Learning to the prediction of Remotely Sensed Vegetation Health

Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast

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