Artificial intelligence is widely applied to estimate ground-level fine particulate matter (PM2.5) from satellite data by constructing the relationship between the aerosol optical thickness (AOT) and the surface PM2.5 concentration. However, aerosol size properties, such as the fine mode fraction (FMF), are rarely considered in satellite-based PM2.5 modeling, especially in machine learning models. This study investigated the linear and non-linear relationships between fine mode AOT (fAOT) and PM2.5 over five AERONET stations in China (Beijing, Baotou, Taihu, Xianghe, and Xuzhou) using AERONET fAOT and 5-year (2015–2019) ground-level PM2.5 data. Results showed that the fAOT separated by the FMF (fAOT = AOT × FMF) had significant linear and non-linear relationships with surface PM2.5. Then, the Himawari-8 V3.0 and V2.1 FMF and AOT (FMFA haze days: R = 0.56) compared to AOT (dust days: R = 0.72; haze days: R = 0.52) partly contributed to the superior accuracy of FMF&AOT-PM2.5. This study demonstrates the importance of including the FMF to improve PM2.5 estimations and emphasizes the need for a more accurate FMF product that enables superior PM2.5 retrieval.

https://www.mdpi.com/2072-4292/13/14/2779/pdf

Superior PM2.5 Estimation by Integrating Aerosol Fine Mode Data from the Himawari-8 Satellite in Deep and Classical Machine Learning Models

A two-layer nested heterogeneous ensemble learning predictive method for COVID-19 mortality.

Reinforcement learning-based QoE-oriented dynamic adaptive streaming framework

Influence of statistical feature normalisation methods on K-Nearest Neighbours and K-Means in the context of industry 4.0

Two-stage stacking heterogeneous ensemble learning method for gasoline octane number loss prediction

A novel deep learning method for query task execution time prediction in graph database

LPG-model: A novel model for throughput prediction in stream processing, using a light gradient boosting machine, incremental principal component analysis, and deep gated recurrent unit network

In the era of big data, as the amount of streaming data continues to increase, stream processing tasks (SPTs) face serious challenges in real-time processing scenarios with low latency and high throughput. However, much of the current literature on the performance of SPTs pays attention to the reactive approach, which cannot well avoid the problem of system crashes due to the inherent performance volatility. In this paper, a novel throughput prediction method based on ExtraTree for SPTs is presented to address these challenges. A volatility detection algorithm was proposed to obtain the reasonable metric values after the performance volatility of SPTs was studied. Moreover, a selection algorithm of regression function was proposed to output the performance values of SPTs under a relative stead state. Furthermore, a ExtraTree-based algorithm was proposed to predict the throughput of SPTs. The experimental results from two open-source benchmarks running on Apache Flink, a popular stream processing system (SPS), indicated that the average of the accuracy and efficiency of the proposed method could achieve 90.535% and 0.835 s/10,000 samples, which proved the effectiveness of the proposed method on the task of predicting the throughput of SPTs.

/pdf/throughput-prediction-based-on-extratree-for-stream-59ycir416z.pdf

Zheng Chu

Papers

A novel deep learning method for query task execution time prediction in graph database

LPG-model: A novel model for throughput prediction in stream processing, using a light gradient boosting machine, incremental principal component analysis, and deep gated recurrent unit network

Throughput prediction based on ExtraTree for stream processing tasks