A review of datasets and methods for deriving spatiotemporal distributions of atmospheric CO2.

Machine learning may accelerate the recognition and control of microplastic pollution: Future prospects.

As one of the most critical elements in the hydrological cycle, real-time and accurate rainfall measurement is of great significance to flood and drought disaster risk assessment and early warning. Using commercial microwave links (CMLs) to conduct rainfall measure is a promising solution due to the advantages of high spatial resolution, low implementation cost, near-surface measurement, and so on. However, because of the temporal and spatial dynamics of rainfall and the atmospheric influence, it is necessary to go through complicated signal processing steps from signal attenuation analysis of a CML to rainfall map. This article first introduces the basic principle and the revolution of CML-based rainfall measurement. Then, the article illustrates different steps of signal process in CML-based rainfall measurement, reviewing the state of the art solutions in each step. In addition, uncertainties and errors involved in each step of signal process as well as their impacts on the accuracy of rainfall measurement are analyzed. Moreover, the article also discusses how machine learning technologies facilitate CML-based rainfall measurement. Additionally, the applications of CML in monitoring phenomena other than rain and the hydrological simulation are summarized. Finally, the challenges and future directions are discussed.

/pdf/a-review-on-rainfall-measurement-based-on-commercial-12bas8bc.pdf

A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks

A case study application of machine-learning for the detection of greenhouse gas emission sources

The opportunity for large amounts of open-for-public and available data is one of the main drivers of the development of an information society at the beginning of the 21st century. In this sense, acquiring knowledge from these data using different methods of machine learning is a prerequisite for solving complex problems in many spheres of human activity, starting from medicine to education and the economy, including traffic as today’s important economic branch. Having this in mind, this paper deals with the prediction of the risk of traffic incidents using both historical and real-time data for different atmospheric factors. The main goal is to construct an ensemble model based on the use of several machine learning algorithms which has better characteristics of prediction than any of those installed when individually applied. In global, a case-proposed model could be a multi-agent system, but in a considered case study, a two-agent system is used so that one agent solves the prediction task by learning from the historical data, and the other agent uses the real time data. The authors evaluated the obtained model based on a case study and data for the city of Niš from the Republic of Serbia and also described its implementation as a practical web citizen application.

/pdf/using-machine-learning-in-predicting-the-impact-of-27omg9on.pdf

Using Machine Learning in Predicting the Impact of Meteorological Parameters on Traffic Incidents

Machine learning (ML) plays an important role in atmospheric environment prediction, having been widely applied in atmospheric science with significant progress in algorithms and hardware. In this paper, we present a brief overview of the development of ML models as well as their application to atmospheric environment studies. ML model performance is then compared based on the main air pollutants (i.e., PM2.5, O3, and NO2) and model type. Moreover, we identify the key driving variables for ML models in predicting particulate matter (PM) pollutants by quantitative statistics. Additionally, a case study for wet nitrogen deposition estimation is carried out based on ML models. Finally, the prospects of ML for atmospheric prediction are discussed.

The Development and Application of Machine Learning in Atmospheric Environment Studies

Objective: Based on a dataset with few features predicts a patient’s likelihood of developing sepsis in the future. Method: We propose a compound ensemble learning framework. At the first stage, UMAP and XgBoost are explored for dimensionality reduction, meanwhile, weak classifiers such as KELM, 1D Convolutional Neural Networks, and XgBoost are employed as base learners to learn patient characteristics from four different emphases. The output of base learners is processed using the proposed Res-Stacking and modified Bagging-like methods in the first stage of compound ensemble learning framework to reduce the error rate. In the secondary ensemble learning stage, the D-S evidence theory is employed to fuse the results of the above two methods and BiLSTM, thereby diminishing the uncertainty of the final output and obtaining more prominent prediction result. Result: The performance of our proposed compound ensemble framework on the clinical dataset of the First Affiliated Hospital of Dalian Medical University in terms of sensitivity, specificity, diagnostic efficiency, PPV, NPV, F1 score and AUC are 0.875, 0.95, 0.928, 0.95, 0.875, 0.95, 0.9125, respectively, better than the single ensemble learning method.

Rui Lin

Papers

The Development and Application of Machine Learning in Atmospheric Environment Studies

Predicting The Likelihood of Patients Developing Sepsis Based on Compound Ensemble Learning