scholarly journals Cokriging Prediction Using as Secondary Variable a Functional Random Field with Application in Environmental Pollution

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1305 ◽  
Author(s):  
Ramón Giraldo ◽  
Luis Herrera ◽  
Víctor Leiva
Keyword(s):  
Particulate Matter ◽  
Wind Speed ◽  
Environmental Pollution ◽  
Random Field ◽  
Spatial Prediction ◽  
Proposed Model ◽  
Cross Correlations ◽  
Primary Variable ◽  
Secondary Variable ◽  
Ordinary Cokriging

Cokriging is a geostatistical technique that is used for spatial prediction when realizations of a random field are available. If a secondary variable is cross-correlated with the primary variable, both variables may be employed for prediction by means of cokriging. In this work, we propose a predictive model that is based on cokriging when the secondary variable is functional. As in the ordinary cokriging, a co-regionalized linear model is needed in order to estimate the corresponding auto-correlations and cross-correlations. The proposed model is utilized for predicting the environmental pollution of particulate matter when considering wind speed curves as functional secondary variable.

scholarly journals Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

2021 ◽  
Vol 11 (14) ◽  
pp. 6590
Author(s):  
Krittakom Srijiranon ◽  
Narissara Eiamkanitchat
Keyword(s):  
Particulate Matter ◽  
Fuzzy Model ◽  
Original Data ◽  
Northern Region ◽  
National Standard ◽  
Minimum Entropy ◽  
Fuzzy Transformation ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Entropy Principle

Air pollution is a major global issue. In Thailand, this issue continues to increase every year, similar to other countries, especially during the dry season in the northern region. In this period, particulate matter with aerodynamic diameters smaller than 10 and 2.5 micrometers, known as PM10 and PM2.5, are important pollutants, most of which exceed the national standard levels, the so-called Thailand air quality index (T-AQI). Therefore, this study created a prediction model to classify T-AQI calculated from both types of PM. The neuro-fuzzy model with a minimum entropy principle model is proposed to transform the original data into new informative features. The processes in this model are able to discover appropriate separation points of the trapezoidal membership function by applying the minimum entropy principle. The membership value of the fuzzy section is then passed to the neural section to create a new data feature, the PM level, for each hour of the day. Finally, as an analytical process to obtain new knowledge, predictive models are created using new data features for better classification results. Various experiments were utilized to find an appropriate structure with high prediction accuracy. The results of the proposed model were favorable for predicting both types of PM up to three hours in advance. The proposed model can help people who are planning short-term outdoor activities.

scholarly journals Air pollution forecasting application based on deep learning model and optimization algorithm

2021 ◽  
Author(s):  
Azim Heydari ◽  
Meysam Majidi Nezhad ◽  
Davide Astiaso Garcia ◽  
Farshid Keynia ◽  
Livio De Santoli
Keyword(s):  
Air Pollution ◽  
Wind Speed ◽  
Power Plant ◽  
Air Temperature ◽  
Short Term Memory ◽  
Combined Cycle ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory

AbstractAir pollution monitoring is constantly increasing, giving more and more attention to its consequences on human health. Since Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are the major pollutants, various models have been developed on predicting their potential damages. Nevertheless, providing precise predictions is almost impossible. In this study, a new hybrid intelligent model based on long short-term memory (LSTM) and multi-verse optimization algorithm (MVO) has been developed to predict and analysis the air pollution obtained from Combined Cycle Power Plants. In the proposed model, long short-term memory model is a forecaster engine to predict the amount of produced NO2 and SO2 by the Combined Cycle Power Plant, where the MVO algorithm is used to optimize the LSTM parameters in order to achieve a lower forecasting error. In addition, in order to evaluate the proposed model performance, the model has been applied using real data from a Combined Cycle Power Plant in Kerman, Iran. The datasets include wind speed, air temperature, NO2, and SO2 for five months (May–September 2019) with a time step of 3-h. In addition, the model has been tested based on two different types of input parameters: type (1) includes wind speed, air temperature, and different lagged values of the output variables (NO2 and SO2); type (2) includes just lagged values of the output variables (NO2 and SO2). The obtained results show that the proposed model has higher accuracy than other combined forecasting benchmark models (ENN-PSO, ENN-MVO, and LSTM-PSO) considering different network input variables. Graphic abstract

New Key Factors Discovery to Enhance Dengue Fever Forecasting Model

2014 ◽  
Vol 931-932 ◽  
pp. 1457-1461 ◽  
Author(s):  
Phatsavee Ongruk ◽  
Padet Siriyasatien ◽  
Kraisak Kesorn
Keyword(s):  
Wind Speed ◽  
Dengue Fever ◽  
Forecast Model ◽  
Key Factors ◽  
Dengue Virus Infection ◽  
Conventional Model ◽  
Forecasting Error ◽  
Proposed Model ◽  
Basic Set ◽  
Almost All

There are several factors that can be used to predict a dengue fever outbreak. Almost all existing research approaches, however, usually exploit the use of a basic set of core attributes to forecast an outbreak, e.g. temperature, humidity, wind speed, and rainfall. In contrast, this research identifies new attributes to improve the prediction accuracy of the outbreak. The experimental results are analyzed using a correlation analysis and demonstrate that the density of dengue virus infection rate in female mosquitoes and seasons have strong correlation with a dengue fever outbreak. In addition, the research constructs a forecast model using Poisson regression analysis. The result shows the proposed model obtains significantly low forecasting error rate when compared it against the conventional model using only temperature, humidity, wind speed, and rainfall parameters.

Essays in Health and Policy in Saudi Arabia

2021 ◽  
Author(s):  
◽  
Ibrahim Alamir
Keyword(s):  
Cardiovascular Disease ◽  
Health Care ◽  
Saudi Arabia ◽  
Primary Health Care ◽  
Particulate Matter ◽  
Wind Speed ◽  
Central Hospital ◽  
Primary Health ◽  
Upper Respiratory

This dissertation is composed of three unrelated chapters, all of which are on different topics. Chapter 1 : The Effect of Wind Speed and Particulate Matter to the Emergency Depart- ment of King Fahad Central Hospital in the Jazan Region of Saudi Arabia by Those Suffering from Asthma. Chapter 2 : The Effect of Gasoline. Chapter 3 : The Effect of Dust and Sand Storms on Asthma, Pneumonia, Cardiovascular Disease, and Upper Respiratory Disease: Primary Health Care Visits in Jazan, Saudi Arabia Prices on Road Fatalities in Saudi Arabia

scholarly journals A New Hybrid Approach for Wind Speed Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Cuckoo Search Algorithm

2018 ◽  
Vol 8 (10) ◽  
pp. 1754 ◽  
Author(s):  
Tongxiang Liu ◽  
Shenzhong Liu ◽  
Jiani Heng ◽  
Yuyang Gao
Keyword(s):  
Wind Speed ◽  
Search Algorithm ◽  
Wind Farms ◽  
Cuckoo Search ◽  
Ensemble Empirical Mode Decomposition ◽  
Cuckoo Search Algorithm ◽  
Support Vector ◽  
Wind Speed Forecasting ◽  
Mode Decomposition ◽  
Proposed Model

Wind speed forecasting plays a crucial role in improving the efficiency of wind farms, and increases the competitive advantage of wind power in the global electricity market. Many forecasting models have been proposed, aiming to enhance the forecast performance. However, some traditional models used in our experiment have the drawback of ignoring the importance of data preprocessing and the necessity of parameter optimization, which often results in poor forecasting performance. Therefore, in order to achieve a more satisfying performance in forecasting wind speed data, a new short-term wind speed forecasting method which consists of Ensemble Empirical Mode Decomposition (EEMD) for data preprocessing, and the Support Vector Machine (SVM)—whose key parameters are optimized by the Cuckoo Search Algorithm (CSO)—is developed in this paper. This method avoids the shortcomings of some traditional models and effectively enhances the forecasting ability. To test the prediction ability of the proposed model, 10 min wind speed data from wind farms in Shandong Province, China, are used for conducting experiments. The experimental results indicate that the proposed model cannot only improve the forecasting accuracy, but can also be an effective tool in assisting the management of wind power plants.

Extra Large Sequence Transformer Model for Chinese Word Segment

2021 ◽  
Author(s):  
Dezhou Shen
Keyword(s):  
Random Field ◽  
Conditional Random Field ◽  
Document Analysis ◽  
Chinese Word ◽  
Test Dataset ◽  
Field Layer ◽  
Segment Model ◽  
Proposed Model ◽  
Transformer Model

Abstract Chinese word segment is widely studied in document analysis. The accuracy of the current popular word segment model, LSTM+CRF, is still not satisfactory. Models trained by the popular dataset often fails in the out-domain situation. In this paper, combining the Transformer-XL layer, the Fully-Connect layer, and the Conditional Random Field layer, the proposed model improved 3.23% in the macro-F1 score, comparing to the BERT+CRF model, on the MSR2005 Chinese word segment test dataset.

Overview

2018 ◽  
Author(s):  
Daniel K. Gardner
Keyword(s):  
Particulate Matter ◽  
Environmental Pollution

Why Should We Be Interested in China’s Environmental Pollution? Is it China’s problem alone when its particulate matter makes its way downwind to Korea and Japan, blanketing them with hazardous smog? Is it China’s problem alone when its particulate matter, carried by easterly winds, shows...

scholarly journals Analysis of air pollution caused by particle matter emission from the copper smelter complex Bor (Serbia)

2010 ◽  
Vol 16 (3) ◽  
pp. 219-228 ◽  
Author(s):  
Visa Tasic ◽  
Novica Milosevic ◽  
Renata Kovacevic ◽  
Nevenka Petrovic
Keyword(s):  
Particulate Matter ◽  
Wind Speed ◽  
Ultrafine Particles ◽  
Fine Particles ◽  
Copper Smelter ◽  
Temporal Variations ◽  
Copper Production ◽  
Copper Smelting ◽  
Particulate Emissions ◽  
Limit Values

The main aim of this paper is to present analyses of temporal variations of particulate matter in Bor (Serbia) influenced by copper production at the Copper Smelter Complex Bor. Particulate emissions are of concern because the presence of fine particles (PM2.5 - particles with diametar less than 2.5 ?m) and ultrafine particles (PM0.1 - particles with diametar less than 0.1 ?m) assume higher risk for human health. Such particles can penetrate deeper into respiratory organs and, at the same time, a probability for such penetration and deposition in the respiratory system is greater. The analysis is based on comparison of SO2 and PM measurements at several locations in the area of Bor town in the close vicinity of Copper Smelter. PM concentrations were highly correlated with sulfur dioxide and inversely correlated with local wind speed during pollution episodes. Presented results indicate that the dominant source of coarse and fine particles in Bor town is the Copper Smelting Complex Bor. The most significant factors for particulate matter distribution are meteorological parameters of wind speed and direction. It was found that exceeding of daily limit values of concentrations of PM10 (50 ?g/m3) usually occurs due to very high concentrations in a period of several hours during the day.

Mechanisms of Gas Turbine Regenerator Fouling

1967 ◽  
Author(s):  
James A. Miller
Keyword(s):  
Heat Transfer ◽  
Particulate Matter ◽  
Theoretical Model ◽  
Gas Turbine ◽  
Two Phase ◽  
Degradation Data ◽  
Proposed Model ◽  
Controlling Mechanism ◽  
Adhesive Coating ◽  
Phase Process

Possible mechanisms of gas turbine regenerator fouling are examined and compared with extant experimental evidence. A theoretical model of fouling which encompasses a two-phase process is proposed. It is shown that the controlling mechanism is the condensation of heavy hydrocarbon isomers which form an adhesive coating in which particulate matter subsequently become entrapped. Typical overall heat transfer and pressure drop degradation data are presented which tend to support the proposed model.

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