Data - Based Pollution Forecasting via Machine Learning: Case of Northwest Texas

Mbarak, Ahmed; Yetis, Yunus; Jamshidi, Mo

doi:10.23919/wac.2018.8430438

Cited by 5 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is used in predicting time series and regression, and it has been reported to show good results and overcome many shortcomings associated with MLP. In this paper, scholars say a prediction model grounded on Least Square Support Vector Machines for wind and weather data, which reveal good results (Mbarak et al 2018).…”

Section: Least Squares Support Vector Machine Modelmentioning

confidence: 99%

Wind Speed Forecasting using Machine Learning Approach based on Meteorological Data-A case study

Yetis¹,

Tehrani²,

Jamshidi³

2022

EER

Self Cite

View full text Add to dashboard Cite

This paper presents a forecasting method to anticipate wind speed accurately. This method is applied to an energy production site using an artificial intelligence method based on machine learning. The accuracy of this method is higher compared to other existing methods in the literature for time series analysis such as artificial neural networks and the complexity of wind speed prediction models without loss of information content. We tested several thousand data between 1985 and 2018 in the city of Basel, a city in northwestern Switzerland. We have used the MATLAB Software for this modeling. The study demonstrates that the use of statistical models based on machine learning is relevant to predict of speed and direction of the wind in power generation systems from meteorological data. The results obtained are presented and discussed.

show abstract

Section: Least Squares Support Vector Machine Modelmentioning

confidence: 99%

Wind Speed Forecasting using Machine Learning Approach based on Meteorological Data-A case study

Yetis¹,

Tehrani²,

Jamshidi³

2022

EER

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is obvious that by making a focus on this area it will cause to have a more clean city with much more financial concerns [9], [17], [18]. Mbarak et al in [19] have proposed a method for pollution forecasting via machine learning. They have proposed their system to be practical in Northwest Texas.…”

Section: B Environmentmentioning

confidence: 99%

“…They have proposed their system to be practical in Northwest Texas. One of their proposed method's advantages is to determine how much green energy should be invested to cut down the pollutants seen on the city [19].…”

Section: B Environmentmentioning

confidence: 99%

See 1 more Smart Citation

A Brief Survey on Smart Community and Smart Transportation

Azgomi

Jamshidi

2018

2018 IEEE 30th International Conference on Tools With Artificial Intelligence (ICTAI)

Self Cite

View full text Add to dashboard Cite

World population growing in conjunction with the preference to live in the cities; make the city management a challenging issue. Traditional Cities with their common features will not be able to handle the human needs. As a result, smart city and its beneficial outcomes will attract a lot of attention recently. In fact, Smart city/community that is the field of collecting and processing data from so many different areas while making proper decisions and feeding to all parts of the system will be the future style of the cities. In other words, smart community is a complex big data problem which should combine different fields of research to make a unit environment. This brief survey will deal with all aspects of the smart communities. There will also be explained different categories of the smart communities in addition to their future challenges. In this work we try to introduce some aspects of the smart transportation in more detail.

show abstract

Assessing the Role of Temporal Information in Modelling Short-Term Air Pollution Effects Based on Traffic and Meteorological Conditions: A Case Study in Wrocław

Brunello

Kamińska

Marzano³

et al. 2019

Communications in Computer and Information Science

View full text Add to dashboard Cite

Data - Based Pollution Forecasting via Machine Learning: Case of Northwest Texas

Cited by 5 publications

References 7 publications

Wind Speed Forecasting using Machine Learning Approach based on Meteorological Data-A case study

Wind Speed Forecasting using Machine Learning Approach based on Meteorological Data-A case study

A Brief Survey on Smart Community and Smart Transportation

Assessing the Role of Temporal Information in Modelling Short-Term Air Pollution Effects Based on Traffic and Meteorological Conditions: A Case Study in Wrocław

Contact Info

Product

Resources

About