Multivariate polynomial regression prediction of primary productivity on U.S. coastal areas

Research Article
Open access

Multivariate polynomial regression prediction of primary productivity on U.S. coastal areas

Yabei Zeng 1*
  • 1 College of letters and Science, University of California, Santa Barbara, CA 93106 United States    
  • *corresponding author yabei_zeng@ucsb.edu
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230447
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

This paper analyzes the factors influencing the primary productivity of U.S. coastal areas in 2010. Understanding primary productivity is crucial to our understanding of the ecosystem and the biodiversity it supports. In this analysis, five parameters were taken into consideration, including longitude, latitude, total nitrogen, total phosphorus, and ammonia. Incorporating data from the Nation Coastal Condition Assessment (NCCA), this study mainly focused on the two models, multivariate polynomial regression and random forest, and general performance on the prediction accuracy. Comparing the results of the two models, the multivariate polynomial model does perform slightly better than that of the random forest model. Both of the two models yield good prediction models that can be further applied to the prediction of primary productivity rate for the ecology prediction and biodiversity prediction.

Keywords:

Primary Productivity, Multivariate Polynomial Regression, Random Forest, Prediction.

Zeng,Y. (2023). Multivariate polynomial regression prediction of primary productivity on U.S. coastal areas. Applied and Computational Engineering,6,875-881.
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References

[1]. The Editors of Encyclopaedia Britannica, 2022, Primary Productivity, Retrieved September 2nd, https://www.britannica.com/science/primary-productivity

[2]. Mathew Williams, Edward B. 1997. Rastetter, David N. Fernandes, Michael L. Goulden, Gaius R. Shaver, Loretta C. Johnson, Predicting Gross Primary Productivity in Terrestrial Ecosystems, Ecological Applications, 7(3), 882-894.

[3]. Mathew Williams, Edward B. 1999. Rastetter, Vegetation Characteristics and Primary Productivity Along an Arctic Transect: Implications for Scaling-Up, Journal of Ecology, 87(5), 885-898.

[4]. EPA 2022, Data from the National Aquatic Resource Surveys, Retrieved September 2nd, https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys

[5]. Kiran Parte 2020, Dimensionality Reduction Principal Component Analysis, Retrieved September 2nd, https://medium.com/analytics-vidhya/dimensionality-reduction-principal-component-analysis-d1402b58feb1

[6]. Lorraine Li 2019, Principal Component Analysis for Dimensionality Reduction, Retrieved September 2nd, https://towardsdatascience.com/principal-component-analysis-for-dimensionality-reduction-115a3d157bad

[7]. Tamas Ujhelyi 2021, Polynomial Regression in Python Using Scikit-Learn, Retrieved September 2nd, https://data36.com/polynomial-regression-python-scikit-learn/

[8]. Wikipedia 2022, Polynomial Regression, Retrieved September 2nd, https://en.wikipedia.org/wiki/Polynomial_regression

[9]. Priyanka Sinha, 2013. Multivariate Polynomial Regression in Data Mining: Methodology, Problems and Solutions, International Journal of Scientific and Engineering Research, 4(12), 962-965.

[10]. Tony Yiu 2019, Understanding Random Forest, Retrieved September 2nd, https://towardsdatascience.com/understanding-random-forest-58381e0602d2

[11]. TIBCO 2022, What is a Random Forest, Retrieved September 2nd, https://www.tibco.com/reference-center/what-is-a-random-forest

Cite this article

Zeng,Y. (2023). Multivariate polynomial regression prediction of primary productivity on U.S. coastal areas. Applied and Computational Engineering,6,875-881.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. The Editors of Encyclopaedia Britannica, 2022, Primary Productivity, Retrieved September 2nd, https://www.britannica.com/science/primary-productivity

[2]. Mathew Williams, Edward B. 1997. Rastetter, David N. Fernandes, Michael L. Goulden, Gaius R. Shaver, Loretta C. Johnson, Predicting Gross Primary Productivity in Terrestrial Ecosystems, Ecological Applications, 7(3), 882-894.

[3]. Mathew Williams, Edward B. 1999. Rastetter, Vegetation Characteristics and Primary Productivity Along an Arctic Transect: Implications for Scaling-Up, Journal of Ecology, 87(5), 885-898.

[4]. EPA 2022, Data from the National Aquatic Resource Surveys, Retrieved September 2nd, https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys

[5]. Kiran Parte 2020, Dimensionality Reduction Principal Component Analysis, Retrieved September 2nd, https://medium.com/analytics-vidhya/dimensionality-reduction-principal-component-analysis-d1402b58feb1

[6]. Lorraine Li 2019, Principal Component Analysis for Dimensionality Reduction, Retrieved September 2nd, https://towardsdatascience.com/principal-component-analysis-for-dimensionality-reduction-115a3d157bad

[7]. Tamas Ujhelyi 2021, Polynomial Regression in Python Using Scikit-Learn, Retrieved September 2nd, https://data36.com/polynomial-regression-python-scikit-learn/

[8]. Wikipedia 2022, Polynomial Regression, Retrieved September 2nd, https://en.wikipedia.org/wiki/Polynomial_regression

[9]. Priyanka Sinha, 2013. Multivariate Polynomial Regression in Data Mining: Methodology, Problems and Solutions, International Journal of Scientific and Engineering Research, 4(12), 962-965.

[10]. Tony Yiu 2019, Understanding Random Forest, Retrieved September 2nd, https://towardsdatascience.com/understanding-random-forest-58381e0602d2

[11]. TIBCO 2022, What is a Random Forest, Retrieved September 2nd, https://www.tibco.com/reference-center/what-is-a-random-forest

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