Modelling of Gully Erosion Site Data in Southeastern Nigeria, Using Poisson and Negative Binomial Regression Models

Ngozi Levi Nwakwasi

doi:doi:10.11648/j.jccee.20180304.13

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Modelling of Gully Erosion Site Data in Southeastern Nigeria, Using Poisson and Negative Binomial Regression Models

Ngozi Levi Nwakwasi

Published in Journal of Civil, Construction and Environmental Engineering (Volume 3, Issue 4)

Received: 3 September 2018 Accepted: 21 September 2018 Published: 25 October 2018

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Abstract

The development of gully and other forms of erosion have become the greatest environmental problem facing the people of Southeastern Nigeria. The availability of farm land for agricultural production and construction activities have, been greatly reduced due to soil erosion. This study is set to apply Poisson and negative binomial regression models to identify the major factors that contribute to gully erosion development in Southeastern Nigeria and to ascertain better model suitable for prediction of gully erosion, using secondary data. Maximum likelihood estimation procedure was used to estimate the parameter of the selected model with the number of gully erosion sites as the response variable (Y) and 5-explanatory variable (X’s). Also applying the forward selection criteria to the 5-explanatory variables, model 5 is best suitable for forecasting the subject under study. The result of the Poisson regression model showed that there was over dispersion in gully erosion site data since the dispersion parameter (3.677) was greater than 1 hence underestimating the standard error and over estimating the coefficient of the explanatory variable, consequently giving misleading inference. The result of the assessment criteria for Poisson regression model and Negative binomial regression model revealed that the Negative binomial regression model predicts gully erosion soil data better in southeastern Nigeria as considered in this study. Heavy Rainfall (HRF), Extractive Industries (EXI), Excess Farm activities (EFX) are the major contributors to gully erosion site development in southeastern Nigeria, with Heavy Rainfall ranking first. A model suitable for prediction of gully erosion sites in southeastern Nigeria has been developed.

Published in	Journal of Civil, Construction and Environmental Engineering (Volume 3, Issue 4)
DOI	10.11648/j.jccee.20180304.13
Page(s)	111-117
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Gully Erosion, Rainfall, Soil, Poisson Regression Model, Negative Binomial Regression Model, Akaike Information Criteria (AIC)

References

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APA Style

Ngozi Levi Nwakwasi. (2018). Modelling of Gully Erosion Site Data in Southeastern Nigeria, Using Poisson and Negative Binomial Regression Models. Journal of Civil, Construction and Environmental Engineering, 3(4), 111-117. https://doi.org/10.11648/j.jccee.20180304.13

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ACS Style

Ngozi Levi Nwakwasi. Modelling of Gully Erosion Site Data in Southeastern Nigeria, Using Poisson and Negative Binomial Regression Models. J. Civ. Constr. Environ. Eng. 2018, 3(4), 111-117. doi: 10.11648/j.jccee.20180304.13

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AMA Style

Ngozi Levi Nwakwasi. Modelling of Gully Erosion Site Data in Southeastern Nigeria, Using Poisson and Negative Binomial Regression Models. J Civ Constr Environ Eng. 2018;3(4):111-117. doi: 10.11648/j.jccee.20180304.13

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@article{10.11648/j.jccee.20180304.13,
  author = {Ngozi Levi Nwakwasi},
  title = {Modelling of Gully Erosion Site Data in Southeastern Nigeria, Using Poisson and Negative Binomial Regression Models},
  journal = {Journal of Civil, Construction and Environmental Engineering},
  volume = {3},
  number = {4},
  pages = {111-117},
  doi = {10.11648/j.jccee.20180304.13},
  url = {https://doi.org/10.11648/j.jccee.20180304.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jccee.20180304.13},
  abstract = {The development of gully and other forms of erosion have become the greatest environmental problem facing the people of Southeastern Nigeria. The availability of farm land for agricultural production and construction activities have, been greatly reduced due to soil erosion. This study is set to apply Poisson and negative binomial regression models to identify the major factors that contribute to gully erosion development in Southeastern Nigeria and to ascertain better model suitable for prediction of gully erosion, using secondary data. Maximum likelihood estimation procedure was used to estimate the parameter of the selected model with the number of gully erosion sites as the response variable (Y) and 5-explanatory variable (X’s). Also applying the forward selection criteria to the 5-explanatory variables, model 5 is best suitable for forecasting the subject under study. The result of the Poisson regression model showed that there was over dispersion in gully erosion site data since the dispersion parameter (3.677) was greater than 1 hence underestimating the standard error and over estimating the coefficient of the explanatory variable, consequently giving misleading inference. The result of the assessment criteria for Poisson regression model and Negative binomial regression model revealed that the Negative binomial regression model predicts gully erosion soil data better in southeastern Nigeria as considered in this study. Heavy Rainfall (HRF), Extractive Industries (EXI), Excess Farm activities (EFX) are the major contributors to gully erosion site development in southeastern Nigeria, with Heavy Rainfall ranking first. A model suitable for prediction of gully erosion sites in southeastern Nigeria has been developed.},
 year = {2018}
}

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TY  - JOUR
T1  - Modelling of Gully Erosion Site Data in Southeastern Nigeria, Using Poisson and Negative Binomial Regression Models
AU  - Ngozi Levi Nwakwasi
Y1  - 2018/10/25
PY  - 2018
N1  - https://doi.org/10.11648/j.jccee.20180304.13
DO  - 10.11648/j.jccee.20180304.13
T2  - Journal of Civil, Construction and Environmental Engineering
JF  - Journal of Civil, Construction and Environmental Engineering
JO  - Journal of Civil, Construction and Environmental Engineering
SP  - 111
EP  - 117
PB  - Science Publishing Group
SN  - 2637-3890
UR  - https://doi.org/10.11648/j.jccee.20180304.13
AB  - The development of gully and other forms of erosion have become the greatest environmental problem facing the people of Southeastern Nigeria. The availability of farm land for agricultural production and construction activities have, been greatly reduced due to soil erosion. This study is set to apply Poisson and negative binomial regression models to identify the major factors that contribute to gully erosion development in Southeastern Nigeria and to ascertain better model suitable for prediction of gully erosion, using secondary data. Maximum likelihood estimation procedure was used to estimate the parameter of the selected model with the number of gully erosion sites as the response variable (Y) and 5-explanatory variable (X’s). Also applying the forward selection criteria to the 5-explanatory variables, model 5 is best suitable for forecasting the subject under study. The result of the Poisson regression model showed that there was over dispersion in gully erosion site data since the dispersion parameter (3.677) was greater than 1 hence underestimating the standard error and over estimating the coefficient of the explanatory variable, consequently giving misleading inference. The result of the assessment criteria for Poisson regression model and Negative binomial regression model revealed that the Negative binomial regression model predicts gully erosion soil data better in southeastern Nigeria as considered in this study. Heavy Rainfall (HRF), Extractive Industries (EXI), Excess Farm activities (EFX) are the major contributors to gully erosion site development in southeastern Nigeria, with Heavy Rainfall ranking first. A model suitable for prediction of gully erosion sites in southeastern Nigeria has been developed.
VL  - 3
IS  - 4
ER  -

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Author Information

Ngozi Levi Nwakwasi

Department of Civil Engineering SEET, Federal University of Technology, Owerri, Nigeria

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Plain Text BibTeX RIS

APA Style

Ngozi Levi Nwakwasi. (2018). Modelling of Gully Erosion Site Data in Southeastern Nigeria, Using Poisson and Negative Binomial Regression Models. Journal of Civil, Construction and Environmental Engineering, 3(4), 111-117. https://doi.org/10.11648/j.jccee.20180304.13

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ACS Style

Ngozi Levi Nwakwasi. Modelling of Gully Erosion Site Data in Southeastern Nigeria, Using Poisson and Negative Binomial Regression Models. J. Civ. Constr. Environ. Eng. 2018, 3(4), 111-117. doi: 10.11648/j.jccee.20180304.13

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AMA Style

Ngozi Levi Nwakwasi. Modelling of Gully Erosion Site Data in Southeastern Nigeria, Using Poisson and Negative Binomial Regression Models. J Civ Constr Environ Eng. 2018;3(4):111-117. doi: 10.11648/j.jccee.20180304.13

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@article{10.11648/j.jccee.20180304.13,
  author = {Ngozi Levi Nwakwasi},
  title = {Modelling of Gully Erosion Site Data in Southeastern Nigeria, Using Poisson and Negative Binomial Regression Models},
  journal = {Journal of Civil, Construction and Environmental Engineering},
  volume = {3},
  number = {4},
  pages = {111-117},
  doi = {10.11648/j.jccee.20180304.13},
  url = {https://doi.org/10.11648/j.jccee.20180304.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jccee.20180304.13},
  abstract = {The development of gully and other forms of erosion have become the greatest environmental problem facing the people of Southeastern Nigeria. The availability of farm land for agricultural production and construction activities have, been greatly reduced due to soil erosion. This study is set to apply Poisson and negative binomial regression models to identify the major factors that contribute to gully erosion development in Southeastern Nigeria and to ascertain better model suitable for prediction of gully erosion, using secondary data. Maximum likelihood estimation procedure was used to estimate the parameter of the selected model with the number of gully erosion sites as the response variable (Y) and 5-explanatory variable (X’s). Also applying the forward selection criteria to the 5-explanatory variables, model 5 is best suitable for forecasting the subject under study. The result of the Poisson regression model showed that there was over dispersion in gully erosion site data since the dispersion parameter (3.677) was greater than 1 hence underestimating the standard error and over estimating the coefficient of the explanatory variable, consequently giving misleading inference. The result of the assessment criteria for Poisson regression model and Negative binomial regression model revealed that the Negative binomial regression model predicts gully erosion soil data better in southeastern Nigeria as considered in this study. Heavy Rainfall (HRF), Extractive Industries (EXI), Excess Farm activities (EFX) are the major contributors to gully erosion site development in southeastern Nigeria, with Heavy Rainfall ranking first. A model suitable for prediction of gully erosion sites in southeastern Nigeria has been developed.},
 year = {2018}
}

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TY  - JOUR
T1  - Modelling of Gully Erosion Site Data in Southeastern Nigeria, Using Poisson and Negative Binomial Regression Models
AU  - Ngozi Levi Nwakwasi
Y1  - 2018/10/25
PY  - 2018
N1  - https://doi.org/10.11648/j.jccee.20180304.13
DO  - 10.11648/j.jccee.20180304.13
T2  - Journal of Civil, Construction and Environmental Engineering
JF  - Journal of Civil, Construction and Environmental Engineering
JO  - Journal of Civil, Construction and Environmental Engineering
SP  - 111
EP  - 117
PB  - Science Publishing Group
SN  - 2637-3890
UR  - https://doi.org/10.11648/j.jccee.20180304.13
AB  - The development of gully and other forms of erosion have become the greatest environmental problem facing the people of Southeastern Nigeria. The availability of farm land for agricultural production and construction activities have, been greatly reduced due to soil erosion. This study is set to apply Poisson and negative binomial regression models to identify the major factors that contribute to gully erosion development in Southeastern Nigeria and to ascertain better model suitable for prediction of gully erosion, using secondary data. Maximum likelihood estimation procedure was used to estimate the parameter of the selected model with the number of gully erosion sites as the response variable (Y) and 5-explanatory variable (X’s). Also applying the forward selection criteria to the 5-explanatory variables, model 5 is best suitable for forecasting the subject under study. The result of the Poisson regression model showed that there was over dispersion in gully erosion site data since the dispersion parameter (3.677) was greater than 1 hence underestimating the standard error and over estimating the coefficient of the explanatory variable, consequently giving misleading inference. The result of the assessment criteria for Poisson regression model and Negative binomial regression model revealed that the Negative binomial regression model predicts gully erosion soil data better in southeastern Nigeria as considered in this study. Heavy Rainfall (HRF), Extractive Industries (EXI), Excess Farm activities (EFX) are the major contributors to gully erosion site development in southeastern Nigeria, with Heavy Rainfall ranking first. A model suitable for prediction of gully erosion sites in southeastern Nigeria has been developed.
VL  - 3
IS  - 4
ER  -

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