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

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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[2] A. C. Emeribeole and C. J. lheaturu, Mapping of potential soil erosion risk areas in Imo State using the Revised Universal Soil Loss (RUSLE), Remote Sensing (RS) and Geospatial Information System (GIS) Techniques. International Journal for Research in Emerging Science and Technology, Vol. 2, Issue-10, Oct-2015.
[3] Y. Farhan, Z. Dalal, and F. Ibrahim, Spatial Estimation of Soil Erosion Risk Using RUSLE Approach, RS, and GIS Techniques: A Case Study of Kufranja Watershed, Northern Jordan. Journal of Water Resource and Protection, vol. 5, pp. 1247- 1261, 2013.
[4] Ume, N. C, Enwereuzor, A. I., Egbe, C. A, Ike, M. C. and S. J. Umo (2014). Application of Geographic information system and remote sensing in identifying the impacts of gully eroding in Urualla, Ideato North, Local Government area, Imo state. Nigeria Global Research Journal of Science 3 (3):1-8.
[5] Idowu, O. J and Oluwatosin, G. A. (2008), Hydraulic Properties in relation to morphology of a tropical soil with hardened plinthite under time land use types. Tropical and Sub-tropical Agro Ecosystems 8 (4): 145-155.
[6] Ofomata, G. E. K. (1985). Soil Erosion in Nigeria. The Views of a Geomorphologist University of Nigeria Inaugural Lecture Series No.7 University of Nigeria Press Nsukka, Nigeria.
[7] Adekalu, K. O. Olorunfemi I. A. and J. A. Osunbitan (2007) Grass mulching effect on infiltration, surface runoff and soil loss of three agricultural soils in Nigeria. Bio resource technology, 98(4): 912 – 913.
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[10] Egboka, B. C. E (2004). Distress call and plea to the senate committee for urgent Actions Against Floods, Soil/Gully erosion/landslides Disasters in the southeastern Nigeria, paper presented to senate committee on Environmental; roads/Erosion senate delegation.
[11] Gunawan G., Dwita S., Herr, S., and W. Sulostiowemi, (2013), Soil Erosion Estimation based on GIS and Remote Sensing for Supporting Integrated Water Resources Conservation Management, International Journal of Technology, 2 (3): 147-156.
[12] FAO (2011). The State of Food Insecurity in the World, special world report of FAO-UN, Rome, 2011: 11-14.
[13] Okoroafor O. O. Akinbile C. O, Adeyemo, A. J., (2017), Soil Erosion in Southeastern Nigeria a Review.
[14] Igwe, C. A., (2012), Gully Erosion in Southeastern Nigeria: Role of Soil Properties and Environmental Factors; InTech Open Science, pp.157-171.
[15] Ufot, U. O., Iren, 0. B., and C. U. Chikere-Njolcu (2016). Effects of and use on Soil physical and chemical properties in Akokwa are of Imo State, Nigeria. International Journal of Life, Science and Scientific Research, 2 (3):1-6.
[16] George, NA, Obot, I. and Akpanetuk, N (2008). Geoelectrical investigation of erosion and flooding using the lithologic compositions of erosion and flood-stricken road in Ukanafun Local Government Area, Akwa Ibom State, Southern Nigeria. Disaster Advancement., 1 (4): 46-51.
[17] Osadebe, CC, and Akpokodje, E. G (2007). Statistical analysis of variability in properties of soils in gully erosion sites of Agulu-Nanka-Oko area, southeastern Nigeria. Journal of Mining Geology 43 (2): 197-202.
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[19] Ezezika, O. O and O. Adetona (2011). Resolving the gully erosion problems in southeastern Nigeria: Innovation through public awareness and connectivity based approaches. Journal of Soil Science and Environmental management 2 (10): 286-291.
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Cite This Article
  • 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
  • Department of Civil Engineering SEET, Federal University of Technology, Owerri, Nigeria

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