Performance evaluation of agricultural drainage water using modeling and statistical approaches

Mahmoud Nasr, Hoda Farouk Zahran

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This study assessed spatial variations in physical and chemical properties of an agricultural drain near Borg El-Arab city, Alexandria, Egypt. Pearson's correlation coefficient indicated that salinity had strong correlations with total dissolved solids (TDS) (r 0.999, p < 0.001) and Cl (r 0.807, p 0.016), whereas, pH was considerably affected by temperature (r 0.674, p 0.067), oxidation reduction potential (ORP) (r 0.866, p 0.006) and NO3 (r 0.731, p 0.039). Those results were further confirmed by applying an adaptive neuro-fuzzy inference system and regression models. Moreover, principal component analysis (PCA) indicated that PC1 explained 41.1% of the total variance, and had high loadings of TDS (0.46), salinity (0.46) and Cl (0.48). Additionally, PC2 accounted for 35.2% of the total variance, and had high loadings of pH (0.53), temperature (0.48), ORP (0.40) and NO3 (0.47). The present study revealed that artificial intelligence and PCA could be used to effectively reduce the number of physicochemical parameters that may assist in the description of drainage water quality. It is recommended that the current status of the drain is suitable for reuse in irrigation purposes except at few locations containing high salinity.

Original languageEnglish
Pages (from-to)141-148
Number of pages8
JournalEgyptian Journal of Aquatic Research
Volume42
Issue number2
DOIs
Publication statusPublished - 1 Jun 2016
Externally publishedYes

Fingerprint

drainage water
total dissolved solids
redox potential
salinity
drain
principal component analysis
modeling
Arabs
artificial intelligence
Egypt
spatial variation
chemical property
physical properties
temperature
physicochemical properties
water quality
physical property
irrigation
evaluation
oxidation-reduction

Keywords

  • Adaptive neuro-fuzzy inference system
  • Drainage water
  • Environmental condition
  • Pearson's correlation coefficient
  • Principal component analysis

ASJC Scopus subject areas

  • Oceanography
  • Ecology, Evolution, Behavior and Systematics
  • Aquatic Science
  • Water Science and Technology

Cite this

Performance evaluation of agricultural drainage water using modeling and statistical approaches. / Nasr, Mahmoud; Zahran, Hoda Farouk.

In: Egyptian Journal of Aquatic Research, Vol. 42, No. 2, 01.06.2016, p. 141-148.

Research output: Contribution to journalArticle

@article{4eadf60b65d04e749d34c08aa4ab8768,
title = "Performance evaluation of agricultural drainage water using modeling and statistical approaches",
abstract = "This study assessed spatial variations in physical and chemical properties of an agricultural drain near Borg El-Arab city, Alexandria, Egypt. Pearson's correlation coefficient indicated that salinity had strong correlations with total dissolved solids (TDS) (r 0.999, p < 0.001) and Cl− (r 0.807, p 0.016), whereas, pH was considerably affected by temperature (r 0.674, p 0.067), oxidation reduction potential (ORP) (r 0.866, p 0.006) and NO3 − (r 0.731, p 0.039). Those results were further confirmed by applying an adaptive neuro-fuzzy inference system and regression models. Moreover, principal component analysis (PCA) indicated that PC1 explained 41.1{\%} of the total variance, and had high loadings of TDS (0.46), salinity (0.46) and Cl− (0.48). Additionally, PC2 accounted for 35.2{\%} of the total variance, and had high loadings of pH (0.53), temperature (0.48), ORP (0.40) and NO3 − (0.47). The present study revealed that artificial intelligence and PCA could be used to effectively reduce the number of physicochemical parameters that may assist in the description of drainage water quality. It is recommended that the current status of the drain is suitable for reuse in irrigation purposes except at few locations containing high salinity.",
keywords = "Adaptive neuro-fuzzy inference system, Drainage water, Environmental condition, Pearson's correlation coefficient, Principal component analysis",
author = "Mahmoud Nasr and Zahran, {Hoda Farouk}",
year = "2016",
month = "6",
day = "1",
doi = "10.1016/j.ejar.2016.04.006",
language = "English",
volume = "42",
pages = "141--148",
journal = "Egyptian Journal of Aquatic Research",
issn = "1687-4285",
publisher = "National Institute of Oceanography and Fisheries",
number = "2",

}

TY - JOUR

T1 - Performance evaluation of agricultural drainage water using modeling and statistical approaches

AU - Nasr, Mahmoud

AU - Zahran, Hoda Farouk

PY - 2016/6/1

Y1 - 2016/6/1

N2 - This study assessed spatial variations in physical and chemical properties of an agricultural drain near Borg El-Arab city, Alexandria, Egypt. Pearson's correlation coefficient indicated that salinity had strong correlations with total dissolved solids (TDS) (r 0.999, p < 0.001) and Cl− (r 0.807, p 0.016), whereas, pH was considerably affected by temperature (r 0.674, p 0.067), oxidation reduction potential (ORP) (r 0.866, p 0.006) and NO3 − (r 0.731, p 0.039). Those results were further confirmed by applying an adaptive neuro-fuzzy inference system and regression models. Moreover, principal component analysis (PCA) indicated that PC1 explained 41.1% of the total variance, and had high loadings of TDS (0.46), salinity (0.46) and Cl− (0.48). Additionally, PC2 accounted for 35.2% of the total variance, and had high loadings of pH (0.53), temperature (0.48), ORP (0.40) and NO3 − (0.47). The present study revealed that artificial intelligence and PCA could be used to effectively reduce the number of physicochemical parameters that may assist in the description of drainage water quality. It is recommended that the current status of the drain is suitable for reuse in irrigation purposes except at few locations containing high salinity.

AB - This study assessed spatial variations in physical and chemical properties of an agricultural drain near Borg El-Arab city, Alexandria, Egypt. Pearson's correlation coefficient indicated that salinity had strong correlations with total dissolved solids (TDS) (r 0.999, p < 0.001) and Cl− (r 0.807, p 0.016), whereas, pH was considerably affected by temperature (r 0.674, p 0.067), oxidation reduction potential (ORP) (r 0.866, p 0.006) and NO3 − (r 0.731, p 0.039). Those results were further confirmed by applying an adaptive neuro-fuzzy inference system and regression models. Moreover, principal component analysis (PCA) indicated that PC1 explained 41.1% of the total variance, and had high loadings of TDS (0.46), salinity (0.46) and Cl− (0.48). Additionally, PC2 accounted for 35.2% of the total variance, and had high loadings of pH (0.53), temperature (0.48), ORP (0.40) and NO3 − (0.47). The present study revealed that artificial intelligence and PCA could be used to effectively reduce the number of physicochemical parameters that may assist in the description of drainage water quality. It is recommended that the current status of the drain is suitable for reuse in irrigation purposes except at few locations containing high salinity.

KW - Adaptive neuro-fuzzy inference system

KW - Drainage water

KW - Environmental condition

KW - Pearson's correlation coefficient

KW - Principal component analysis

UR - http://www.scopus.com/inward/record.url?scp=84969513777&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84969513777&partnerID=8YFLogxK

U2 - 10.1016/j.ejar.2016.04.006

DO - 10.1016/j.ejar.2016.04.006

M3 - Article

AN - SCOPUS:84969513777

VL - 42

SP - 141

EP - 148

JO - Egyptian Journal of Aquatic Research

JF - Egyptian Journal of Aquatic Research

SN - 1687-4285

IS - 2

ER -