Performance evaluation of agricultural drainage water using modeling and statistical approaches

详细信息    查看全文

• 作者：Mahmoud Nasr^a ; Hoda Farouk Zahran^b ; ^{hfzahran@gmail.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Adaptive neuro-fuzzy inference system ; Principal component analysis ; Pearson&rsquo ; s correlation coefficient ; Drainage water ; Environmental condition
• 刊名：The Egyptian Journal of Aquatic Research
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：42
• 期：2
• 页码：141-148
• 全文大小：522 K

文摘

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.