干旱区生态环境知识资源中心(Arid): The Integration of Multivariate Statistical Approaches, Hyperspectral Reflectance, and Data-Driven Modeling for Assessing the Quality and Suitability of Groundwater for Irrigation

Arid

DOI	10.3390/w13010035
	The Integration of Multivariate Statistical Approaches, Hyperspectral Reflectance, and Data-Driven Modeling for Assessing the Quality and Suitability of Groundwater for Irrigation
	Khadr, Mosaad; Gad, Mohamed; El-Hendawy, Salah; Al-Suhaibani, Nasser; Dewir, Yaser Hassan; Tahir, Muhammad Usman; Mubushar, Muhammad; Elsayed, Salah
通讯作者	El-Hendawy, S (corresponding author), King Saud Univ, Dept Plant Prod, Coll Food & Agr Sci, POB 2460, Riyadh 11451, Saudi Arabia. ; El-Hendawy, S (corresponding author), Suez Canal Univ, Dept Agron, Fac Agr, Ismailia 41522, Egypt.
来源期刊	WATER
EISSN	2073-4441
出版年	2021
卷号	13 期号:1
英文摘要	Sustainable agriculture in arid regions necessitates that the quality of groundwater be carefully monitored; otherwise, low-quality irrigation water may cause soil degradation and negatively impact crop productivity. This study aimed to evaluate the quality of groundwater samples collected from the wells in the quaternary aquifer, which are located in the Western Desert (WD) and the Central Nile Delta (CND), by integrating a multivariate analysis, proximal remote sensing data, and data-driven modeling (adaptive neuro-fuzzy inference system (ANFIS) and support vector machine regression (SVMR)). Data on the physiochemical parameters were subjected to multivariate analysis to ease the interpretation of groundwater quality. Then, six irrigation water quality indices (IWQIs) were calculated, and the original spectral reflectance (OSR) of groundwater samples were collected in the 302-1148 nm range, with the optimal spectral wavelength intervals corresponding to each of the six IWQIs determined through correlation coefficients (r). Finally, the performance of both the ANFIS and SVMR models for evaluating the IWQIs was investigated based on effective spectral reflectance bands. From the multivariate analysis, it was concluded that the combination of factor analysis and principal component analysis was found to be advantageous to examining and interpreting the behavior of groundwater quality in both regions, as well as predicting the variables that may impact groundwater quality by illuminating the relationship between physiochemical parameters and the factors or components of both analyses. The analysis of the six IWQIs revealed that the majority of groundwater samples from the CND were highly suitable for irrigation purposes, whereas most of the groundwater from the WD can be used with some limitations to avoid salinity and alkalinity issues in the long term. The high r values between the six IWQIs and OSR were located at wavelength intervals of 302-318, 358-900, and 1074-1148 nm, and the peak value of r for these was relatively flat. Finally, the ANFIS and SVMR both obtained satisfactory degrees of model accuracy for evaluating the IWQIs, but the ANFIS model (R-2 = 0.74-1.0) was superior to the SVMR (R-2 = 0.01-0.88) in both the training and testing series. Finally, the multivariate analysis was able to easily interpret groundwater quality and ground-based remote sensing on the basis of spectral reflectance bands via the ANFIS model, which could be used as a fast and low-cost onsite tool to estimate the IWQIs of groundwater.
英文关键词	adaptive neuro-fuzzy inference system arid regions factor analysis irrigation water quality indices principal component analysis physicochemical parameters proximal remote sensing support vector machine regression
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000606413900001
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
来源机构	King Saud University
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/348261
作者单位	[Khadr, Mosaad] Univ Bisha, Dept Civil Engn, Coll Engn, Bisha 61922, Saudi Arabia; [Khadr, Mosaad] Tanta Univ, Irrigat & Hydraul Dept, Fac Engn, Tanta 31734, Egypt; [Gad, Mohamed] Univ Sadat City, ESRI, Evaluat Nat Resources Dept, Hydrogeol, Menoufia 32897, Egypt; [El-Hendawy, Salah; Al-Suhaibani, Nasser; Dewir, Yaser Hassan; Tahir, Muhammad Usman; Mubushar, Muhammad] King Saud Univ, Dept Plant Prod, Coll Food & Agr Sci, POB 2460, Riyadh 11451, Saudi Arabia; [El-Hendawy, Salah] Suez Canal Univ, Dept Agron, Fac Agr, Ismailia 41522, Egypt; [Dewir, Yaser Hassan] Kafrelsheikh Univ, Fac Agr, Kafr Al Sheikh 33516, Egypt; [Elsayed, Salah] Univ Sadat City, Environm Studies & Res Inst, Evaluat Nat Resources Dept, Agr Engn, Menoufia 32897, Egypt
推荐引用方式 GB/T 7714	Khadr, Mosaad,Gad, Mohamed,El-Hendawy, Salah,et al. The Integration of Multivariate Statistical Approaches, Hyperspectral Reflectance, and Data-Driven Modeling for Assessing the Quality and Suitability of Groundwater for Irrigation[J]. King Saud University,2021,13(1).
APA	Khadr, Mosaad.,Gad, Mohamed.,El-Hendawy, Salah.,Al-Suhaibani, Nasser.,Dewir, Yaser Hassan.,...&Elsayed, Salah.(2021).The Integration of Multivariate Statistical Approaches, Hyperspectral Reflectance, and Data-Driven Modeling for Assessing the Quality and Suitability of Groundwater for Irrigation.WATER,13(1).
MLA	Khadr, Mosaad,et al."The Integration of Multivariate Statistical Approaches, Hyperspectral Reflectance, and Data-Driven Modeling for Assessing the Quality and Suitability of Groundwater for Irrigation".WATER 13.1(2021).