The Integration of Multivariate Statistical Approaches, Hyperspectral Reflectance, and Data-Driven Modeling for Assessing the Qu
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water Article The Integration of Multivariate Statistical Approaches, Hyperspectral Reflectance, and Data-Driven Modeling for Assessing the Quality and Suitability of Groundwater for Irrigation Mosaad Khadr 1,2 , Mohamed Gad 3 , Salah El-Hendawy 4,5,*, Nasser Al-Suhaibani 4, Yaser Hassan Dewir 4,6 , Muhammad Usman Tahir 4, Muhammad Mubushar 4 and Salah Elsayed 7 1 Civil Engineering Department, College of Engineering, University of Bisha, Bisha 61922, Saudi Arabia; [email protected] 2 Irrigation and Hydraulics Department, Faculty of Engineering, Tanta University, Tanta 31734, Egypt 3 Hydrogeology, Evaluation of Natural Resources Department, Environmental Studies and Research Institute (ESRI), University of Sadat City, Minufiya 32897, Egypt; [email protected] 4 Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, KSA, P.O. Box 2460, Riyadh 11451, Saudi Arabia; [email protected] (N.A.-S.); [email protected] (Y.H.D.); [email protected] (M.U.T.); [email protected] (M.M.) 5 Department of Agronomy, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt 6 Faculty of Agriculture, Kafrelsheikh University, Kafr El-Sheikh 33516, Egypt 7 Agricultural Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Minufiya 32897, Egypt; [email protected] * Correspondence: [email protected]; Tel.: +966-535318364 Abstract: Sustainable agriculture in arid regions necessitates that the quality of groundwater be care- Citation: Khadr, M.; Gad, M.; fully monitored; otherwise, low-quality irrigation water may cause soil degradation and negatively El-Hendawy, S.; Al-Suhaibani, N.; impact crop productivity. This study aimed to evaluate the quality of groundwater samples collected Dewir, Y.H.; Tahir, M.U.; Mubushar, from the wells in the quaternary aquifer, which are located in the Western Desert (WD) and the M.; Elsayed, S. The Integration of Central Nile Delta (CND), by integrating a multivariate analysis, proximal remote sensing data, and Multivariate Statistical Approaches, data-driven modeling (adaptive neuro-fuzzy inference system (ANFIS) and support vector machine Hyperspectral Reflectance, and regression (SVMR)). Data on the physiochemical parameters were subjected to multivariate analysis Data-Driven Modeling for Assessing to ease the interpretation of groundwater quality. Then, six irrigation water quality indices (IWQIs) the Quality and Suitability of were calculated, and the original spectral reflectance (OSR) of groundwater samples were collected in Groundwater for Irrigation. Water the 302–1148 nm range, with the optimal spectral wavelength intervals corresponding to each of the 2021, 13, 35. https://dx.doi.org/ six IWQIs determined through correlation coefficients (r). Finally, the performance of both the ANFIS 10.3390/w13010035 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 Received: 12 November 2020 Accepted: 23 December 2020 and principal component analysis was found to be advantageous to examining and interpreting the Published: 27 December 2020 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 Publisher’s Note: MDPI stays neu- factors or components of both analyses. The analysis of the six IWQIs revealed that the majority of tral with regard to jurisdictional claims groundwater samples from the CND were highly suitable for irrigation purposes, whereas most of in published maps and institutional the groundwater from the WD can be used with some limitations to avoid salinity and alkalinity affiliations. 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 2 2 Copyright: © 2020 by the authors. Li- IWQIs, but the ANFIS model (R = 0.74–1.0) was superior to the SVMR (R = 0.01–0.88) in both the censee MDPI, Basel, Switzerland. This training and testing series. Finally, the multivariate analysis was able to easily interpret groundwater article is an open access article distributed quality and ground-based remote sensing on the basis of spectral reflectance bands via the ANFIS under the terms and conditions of the model, which could be used as a fast and low-cost onsite tool to estimate the IWQIs of groundwater. Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Water 2021, 13, 35. https://dx.doi.org/10.3390/w13010035 https://www.mdpi.com/journal/water Water 2021, 13, 35 2 of 24 Keywords: 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 1. Introduction Groundwater is regarded as one of the most essential irrigation resources, especially in countries that face extreme water scarcity. However, various agricultural and other human activities in recent decades have resulted in the severe pollution of groundwater, making it unsuitable for irrigation purposes under ordinary conditions. Therefore, water quality, which is fundamental to groundwater management and irrigation water, should be regularly evaluated and monitored. The viability of any water resource for irrigation purposes is evaluated through several irrigation water quality indices (IWQIs) [1]. These indices, which are based on several physiochemical variables of water, include a water quality index (WQI), residual sodium bicarbonate (RSBC), total hardness (TH), potential salinity (PS), residual sodium carbonate (RSC), magnesium hazard (MH), and others [2–6]. The main objective of each index is to determine groundwater’s suitability for either drinking or irrigation purposes. For example, the primary objective of the WQI is to evaluate the overall water quality levels by transforming a wide range of selected physiochemical water variables into a single numerical value using a simple mathematical function [4,7,8]. Again, this index can be considered a simplified model of the complex reality of water quality. The main objective of MH is to represent the excess of Mg2+ in irrigation water. A high MH value in irrigation water may lead to soil alkalinity, as well as reduce the infiltration capacity of soil if a large amount of water is adsorbed between magnesium and clay particles [5,9]. The primary 2− − goal of RSC is to measure the relationship between the sum of CO3 and HCO3 and that of Ca2+ and Mg2+ [10]. A high-RSC value in irrigation water makes the soil infertile because of the deposition of sodium carbonate [10–12]. PS is a characterization of the − 2− dominant Cl and SO4 in irrigation water [13]. Since salts with low solubility precipitate in the soil and accumulate with each successive round of irrigation, this leads to issues of soil salinity. Thus, a high PS value would foster soil permeability problems and thus inhibit the water absorption by crops [4]. Therefore, various important dissolved ions in + + 2+ 2+ − 2− 2− − groundwater such as Na ,K , Ca , Mg , Cl , SO4 , CO3 , and HCO3 represent a common factor in determining various IWQIs. This is because the productivity of field crops as well as the different physiochemical properties of soil are mainly dependent on the concentrations of these ions in irrigation water. If the concentration of some particular ions exceeds a specific threshold, the water cannot be suitable for irrigation purposes. For example, excessive concentrations of Na+ in irrigation water reduce soil permeability as well as hinder the uptake of essential ions (Ca2+, Mg2+, and K+). Excessive concentrations 2+ − of Mg can lead to soil alkalinity. Excessive concentrations of HCO3 negatively affect the plant uptake and metabolism of nutrients. Finally, many of these negative impacts of excessive particular ions limit the growth and productivity of most field crops [4,5,9] The evaluation of groundwater quality through the analysis of a large number of physiochemical parameters produces complex data matrices, which make the understand- ing and interpretation of the intricacies of water quality difficult. Recently, multivariate statistical methods such as factor analysis (FA) and principal component analysis (PCA) have been used to aid in the improved interpretation of large and complex data matrices relating to water quality without losing necessary information collected from actual obser- vations [14–17]. Both types of analysis, incorporating several physiochemical parameters, are applied for extracting new important components or factors that account for most of the variations in groundwater quality [16,18–20]. Using both analytical methods, each new factor produces its own effect on groundwater quality within areas under investigation. In summary, FA can determine the key parameters that explain the original data to the Water 2021, 13, 35 3 of 24 greatest extent, whereas PCA can provide a comprehensive picture of the interrelation- ships between all parameters and confirm their relative degrees of influence. Thus, in the present study, FA was used together with PCA