The Integration of Multivariate Statistical Approaches, Hyperspectral Reflectance, and Data-Driven Modeling for Assessing the Quality and Suitability of Groundwater for Irrigation

Mosaad Khadr,Nasser Al-Suhaibani,Muhammad Mubushar,Salah El-Hendawy,Muhammad Usman Tahir,Yaser Hassan Dewir,Salah Elsayed,Mohamed Gad

doi:10.3390/w13010035

Abstract

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 (R2 = 0.74–1.0) was superior to the SVMR (R2 = 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.

Highlights

Groundwater is regarded as one of the most essential irrigation resources, especially in countries that face extreme water scarcity
The highest correlation between the six irrigation water quality indices (IWQIs) parameters and the original spectral reflectance was located at wavelength intervals of 302–318, 358–900, and 1074–1148 nm, where the correlation coefficients between these wavelength regions and the water quality index (WQI), residual sodium carbonate (RSC), residual sodium bicarbonate (RSBC), potential salinity (PS), total hardness (TH), and MR were higher than 0.70, 0.50, 0.35, 0.49, 0.52, and 0.57, respectively (Figure 7)
The results from the studied articles fully confirm this statement and have indicated a higher and consistent association the six IWQI parameters and the original spectral reflectance was located at wavelength intervals of 302–318, 358–900, and 1074–1148 nm, where the correlation coefficients between these wavelength regions and the WQI, RSC, RSBC, PS, TH, and MR were higher than 0.70, 0.50, 0.35, 0.49, 0.52, and 0.57, respectively (Figure 7)

Summary

Introduction

Groundwater is regarded as one of the most essential irrigation resources, especially in countries that face extreme water scarcity. 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]. This index can be considered a simplified model of the complex reality of water quality. Excessive concentrations of Na+ in irrigation water reduce soil permeability as well as hinder the uptake of essential ions (Ca2+ , Mg2+ , and K+ ). Many of these negative impacts of excessive particular ions limit the growth and productivity of most field crops [4,5,9]

Objectives

Methods

Results

Conclusion