Assessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural Networks

Salah Elsayed,Hekmat Ibrahim,Adel H Elmetwalli,Farahat S Moghanm,Rahul Datta,Subhan Danish,Adel M Ghoneim,Osama Elsherbiny,Mohamed Gad,Hend Hussein

doi:10.3390/w13213094

Abstract

Monitoring and managing water quality parameters (WQPs) in water bodies (e.g., lakes) on a large scale using sampling-point techniques is tedious, laborious, and not highly representative. Hyperspectral and data-driven technology have provided a potentially valuable tool for the precise measurement of WQPs. Therefore, the objective of this work was to integrate WQPs, derived spectral reflectance indices (published spectral reflectance indices (PSRIs)), newly two-band spectral reflectance indices (NSRIs-2b) and newly three-band spectral indices (NSRIs-3b), and artificial neural networks (ANNs) for estimating WQPs in Lake Qaroun. Shipboard cruises were conducted to collect surface water samples at 16 different sites throughout Lake Qaroun throughout a two-year study (2018 and 2019). Different WQPs, such as total nitrogen (TN), ammonium (NH4+), orthophosphate (PO43−), and chemical oxygen demand (COD), were evaluated for aquatic use. The results showed that the highest determination coefficients were recorded with the NSRIs-3b, followed by the NSRIs-2b, and then followed by the PSRIs, which produced lower R2 with all tested WQPs. The majority of NSRIs-3bs demonstrated strong significant relationships with three WQPs (TN, NH4+, and PO43−) with (R2 = 0.70 to 0.77), and a moderate relationship with COD (R2 = 0.52 to 0.64). The SRIs integrated with ANNs would be an efficient tool for estimating the investigated four WQPs in both calibration and validation datasets with acceptable accuracy. For examples, the five features of the SRIs involved in this model are of great significance for predicting TN. Its outputs showed high R2 values of 0.92 and 0.84 for calibration and validation, respectively. The ANN-PO43−VI-17 was the highest accuracy model for predicting PO43− with R2 = 0.98 and 0.89 for calibration and validation, respectively. In conclusion, this research study demonstrated that NSRIs-3b, alongside a combined approach of ANNs models and SRIs, would be an effective tool for assessing WQPs of Lake Qaroun.

Highlights

The quality of surface water is a critical worldwide environmental concern, since it is essential for long-term economic progress and environmental protection
This research study examined the efficiency of ground-based remote sensing based spectral indices for the retrieval of different water quality parameters (WQPs), including total nitrogen (TN), ammonium (NH4+), orthophosphate (PO43−), and chemical oxygen demand (COD), using hyperspectral data collected from multi-temporal cruises over a two-year campaign (2018 and 2019)
Different PSRIs, NSRIs-2b and NSRIs-3b, as well as artificial neural networks (ANNs) were used for the quantification of some WQPs of Lake Qaroun

Summary

Introduction

The quality of surface water is a critical worldwide environmental concern, since it is essential for long-term economic progress and environmental protection. WQPs are essentially concerned with carefully constructed water quality metrics that are difficult to effectively interpret, since the condition of water quality status is dependent on several physiochemical properties [2,3]. According to the US Environmental Protection Agency’s (USEPA) [8] most current national water quality surveys, more than a third of our lakes, as well as nearly half of our rivers and streams, are polluted. According to the United Nations (UNEP) [9], over 80% of the world’s wastewater is dumped into the environment without being cleaned or repurposed. Most cities in developing countries generate 30–70 mm of wastewater per person every year [10]

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