article · Water
Traditional point sampling for monitoring water quality in lakes is labour-intensive and often unrepresentative over large areas. This research investigated the use of ground-based hyperspectral data and artificial neural networks to estimate water quality parameters across Lake Qaroun. Across 16 sites sampled over two years, measurements were taken for total nitrogen, ammonium, orthophosphate, and chemical oxygen demand. These parameters were compared against published spectral reflectance indices alongside newly developed two-band and three-band indices. Newly designed three-band indices outperformed older metrics, demonstrating strong relationships with nitrogen, ammonium, and orthophosphate, as well as moderate correlations with chemical oxygen demand. Furthermore, combining spectral indices with artificial neural network models yielded high estimation accuracy across calibration and validation datasets, notably achieving strong validation performance for total nitrogen and orthophosphate. The findings demonstrate that integrating novel spectral indices with neural networks offers a viable method for lake water assessment.
Monitoring large water bodies through manual collection is slow and resource-heavy. Demonstrating that ground-based optical sensors combined with machine learning can accurately track key nutrients and chemical oxygen demand helps environmental managers detect pollution faster. This provides a more comprehensive way to safeguard lake ecosystems and manage aquatic resources without relying entirely on tedious field-sampling routines.
This approach could enable digital monitoring tools or decision-support software for environmental agencies and water resource managers. By combining spectral indices with machine learning, automated or semi-automated lake surveillance systems become feasible. Based on the abstract, this represents applied research validated against field data from a single lake over two years, positioning the methodology at an early applied testing stage prior to broader commercial development or deployment.
AI-generated from the published abstract. Always read the original work before citing.
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.
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DOI: 10.3390/w13213094
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