Long-Term Monitoring of Inland Water Quality Parameters Using Landsat Time-Series and Back-Propagated ANN: Assessment and Usability in a Real-Case Scenario

Abstract

Water scarcity and quality deterioration, driven by rapid population growth, urbanization, and intensive industrial and agricultural activities, emphasize the urgency for effective water management. This study aims to develop a model to comprehensively monitor various water quality parameters (WQP) and evaluate the feasibility of implementing this model in real-world scenarios, addressing the limitations of conventional in-situ sampling. Thus, a comprehensive model for monitoring WQP was developed using a 38-year dataset of Landsat imagery and in-situ data from the Water Information System of Europe (WISE), employing Back-Propagated Artificial Neural Networks (ANN). Correlation analyses revealed strong associations between remote sensing data and various WQPs, including Total Suspended Solids (TSS), chlorophyll-a (chl-a), Dissolved Oxygen (DO), Total Nitrogen (TN), and Total Phosphorus (TP). Optimal band combinations for each parameter were identified, enhancing the accuracy of the WQP estimation. The ANN-based model exhibited very high accuracy, particularly for chl-a and TSS (R2 > 0.90, NRMSE < 0.79%), surpassing previous studies. The independent validation showcased accurate classification for TSS and TN, while DO estimation faced challenges during high variation periods, highlighting the complexity of DO dynamics. The usability of the developed model was successfully tested in a real-case scenario, proving to be an operational tool for water management. Future research avenues include exploring additional data sources for improved model accuracy, potentially enhancing predictions and expanding the model’s utility in diverse environmental contexts.

Spatio-Temporal Dynamics Coupling between Land Use/Cover Change and Water Quality in Dongjiang Lake Watershed Using Satellite Remote Sensing

“…Remote sensing satellites offer cost-effective and wide-coverage-standardized monitoring data with a long time series [47]. However, the use of remote sensing data to explore the coupling relationship between water quality and LUCC is plagued [48] by problems such as the difficulty of capturing small-scale features and processes, time-consuming data processing, large errors in the estimation of water quality parameters, and poor compatibility between remote sensing and ground data [49]. This study analyzed measured data in the Dongjiang Lake Watershed to identify key water quality parameters, including DO, COD Mn , COD, NH 3 -N, and TP [26].…”

Smart Aquaponics: An Automated Water Quality Management System for Sustainable Urban Agriculture

“…The mention of obtaining optimum values for each controller (vegetable tank, fish tank, and heater system) indicates a tailored approach to optimization. Earlier systems [19][20][21][22][23][24][25][26] may have employed generic control settings or overlooked the significance of fine-tuning control parameters for specific components within the aquaponic system. By optimizing the parameters for each subsystem, the proposed system is likely to achieve a superior overall performance and efficiency.…”

“…Research for managing water quality in treatment plants has intensified due to its substantial potential to enhance operational efficiency, ensure regulatory compliance, and improve overall water treatment effectiveness [16][17][18]. Simultaneously, research efforts [19][20][21][22][23][24][25][26][27][28][29][30] have concentrated on refining water management control. In the realm of aquaponics, where water plays a pivotal role, the research paper [29] offers a succinct overview of the proposed solutions in the literature.…”