ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Big Data, AI, and the Environment
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1612658
AI-Powered Approaches for Enhancing Remote Sensing-Based Water Contamination Detection in Ecological Systems
Provisionally accepted
- Zhongyuan University of Science and Technology, Zhengzhou, China
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Water contamination poses a significant threat to both public health and ecosystems worldwide, leading to increased emphasis on developing robust detection and mitigation strategies. Traditional methods for monitoring water quality, such as manual sampling and basic chemical analysis, are limited in their ability to provide real-time data and often fail to detect contaminants in a timely manner. Recent advancements in artificial intelligence (AI) offer promising solutions to enhance water contamination detection, particularly by leveraging machine learning algorithms and sensor networks for continuous monitoring. This paper presents a novel AI-powered approach for improving water contamination detection, which incorporates real-time data processing and predictive modeling to identify contamination events and optimize response strategies.We combine sensor data with advanced machine learning techniques to accurately predict contaminant concentrations and assess the effectiveness of various mitigation strategies in different water bodies. Experimental results across four benchmark datasets show that our model, AquaDynNet, achieves outstanding performance. Specifically, it achieves an accuracy of 90.75%, F1-score of 88.79, and AUC of 92.02 on the Terra Satellite dataset. On the Aquatic Toxicity dataset, the model obtains an accuracy of 92.58% and AUC of 94.13, and on the Water Quality dataset, it reaches an F1-score of 85.54 and AUC of 89.72. On the infrastructure-focused WaterNet dataset, it achieves 91.98% accuracy and AUC of 92.47. These results consistently demonstrate our model's superior detection accuracy and robustness compared to baseline approaches.Furthermore, our approach is capable of providing actionable insights for policymakers and environmental agencies to mitigate the impacts of contamination on human health and aquatic ecosystems. This research addresses critical challenges in water quality management, offering a scalable and adaptable solution for addressing global water contamination issues.
Keywords: AI-Powered Detection, Water contamination, machine learning, Real-time monitoring, Ecological health
Received: 16 Apr 2025; Accepted: 22 Jul 2025.
Copyright: © 2025 Lin, Hei, Wang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xinhao Lin, Zhongyuan University of Science and Technology, Zhengzhou, China
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