Artificial intelligence based detection and control strategies for river water pollution: A comprehensive review

Area of Science:

• Environmental Science
• Computer Science
• Data Science

Background:

• River water quality is crucial for ecosystem health and human safety.
• Anthropogenic activities significantly contaminate river systems globally.
• Managing complex, non-linear water quality data presents substantial challenges.

Purpose of the Study:

• To provide a comprehensive review of Artificial Intelligence (AI) applications in river water quality (WQ) prediction and control.
• To analyze advancements in Machine Learning (ML) and Deep Learning (DL) models for WQ simulation from 2019-2024.
• To explore the integration of Internet of Things (IoT) with AI for enhanced WQ monitoring and management.

Main Methods:

• Systematic review and analysis of over 110 research articles on river WQ modeling (2019-2024).
• Highlighting key AI techniques including Artificial Neural Networks (ANN), Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Random Forest (RF).
• Investigating the synergistic role of Internet of Things (IoT) in real-time data collection and AI-driven WQ analysis.

Main Results:

• AI, ML, and DL models demonstrate significant potential in simulating and predicting river WQ.
• IoT integration enhances predictive accuracy and enables continuous, real-time WQ monitoring.
• Specific ML/DL algorithms like LSTM and RF show promise for complex WQ datasets.

Conclusions:

• Advanced AI, ML, and DL techniques are vital for addressing challenges in river water pollution control.
• The combination of AI and IoT offers powerful tools for sustainable water resource management.
• Future research should focus on leveraging these innovations to protect aquatic ecosystems and ensure water safety.