Harnessing AI for Water Quality Prediction and Assessment

By Dr. Sampath LonkaReviewed by Susha Cheriyedath, M.Sc.Sep 21 2023

In a recent publication in the journal Water, researchers explored artificial intelligence (AI) techniques to predict the water quality index (WQI) and water quality classification (WQC). Further, advanced models for pH and alkalinity assessment are explored.

Background

Water quality monitoring involves sample collection, analysis methods, and sensors, with traditional methods being laborious and costly. Monitoring systems support healthy ecosystems, address chemical pollutants and ecological impacts, and raise public awareness. Parameters vary based on water use, including irrigation and industrial purposes. Water quality parameters fall into chemical, physical, and biological categories.

Environmental monitoring compares results with established criteria. Water quality sensors play a vital role in various management decisions, including compliance with regulatory standards, hydrant flushing schedules, water quality modeling, and contamination warning systems. Water quality modeling involves complex mathematical representations of pollutant fate, transport, and degradation in natural water systems. These models vary based on water body types and the parameters involved, such as metals, dissolved oxygen, nutrients, etc. Model selection depends on factors such as applicability, cost, and accessibility to software source code.

AI in water quality monitoring and modeling

The analysis and prediction of water quality models have become increasingly complex due to the vast amount of data collected from various water bodies. AI facilitates the examination of extensive datasets, real-time monitoring, and the prediction of water quality trends. Machine learning models offer several advantages, including accuracy, efficiency, low cost, and the ability to automate data collection and analysis. They excel at detecting subtle shifts in water quality, enabling swift responses to pollution incidents.

However, implementing AI in water quality monitoring and modeling has limitations. Reliance on accurate and representative data is paramount, as erroneous or biased input data can compromise the reliability of AI-generated predictions. Some AI methods require substantial historical datasets for effective training, which may not always be available. Complex AI models, like deep neural networks, can be challenging to interpret and may lack contextual understanding. Furthermore, the potential for biases in training data and the need for specialized expertise in both water quality management and AI technologies pose additional hurdles. Overfitting and sensitivity to external variables are also concerns.

Advancements in AI for water quality prediction and assessment

Machine learning, as a robust data analysis method, finds widespread application in discerning patterns and making predictions from extensive datasets across various domains. In a recent review article delving into machine learning's role in assessing water quality, its utility becomes evident in predicting water quality, optimizing resource allocation, and addressing resource scarcity.

Neural network models, including the long short-term memory (LSTM) algorithm and the nonlinear autoregressive neural network (NARNET), are used for predicting the Water Quality Index (WQI). Machine learning models such as support vector machines (SVM), naive Bayes, and K-nearest neighbor (K-NN) outperformed for water quality forecasting. For predicting and categorizing WQI, different water quality parameters are included. With the chosen parameters, the FFNN outperformed the KNN in water quality classification.

Researchers employed several machine learning models for arsenic concentration estimation. The models Random Forest (RF) and Decision Tree (DT) excel in predictive performance. To estimate nitrate concentrations in groundwater, similar machine-learning techniques are employed. For estimating calcium levels in the Tireh River, Iran, AI methods such as ANN, group method of data handling (GMDH), and support vector machine (SVM) prove invaluable.

A novel Android smartphone app for estimating water quality parameters emerges, employing artificial neural network models trained on image features. The app, known as the Water App, demonstrates promising performance. Groundwater quality, like surface water, is a concern, with human activities often leading to contamination. Groundwater vulnerability assessment becomes crucial, considering anthropogenic and natural factors.

Lastly, fluoride contamination in groundwater is addressed through machine learning algorithms, with extreme learning machines (ELM) outperforming other models. With the RBF kernel function, SVM emerges as the top performer, highlighting the significance of appropriate algorithm selection.

Future directions and conclusion

AI offers promise in water quality management through data integration, model generalization, and increased interpretability. Collaboration between AI and water quality experts enhances modeling precision while quantifying uncertainty is vital for trust in AI-generated forecasts. Flexible AI methods that adapt in real-time improve monitoring in dynamic environments. While combining traditional models with AI enhances prediction accuracy, addressing bias and ensuring equitable AI-driven solutions is essential. AI can optimize sensor networks, reduce monitoring costs, and identify long-term trends like climate change and urbanization for informed decision-making.

In conclusion, AI can transform water quality monitoring and modeling, but its successful implementation requires careful consideration of data quality, transparency, bias mitigation, and domain expertise.