Application of deep learning and machine learning methods in water quality modeling and prediction: a review

Ewuzie, Ugochukwu; Bolade, Oladotun P.; Egbedina, Abisola Opeyemi

doi:10.1016/b978-0-323-85597-6.00020-3

Cited by 15 publications

(8 citation statements)

References 93 publications

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“…The traditional statistical methods although produces good results, they are time consuming and are complicated [76,77]. Therefore, researchers have developed and applied advanced techniques based on artificial intelligence (AI) for the analysis and prediction of WQI [25,26,33]. The main advantage of AI-based techniques is they produces the reliable outcomes in very short time even with the fewer data [33].…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, researchers have developed and applied advanced techniques based on artificial intelligence (AI) for the analysis and prediction of WQI [25,26,33]. The main advantage of AI-based techniques is they produces the reliable outcomes in very short time even with the fewer data [33]. In this study, although all the AI-based DL and ML models produced reliable re- Based on these findings, the policy for water quality management should prioritize reducing TDS and EC levels in the water.…”

Section: Discussionmentioning

confidence: 99%

“…By combining weak and strong ML and DL algorithms, our ensemble stacking model can more accurately learn highly nonlinear data, leading to improved prediction results [32]. Additionally, DL models, while effective at learning noisy and highly nonlinear data, typically require a large amount of data to achieve optimal performance [33,34]. However, we tackle this limitation by implementing hyperparameter optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

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Coupling Machine and Deep Learning with Explainable Artificial Intelligence for Improving Prediction of Groundwater Quality and Decision-Making in Arid Region, Saudi Arabia

Alshehri

Rahman

2023

Water

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Recently, machine learning (ML) and deep learning (DL) models based on artificial intelligence (AI) have emerged as fast and reliable tools for predicting water quality index (WQI) in various regions worldwide. In this study, we propose a novel stacking framework based on DL models for WQI prediction, employing a convolutional neural network (CNN) model. Additionally, we introduce explainable AI (XAI) through XGBoost-based SHAP (SHapley Additive exPlanations) values to gain valuable insights that can enhance decision-making strategies in water management. Our findings demonstrate that the stacking model achieves the highest accuracy in WQI prediction (R2: 0.99, MAPE: 15.99%), outperforming the CNN model (R2: 0.90, MAPE: 58.97%). Although the CNN model shows a relatively high R2 value, other statistical measures indicate that it is actually the worst-performing model among the five tested. This discrepancy may be attributed to the limited training data available for the CNN model. Furthermore, the application of explainable AI (XAI) techniques, specifically XGBoost-based SHAP values, allows us to gain deep insights into the models and extract valuable information for water management purposes. The SHAP values and interaction plot reveal that elevated levels of total dissolved solids (TDS), zinc, and electrical conductivity (EC) are the primary drivers of poor water quality. These parameters exhibit a nonlinear relationship with the water quality index, implying that even minor increases in their concentrations can significantly impact water quality. Overall, this study presents a comprehensive and integrated approach to water management, emphasizing the need for collaborative efforts among all stakeholders to mitigate pollution levels and uphold water quality. By leveraging AI and XAI, our proposed framework not only provides a powerful tool for accurate WQI prediction but also offers deep insights into the models, enabling informed decision-making in water management strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Coupling Machine and Deep Learning with Explainable Artificial Intelligence for Improving Prediction of Groundwater Quality and Decision-Making in Arid Region, Saudi Arabia

Alshehri

Rahman

2023

Water

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“…But it has a special mechanism that can let previous information loop back to the input layer again in order to model sequence data like time series, text, and audio, etc. for prediction [11]. LSTM as an advanced RNN model can learn long-term dependencies and solve vanishing gradients in traditional RNNs by ignoring useless values in the network.…”

Section: Related Workmentioning

confidence: 99%

Traffic flow study on CA I5-N from Santa Ana to Anaheim

2023

Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023)

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In intelligent transportation systems (ITS), accuracy in predicting traffic flow is an important prerequisite for better road planning, calculating estimated arrival times, and reducing traffic congestion. However, in the real world, forecasting realtime traffic flows need to be both accurate and efficient, and traffic data for training is limited in many cases. The accuracy of two mainstream machine learning and deep learning models, random forest and long short-term memory network (LSTM), in predicting small-scale short-term time-series traffic flows is questionable. In this study, the prediction accuracy of the two models was compared by training a total of 5208 data for the whole highway section and 168 data for each small road section separately, but they are both time-series data with one-hour intervals. The results of the study show that when comparing longitudinally, random forest predictions are more accurate for both data, while when comparing horizontally, the random forest has higher accuracy when predicting each road segment's data with smaller amounts.

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“…The accuracy of predictions in water quality parameters has been shown to depend on several factors that include but are not limited to the type of machine and deep learning modeling algorithms used [53][54][55], type of satellites used [56], and atmospheric correction methods applied [57]. However, no one factor has, up to this point, consistently provided optimal results in every situation.…”

Section: Factors Affecting the Accuracy Of Swt Predictionsmentioning

confidence: 99%

Micro-Climate Computed Machine and Deep Learning Models for Prediction of Surface Water Temperature Using Satellite Data in Mundan Water Reservoir

Mukonza

Chiang

2022

Water

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Water temperature is an important indicator of water quality for surface water resources because it impacts solubility of dissolved gases in water, affects metabolic rates of aquatic inhabitants, such as fish and harmful algal blooms (HABs), and determines the fate of water resident biogeochemical nutrients. Furthermore, global warming is causing a widespread rise in temperature levels in water sources on a global scale, threatening clean drinking water supplies. Therefore, it is key to increase the frequency of spatio-monitoring for surface water temperature (SWT). However, there is a lack of comprehensive SWT monitoring datasets because current methods for monitoring SWT are costly, time consuming, and not standardized. The research objective of this study was to estimate SWT using data from the Landsat-8 (L8) and Sentinel-3 (S3) satellites. To do this, we used machine learning techniques, such as Support Vector Regression (SVR), Gaussian Process Regression (GPR), simple neural network (ANN), and deep learning techniques (Long Short Term Memory, LSTM, and Convolutional Long Short Term Memory, 1D ConvLSTM). Using deep and machine learning techniques to regress satellite data to estimate SWT presents a number of challenges, including prediction uncertainty, over- or under-estimation of measured values, and significant variation in the final estimated data. The performance of the L8 ConvLSTM model was superior to all other methods (R2 of 0.93 RMSE of 0.16 °C, and bias of 0.01 °C). The factors that had a significant effect on the model’s accuracy performance were identified and quantified using a two-factor analysis of variance (ANOVA) analysis. The results demonstrate that the main effects and interaction of the type of machine/deep learning (ML/DL) model and the type of satellite have statistically significant effects on the performances of the different models. The test statistics are as follows: (satellite type main effect p *** ≤ 0.05, Ftest = 15.4478), (type of ML/DL main effect p *** ≤ 0.05, Ftest = 17.4607) and (interaction, satellite type × type of ML/DL p ** ≤ 0.05, Ftest = 3.5325), respectively. The models were successfully deployed to enable satellite remote sensing monitoring of SWT for the reservoir, which will help to resolve the limitations of the conventional sampling and laboratory techniques.

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Application of deep learning and machine learning methods in water quality modeling and prediction: a review

Cited by 15 publications

References 93 publications

Coupling Machine and Deep Learning with Explainable Artificial Intelligence for Improving Prediction of Groundwater Quality and Decision-Making in Arid Region, Saudi Arabia

Coupling Machine and Deep Learning with Explainable Artificial Intelligence for Improving Prediction of Groundwater Quality and Decision-Making in Arid Region, Saudi Arabia

Traffic flow study on CA I5-N from Santa Ana to Anaheim

Micro-Climate Computed Machine and Deep Learning Models for Prediction of Surface Water Temperature Using Satellite Data in Mundan Water Reservoir

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