Urban River Dissolved Oxygen Prediction Model Using Machine Learning

Moon, Juhwan; Lee, Jae Joon; Lee, Sang‐Won; Yun, Hong Sik

doi:10.3390/w14121899

Cited by 11 publications

(2 citation statements)

References 35 publications

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“…R2 is the measurement of the independent variables’ abilities to interpret dependent variables. When the R2 value is close to 1, the explanatory abilities of independent variables are at higher levels [ 63 , 64 , 65 ]. Among all models, the most explanatory is LightGBM, followed by XGBoost and CatBoost, and finally DT and RF.…”

Section: Numerical Resultsmentioning

confidence: 99%

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

Lai

Lin

et al. 2023

Micromachines

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The transmission characteristics of the printed circuit board (PCB) ensure signal integrity and support the entire circuit system, with impedance matching being critical in the design of high-speed PCB circuits. Because the factors affecting impedance are closely related to the PCB production process, circuit designers and manufacturers must work together to adjust the target impedance to maintain signal integrity. Five machine learning models, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and light gradient boosting machine (LightGBM), were used to forecast target impedance values. Furthermore, the Optuna algorithm is used to determine forecasting model hyperparameters. This study applied tree-based machine learning techniques with Optuna to predict impedance. The results revealed that five tree-based machine learning models with Optuna can generate satisfying forecasting accuracy in terms of three measurements, including mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2). Meanwhile, the LightGBM model with Optuna outperformed the other models. In addition, by using Optuna to tune the parameters of machine learning models, the accuracy of impedance matching can be increased. Thus, the results of this study suggest that the tree-based machine learning techniques with Optuna are a viable and promising alternative for predicting impedance values for circuit analysis.

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Section: Numerical Resultsmentioning

confidence: 99%

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

Lai

Lin

et al. 2023

Micromachines

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“…A plethora of studies have shown that different ML and hybrid ML models can forecast DO levels in distinct locations [28][29][30]. In the context of urban rivers, the ML paradigm also proved to be a valuable asset in DO estimation [6,[31][32][33].…”

Section: Introductionmentioning

confidence: 99%

New Graph-Based and Transformer Deep Learning Models for River Dissolved Oxygen Forecasting

Costa Rocha,

Oliveira Santos,

Van Griensven Thé

et al. 2023

Environments

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Dissolved oxygen (DO) is a key indicator of water quality and the health of an aquatic ecosystem. Aspiring to reach a more accurate forecasting approach for DO levels of natural streams, the present work proposes new graph-based and transformer-based deep learning models. The models were trained and validated using a network of real-time hydrometric and water quality monitoring stations for the Credit River Watershed, Ontario, Canada, and the results were compared with both benchmarking and state-of-the-art approaches. The proposed new Graph Neural Network Sample and Aggregate (GNN-SAGE) model was the best-performing approach, reaching coefficient of determination (R2) and root mean squared error (RMSE) values of 97% and 0.34 mg/L, respectively, when compared with benchmarking models. The findings from the Shapley additive explanations (SHAP) indicated that the GNN-SAGE benefited from spatiotemporal information from the surrounding stations, improving the model’s results. Furthermore, temperature has been found to be a major input attribute for determining future DO levels. The results established that the proposed GNN-SAGE model outperforms the accuracy of existing models for DO forecasting, with great potential for real-time water quality management in urban watersheds.

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Exploring the Potential of the Machine Learning Techniques in the Water Quality Assessment: A Review of Applications and Performance

Márquez,

Al-taie,

Zakur

et al. 2024

Lecture Notes in Networks and Systems

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Urban River Dissolved Oxygen Prediction Model Using Machine Learning

Cited by 11 publications

References 35 publications

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

New Graph-Based and Transformer Deep Learning Models for River Dissolved Oxygen Forecasting

Exploring the Potential of the Machine Learning Techniques in the Water Quality Assessment: A Review of Applications and Performance

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