Water Classification Using Convolutional Neural Network

Asghar, Saira; Gilanie, Ghulam; Saddique, Mubbashar; Ullah, Hafeez; Mohamed, Heba G.; Abbasi, Irshad Ahmed; Abbas, Mohamed

doi:10.1109/access.2023.3298061

Cited by 4 publications

(2 citation statements)

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“…Deep Neural Networks (DNN) represent a deep learning model comprising multiple layers of interconnected neural networks that utilize weighted connections to process and transmit information [34,54]. The Rectified Linear Unit (ReLU) function is chosen as the activation function, introducing nonlinearity to the model.…”

Section: Deep Neural Network (Dnn)mentioning

confidence: 99%

“…However, during the training process, it was observed that increasing the number of hidden layers did not lead to a significant increase in the coefficient of determination, R 2 . This could be because deep learning algorithms typically require a substantial amount of training data, and the limited dataset in this study restricted the model's fitting performance to some extent [54,72].…”

Section: Model Performance Analysismentioning

confidence: 99%

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Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms

Chen,

Yao,

Zhu

et al. 2024

Water

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Remote sensing technology applications for water quality inversion in large rivers are common. However, their application to medium/small-sized water bodies within rural areas is limited due to the low spatial resolution of remote sensing images. In this work, a typical small rural river was selected, and high-resolution unmanned aerial vehicle (UAV) multispectral images and ground monitoring data of the river were obtained. Then, a comparative analysis of three univariate regression models and nine machine learning models (Ridge Regression (RR), Support Vector Regression (SVR), Grid Search Support Vector Regression (GS-SVR), Random Forest (RF), Grid Search Random Forest (GS-RF), eXtreme Gradient Boosting (XGBoost), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Catboost Regression (CBR)) for their accuracy in the prediction of turbidity (TUB), total nitrogen (TN), and total phosphorus (TP) was performed. TUB can be achieved by simple statistical regression models. The CBR model exhibited the best performance for the three index inversions on the test set evaluation metrics: R2 (0.90~0.92), RMSE (7.57 × 10−3~1.59 mg/L), MAE (0.01~1.30 mg/L), RPD (3.21~3.56), and NSE (0.84~0.92). The water pollution of the study area was closely related to its land-use pattern, excessive and irrational fertilizer application, and distribution of pollutant outlets.

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Section: Deep Neural Network (Dnn)mentioning

confidence: 99%

Section: Model Performance Analysismentioning

confidence: 99%