Pollution from many different sources severely affects the quality of our water supply. Over the past few years, a large number of online water quality monitoring stations have been used to gather time series data on water quality monitoring. These numbers are the foundation for deep learning techniques for forecasting water quality. In particular, typical deep learning approaches struggle to accurately estimate water quality in the presence of net promoter system (NPS) contamination. To overcome this shortcoming, a new deep learning model called long short-term memory (LSTM)–gray wolf optimization (GWO)–fish swarm optimization (FSO) was developed to enhance the precision of water quality prediction with NPS pollution. The well-established ...
Predicting trends in water quality plays an essential role in the field of environmental modeling. T...
Dissolved oxygen (DO) concentration in water is one of the key parameters for assessing river water ...
In smart mariculture, traditional methods are not only difficult to adapt to the complex, dynamic an...
In line with rapid economic development and accelerated urbanization, the increasing discharge of wa...
During the last years, water quality has been threatened by various pollutants. Therefore, modeling ...
This research paper focuses on a water quality prediction model which requires high-quality data. In...
Water quality status in terms of one crucial parameter such as dissolved oxygen (D.O.) has been an i...
This study aimed to investigate the applicability of deep learning algorithms to (monthly) surface w...
Water quality assessment is critical for environmental sustainability and public health. This resear...
Accurate forecast of water quality parameters is important for water quality monitoring and water qu...
Water quality assessment is critical for environmental sustainability and public health. This resear...
Water quality prediction is aided by environmental monitoring, ecological sustainability, and aquacu...
Providing an accurate prediction of water quality parameters for improved water quality management i...
The rapid development of urban industrialization has had many negative effects on the quality of wat...
Clean water is an indispensable essential resource on which humans and other living beings depend. T...
Predicting trends in water quality plays an essential role in the field of environmental modeling. T...
Dissolved oxygen (DO) concentration in water is one of the key parameters for assessing river water ...
In smart mariculture, traditional methods are not only difficult to adapt to the complex, dynamic an...
In line with rapid economic development and accelerated urbanization, the increasing discharge of wa...
During the last years, water quality has been threatened by various pollutants. Therefore, modeling ...
This research paper focuses on a water quality prediction model which requires high-quality data. In...
Water quality status in terms of one crucial parameter such as dissolved oxygen (D.O.) has been an i...
This study aimed to investigate the applicability of deep learning algorithms to (monthly) surface w...
Water quality assessment is critical for environmental sustainability and public health. This resear...
Accurate forecast of water quality parameters is important for water quality monitoring and water qu...
Water quality assessment is critical for environmental sustainability and public health. This resear...
Water quality prediction is aided by environmental monitoring, ecological sustainability, and aquacu...
Providing an accurate prediction of water quality parameters for improved water quality management i...
The rapid development of urban industrialization has had many negative effects on the quality of wat...
Clean water is an indispensable essential resource on which humans and other living beings depend. T...
Predicting trends in water quality plays an essential role in the field of environmental modeling. T...
Dissolved oxygen (DO) concentration in water is one of the key parameters for assessing river water ...
In smart mariculture, traditional methods are not only difficult to adapt to the complex, dynamic an...