Water Quality Prediction Model Combining Sparse Auto-encoder and LSTM Network

Li, Zhenbo; Peng, Fang-Le; Niu, Bingshan; Li, Guangyao; Wu, Jian; Miao, Zheng

doi:10.1016/j.ifacol.2018.08.091

Cited by 78 publications

(40 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The steep increase in error gap between the short-term and long-term DO content forecasting performance of our proposed hybrid model indicates that the nearer the forecast future the higher the forecast accuracy and vice versa. Table 2, the performance of the proposed hybrid EEMD-based LSTM NN is compared with another related hybrid water quality prediction model based on Sparse Auto-encoder (SAE) and LSTM NN, SAE-BPNN, single LSTM and BPNN developed by Li et al [28]. The tabulated error statistics indicate that our proposed hybrid model outperform the other models listed in Table II in terms of the error margin of the forecasted data.…”

Section: Discussionmentioning

confidence: 98%

“…Consequently, the proposed hybrid multi-scale forecasting model can get more features through the decomposition process for the forecasted signals, which further results in improved forecasting accuracy. Amongst the models proposed in [28], the hybrid SAE-LSTM model demonstrated the least error in terms of prediction accuracy. However, the tabulated error statistics in Table II indicate that our Hybrid EEMD-based LSTM outperforms the SAE-LSTM model due to the potentials of the applied EEMD method.…”

Section: Discussionmentioning

confidence: 99%

“…From the analysis of these related literatures, it can be seen that novel hybrid forecasting models that are based on multi-scale features of the original datasets are not only effective and reliable, but also suitable for water quality data forecasting in the field of aquaculture. Hence, our study seeks to develop a novel accurate hybrid forecasting model for DO content prediction in aquaculture environment by combining the potentials of EEMD method with LSTM [28] neural network.…”

Section: Related Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

Eze¹,

Ajmal²

2020

Preprint

View full text Add to dashboard Cite

Dissolved Oxygen (DO) concentration is a vital parameter that indicates water quality. DO short term forecasting using time series analysis on data collected from an aquaculture pond is presented here. This can provide data support for an early warning system for an improved management of the aquaculture farm. The conventional forecasting approaches are commonly characterized by low accuracy and poor generalization problems. In this article, we present a novel hybrid DO concentration forecasting method with ensemble empirical mode decomposition (EEMD) based LSTM (Long short-term memory) neural network (NN). With this method, first, the sensor data integrity is improved through linear interpolation and moving average filtering methods of data preprocessing. Next, the EEMD algorithm is applied to decompose the original sensor data into multiple intrinsic mode functions (IMFs). Finally, the feature selection is used to carefully select IMFs that are strongly correlated with the original sensor data and integrate both into inputs for the NN. The hybrid EEMD-based LSTM forecasting model is then constructed. Performance of this proposed model in training and validation sets was compared with the observed real sensor data. To obtain the exact evaluation accuracy of the forecasted results of the hybrid EEMD-based LSTM forecasting model, three statistical performance indices were adopted: MAE, MSE, and RMSE. Results presented for short term (12-hour period) and long term (1-month period) give a strong indication of suitability of this method for forecasting DO values.

show abstract

Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Related Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

Eze¹,

Ajmal²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, observation of higher frequency data and proper pretreatment of the massive data (e.g., interpolation) to minimize internal error or missing points of data are essential for improving the practicability of the advanced data analysis technologies. Recently, real-time data measured with in situ sensors is increasingly used for the analyses and prediction of water quality; however, the information on the sensor types, measuring parameters, and their frequency of the measurement has not been clearly reported [17,135,142]. Normally, high-frequency real-time data includes errors (e.g., missing data or abnormal values) and thus require a proper pretreatment (e.g., consideration of missing data or removal of data errors) to obtain a reliable model performance [142,151].…”

Section: Future Of Ict Research For Water Quality Monitoringmentioning

confidence: 99%

“…The general function of real-time online water quality monitoring systems is to conduct data acquisition, transmission, and interpretation of the measured data [2]. The advanced data analysis technologies, such as machine learning, also provide valuable tools for effective water quality data management (e.g., prediction of water quality changes) [16][17][18]. Recently, deep-learning, a type of machine learning method, is increasingly used for the analysis of massive environmental data, to provide useful information for water quality management.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Information and Communications Technology (ICT) and Sensor Technology for Monitoring Water Quality

Park

Kim

Lee

2020

Water

View full text Add to dashboard Cite

Water quality control and management in water resources are important for providing clean and safe water to the public. Due to their large area, collection, analysis, and management of a large amount of water quality data are essential. Water quality data are collected mainly by manual field sampling, and recently real-time sensor monitoring has been increasingly applied for efficient data collection. However, real-time sensor monitoring still relies on only a few parameters, such as water level, velocity, temperature, conductivity, dissolved oxygen (DO), and pH. Although advanced sensing technologies, such as hyperspectral images (HSI), have been used for the areal monitoring of algal bloom, other water quality sensors for organic compounds, phosphorus (P), and nitrogen (N) still need to be further developed and improved for field applications. The utilization of information and communications technology (ICT) with sensor technology shows great potential for the monitoring, transmission, and management of field water-quality data and thus for developing effective water quality management. This paper presents a review of the recent advances in ICT and field applicable sensor technology for monitoring water quality, mainly focusing on water resources, such as rivers and lakes, and discusses the challenges and future directions.

show abstract