Abstract

Eutrophication in fresh water has become a critical challenge worldwide and chlorophyll-a content is a key water quality parameter that indicates the extent of eutrophication and algae concentration in a body of water. In this paper, a forecasting model for the high accuracy prediction of chlorophyll-a content is proposed to enable aquafarm managers to take remediation actions against the occurrence of toxic algal blooms in the aquaculture industry. The proposed model combines the ensemble empirical mode decomposition (EEMD) technique and a deep learning (DL) long short-term memory (LSTM) neural network (NN). With this hybrid approach, the time-series data are firstly decomposed with the aid of the EEMD algorithm into manifold intrinsic mode functions (IMFs). Secondly, a multi-attribute selection process is employed to select the group of IMFs with strong correlations with the measured real chlorophyll-a dataset and integrate them as inputs for the DL LSTM NN. The model is built on water quality sensor data collected from the Loch Duart salmon aquafarm in Scotland. The performance of the proposed novel hybrid predictive model is validated by comparing the results against the dataset. To measure the overall accuracy of the proposed novel hybrid predictive model, the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) were used.

Highlights

  • Eutrophication in freshwater bodies is an organic process usually caused by the increased enrichment of nutrients which can pollute water quality and adversely affect aquatic ecosystems

  • With the proposed novel water quality forecasting model, the measured real water-quality parameter content dataset undergoes decomposition processes into disparate components by applying the ensemble empirical mode decomposition (EEMD) method for the purpose of improving the prediction accuracy of the proposed predictive model

  • This was done through the multi-feature selection process of the EEMD method which allowed for the selection of certain groups of intrinsic mode functions (IMFs) that strongly correlate with the Chelsea’s TriLux multi-parameter fluorometer measured chlorophyll-a data and integrate them into inputs for the deep learning long short-term memory (LSTM)

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Summary

Introduction

Eutrophication in freshwater bodies is an organic process usually caused by the increased enrichment of nutrients which can pollute water quality and adversely affect aquatic ecosystems. Sensory monitoring of the chlorophyll-a concentration is an effective approach for reliably assessing the trophic state of freshwater bodies given its strong affinity to the abundance of phytoplankton, cyanobacteria, and biomass, which affect the turbidity and general colouration of fresh water [8]. Research has shown that deep learning long short-term memory (LSTM) neural networks can overcome the above-mentioned weakness and can provide efficient applicability and reliability for water quality parameter prediction [13,14]. Combining the ensemble empirical mode decomposition (EEMD) method with deep learning LSTM neural network has demonstrated clear advantages over traditional LSTM neural networks in terms of improved water quality parameter prediction accuracy in the aquaculture environment [13]. A novel deep learning-based hybrid chlorophyll-a prediction model for the aquaculture industry is proposed

The Study Area Description and Datasets Analysis
Fluorometers
Chelsea
Sensor L
Proposed Model
Deep Learning LSTM Neural Networks
Proposed Water Quality Prediction Model
Evaluation
Results and Discussions
Hour Prediction
Conclusions
Full Text
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