A water quality prediction method based on the multi-time scale bidirectional long short-term memory network

As an important factor affecting the mangrove wetland ecosystem, water quality has become the focus of attention in recent years. Therefore, many studies have focused on the prediction of water quality to help establish a regulatory framework for the assessment and management of water pollution and ecosystem health. To make a more accurate and comprehensive forecast analysis of water quality, we propose a method for water quality prediction based on the multi-time scale bidirectional LSTM network. In the method, we improve data integrity and data volume through data preprocessing. And the network processes input data forward and backward and considers the dependencies at multiple time scales. Besides, we use the Box–Behnken experimental design method to adjust hyper-parameters in the process of modeling. In this study, we apply this method to the water quality prediction research of Beilun Estuary, and the performance of our proposed model is evaluated and compared with other models. The experiment results show that this model has better performance in water quality prediction than that of using LSTM or bidirectional LSTM alone. Graphical Abstract Schematic of research work

[1]  M. Russell,et al.  Contaminant profiles for surface water, sediment, flora and fauna associated with the mangrove fringe along middle and lower eastern Tampa Bay. , 2015, Marine pollution bulletin.

[2]  Guangyu Ding,et al.  Study on the prediction of stock price based on the associated network model of LSTM , 2019, International Journal of Machine Learning and Cybernetics.

[3]  Seok Soon Park,et al.  Factors affecting algal blooms in a man-made lake and prediction using an artificial neural network , 2014 .

[4]  Tao Jin,et al.  A data-driven model for real-time water quality prediction and early warning by an integration method , 2019, Environmental Science and Pollution Research.

[5]  Lijuan Sun,et al.  Water Quality Prediction Method Based on IGRA and LSTM , 2018, Water.

[6]  Jian Zhou,et al.  Water quality prediction method based on LSTM neural network , 2017, 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE).

[7]  Ashbindu Singh,et al.  Status and distribution of mangrove forests of the world using earth observation satellite data , 2011 .

[8]  Jun Wu,et al.  Application of chaos and fractal models to water quality time series prediction , 2009, Environ. Model. Softw..

[9]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[10]  Juntao Liu,et al.  A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture , 2019, Sensors.

[11]  Qi Li,et al.  A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory. , 2019, The Science of the total environment.

[12]  Jian-Zhong Li,et al.  A Temporal and Spatial Correlation Based Missing Values Imputation Algorithm in Wireless Sensor Networks: A Temporal and Spatial Correlation Based Missing Values Imputation Algorithm in Wireless Sensor Networks , 2010 .

[13]  D. Friess,et al.  Variability in mangrove change estimates and implications for the assessment of ecosystem service provision , 2014 .

[14]  Santosh Kumar Nanda,et al.  A Cascaded Fuzzy Inference System for Indian river water quality prediction , 2011, Adv. Eng. Softw..

[15]  K. Singh,et al.  Dependence of precipitation of trace elements on pH in standard water , 2018 .

[16]  C. L. Philip Chen,et al.  Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting , 2015, IEEE Transactions on Sustainable Energy.

[17]  Yong Shi,et al.  Feature Selection with Attributes Clustering by Maximal Information Coefficient , 2013, ITQM.

[18]  Pan Li,et al.  A Temporal and Spatial Correlation Based Missing Values Imputation Algorithm in Wireless Sensor Networks , 2010 .

[19]  Viktor Pocajt,et al.  Application of experimental design for the optimization of artificial neural network-based water quality model: a case study of dissolved oxygen prediction , 2018, Environmental Science and Pollution Research.

[20]  Jianzhuo Yan,et al.  Prediction of water quality time series data based on least squares support vector machine , 2012 .

[21]  Ha Young Kim,et al.  ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module , 2018, Expert Syst. Appl..

[22]  Hongju Cheng,et al.  Data prediction model in wireless sensor networks based on bidirectional LSTM , 2019, EURASIP J. Wirel. Commun. Netw..

[23]  Dongmei Jiang,et al.  Multimodal Affective Dimension Prediction Using Deep Bidirectional Long Short-Term Memory Recurrent Neural Networks , 2015, AVEC@ACM Multimedia.

[24]  Junfei Qiao,et al.  An efficient self-organizing RBF neural network for water quality prediction , 2011, Neural Networks.

[25]  Peter Batista Cheung,et al.  Parameter Estimation of Seasonal Arima Models for Water Demand Forecasting Using the Harmony Search Algorithm , 2017 .

[26]  B. Tomassetti,et al.  Analysis of surface ozone using a recurrent neural network. , 2015, The Science of the total environment.

[27]  Chuyuan Wang,et al.  Examining the ecosystem health and sustainability of the world's largest mangrove forest using multi-temporal MODIS products. , 2016, The Science of the total environment.

[28]  Kyongjin Sok,et al.  A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction , 2018, Air Quality, Atmosphere & Health.

[29]  Peng Jiang,et al.  Water quality prediction based on recurrent neural network and improved evidence theory: a case study of Qiantang River, China , 2019, Environmental Science and Pollution Research.

[30]  K. P. Singh,et al.  Support vector machines in water quality management. , 2011, Analytica chimica acta.

[31]  Shuangyin Liu,et al.  Study of short-term water quality prediction model based on wavelet neural network , 2013, Math. Comput. Model..

[32]  Arun Kumar Sangaiah,et al.  Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment , 2019, Sustainability.

[33]  Guang Liu,et al.  Learning performance prediction via convolutional GRU and explainable neural networks in e-learning environments , 2019, Computing.

[34]  Ahmed El-Shafie,et al.  Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring , 2014, Environmental Science and Pollution Research.

[35]  Kai Zhang,et al.  Forecasting with prediction intervals for periodic autoregressive moving average models , 2013, Journal of time series analysis.

[36]  Koji Zettsu,et al.  Dynamic pre-training of Deep Recurrent Neural Networks for predicting environmental monitoring data , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[37]  K. Reckhow.  Water quality prediction and probability network models , 1999 .

[38]  Chun Kiat Chang,et al.  Prediction of water quality index in constructed wetlands using support vector machine , 2015, Environmental Science and Pollution Research.