Water Quality Prediction of Small Watershed Based on Wavelet Neural Network

In recent years, China has faced a very serious issue of water pollution, which has had a dreadful impact on the ecological environment and human health. Due to the rapid growth of industry and economy, water pollution around China's urban areas has received extensive attentions. Among them, small watershed pollution, which is difficult to sample real-time data, is particularly prominent. Therefore, it is extremely important to propose new, better and more reliable prediction models to accurately predict the water quality in these small watersheds. This paper selects the water quality data of small watersheds around Chongqing for the study to come up with a new wavelet neural network model of forecasting using small amount of data to predict the China Water Quality Index (WQI). The present study is aimed to improve the prediction results by minimizing the prediction errors of current machine learning algorithms by considering the main environmental pollutant in small watersheds as input. The results show that when there is a strong interaction and correlation between the water quality characteristic attribute and WQI, the MAPE of the wavelet neural network model training results will decrease. In addition, the geographical location is found to play an important role in the Chongqing WQI forecast.

[1]  Richard J. Wagner,et al.  Guidelines and Standard Procedures for Continuous Water-Quality Monitors: Station Operation, Record Computation, and Data Reporting , 2014 .

[2]  Kulwinder Singh Parmar,et al.  River Water Prediction Modeling Using Neural Networks, Fuzzy and Wavelet Coupled Model , 2014, Water Resources Management.

[3]  Yang Dongyua.  THE THEORETICAL APPROACH OF THE ECOLOGICAL CITY , 2001 .

[4]  Xiang Tan,et al.  Water quality in the upper Han River basin, China: the impacts of land use/land cover in riparian buffer zone. , 2009, Journal of hazardous materials.

[5]  Taeshik Shon,et al.  A hybrid machine learning approach to network anomaly detection , 2007, Inf. Sci..

[6]  Taher Rajaee,et al.  Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers. , 2011, The Science of the total environment.

[7]  Ying Zhao,et al.  Water quality forecast through application of BP neural network at Yuqiao reservoir , 2007 .

[8]  Lubertus Bijlsma,et al.  Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis. , 2015, The Science of the total environment.

[9]  Katherine A. Klise,et al.  MULTIVARIATE APPLICATIONS FOR DETECTING ANOMALOUS WATER QUALITY , 2008 .

[10]  Guangxia Xu,et al.  A Survey for Mobility Big Data Analytics for Geolocation Prediction , 2017, IEEE Wireless Communications.

[11]  Michele Vespe,et al.  Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction , 2013, Entropy.

[12]  Ashok Lumb,et al.  A Review of Genesis and Evolution of Water Quality Index (WQI) and Some Future Directions , 2011 .

[13]  Durdu Ömer Faruk.  A hybrid neural network and ARIMA model for water quality time series prediction , 2010, Eng. Appl. Artif. Intell..

[14]  Balaji Rajagopalan,et al.  Wavelet‐based time series bootstrap model for multidecadal streamflow simulation using climate indicators , 2016 .

[15]  Miklas Scholz,et al.  Review of Ecological Engineering Solutions for Rural Non-Point Source Water Pollution Control in Hubei Province, China , 2013, Water, Air, & Soil Pollution.

[16]  Jing Meng,et al.  Impacts of soil and water pollution on food safety and health risks in China. , 2015, Environment international.