Research on the water quality forecast method based on SVM

2009 ◽  
Author(s):  
Jian Cao ◽  
Hongsheng Hu ◽  
Suxiang Qian ◽  
Gongbiao Yan
Keyword(s):  
Water Quality ◽  
Forecast Method ◽  
Water Quality Forecast ◽  
Quality Forecast

Bathing Water Quality Forecast for Chennai Coastal Waters

2020 ◽  
Vol 89 (sp1) ◽  
pp. 111
Author(s):  
Uma Sankar Panda ◽  
Uma Kanta Pradhan ◽  
Saka Sujith Kumar ◽  
Subrat Naik ◽  
Mehmuna Begum ◽  
...  
Keyword(s):  
Water Quality ◽  
Coastal Waters ◽  
Bathing Water ◽  
Bathing Water Quality ◽  
Water Quality Forecast ◽  
Quality Forecast

scholarly journals Daily Water Quality Forecast of the South-To-North Water Diversion Project of China Based on the Cuckoo Search-Back Propagation Neural Network

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1471 ◽  
Author(s):  
Dongguo Shao ◽  
Xizhi Nong ◽  
Xuezhi Tan ◽  
Shu Chen ◽  
Baoli Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Back Propagation ◽  
Cuckoo Search ◽  
Training Data ◽  
Water Diversion ◽  
Daily Water ◽  
Bp Model ◽  
Water Quality Forecast ◽  
Quality Forecast

Water quality forecast is a critical part of water security management. Spatiotemporal and multifactorial variations make water quality very complex and changeable. In this article, a novel model, which was based on back propagation neural network that was optimized by the Cuckoo Search algorithm (hereafter CS-BP model), was applied to forecast daily water quality of the Middle Route of South-to-North Water Diversion Project of China. Nine water quality indicators, including conductivity, chlorophyll content, dissolved oxygen, dissolved organic matter, pH, permanganate index, turbidity, total nitrogen, and water temperature were the predictand. Seven external environmental factors, including air temperature, five particulate matter (PM2.5), rainfall, sunshine duration, water flow, wind velocity, and water vapor pressure were the default predictors. A data pre-processing method was applied to select pertinent predictors. The results show that the CS-BP model has the best forecast accuracy, with the Mean Absolute Percentage Errors (MAPE) of 0.004%–0.33%, and the lowest Root Mean Square Error (RMSE) of each water quality indicator in comparison with traditional Back Propagation (BP) model, General Regression Neural Network model and Particle Swarm Optimization-Back Propagation model under default data proportion, 150:38 (training data: testing data). When training data reduced from 150 to 140, and from 140 to 130, the CS-BP model still produced the best forecasts, with the MAPEs of 0.014%–0.057% and 0.004%–1.154%, respectively. The results show that the CS-BP model can be an effective tool in daily water quality forecast with limited observed data. The improvement of the Cuckoo Search algorithm such as calculation speed, the forecast errors reduction of the CS-BP model, and the large-scale impacts such as land management on different water quality indicators, will be the focus of future research.

A Chaotic Prediction Method to Time Series Data

2010 ◽  
Vol 113-116 ◽  
pp. 1367-1370 ◽  
Author(s):  
Bin Sheng Liu ◽  
Ying Wang ◽  
Xue Ping Hu
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Time Series ◽  
Drinking Water ◽  
Time Series Data ◽  
Prediction Method ◽  
Drinking Water Quality ◽  
Series Data ◽  
Gray Model ◽  
Forecast Method

There are many ways to predict drinking water quality such as neural network, gray model, ARIMA. But the prediction precise is need to improve. This paper proposes a new forecast method according the characteristic of drinking water quality and the evidence showed that the prediction is effectively. So it is able to being used in actual prediction.

Application of Self-Adaptive Exponential Smoothing Method in the Water Quality Forecast of Poyang Lake

2012 ◽  
Vol 518-523 ◽  
pp. 1464-1467
Author(s):  
Bin Xiang Liu ◽  
Qun Cao ◽  
Xiang Cheng
Keyword(s):  
Water Quality ◽  
Dynamic Characteristic ◽  
Poyang Lake ◽  
Exponential Smoothing ◽  
Smoothing Parameter ◽  
Smoothing Method ◽  
Forecast Result ◽  
Exponential Smoothing Method ◽  
Water Quality Forecast ◽  
Self Adaptive

The smoothing parameter is a constant when forecasting water quality using exponential smoothing, which usually renders the error to be enlarged, but the assumption of constant is out of accord with the practice. Based on the deep analysis of deficiency of traditional exponential smoothing, this paper establishes self-adaptive exponential smoothing model and compares the forecast result. It is proved that the dynamic characteristic of water quality can be better reflected and the forecasting precision can be improved further by self-adaptive exponential smoothing model.

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