Geostatistical study of spatial correlations of lead and zinc concentration in urban reservoir. Study case Czerniakowskie Lake, Warsaw, Poland

Fabijańczyk, Piotr; Zawadzki, Jarosław; Wojtkowska, Małgorzata

doi:10.1515/geo-2016-0043

Cited by 2 publications

(1 citation statement)

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“…However, when the amount of data to be analyzed is insufficient, the analysis results are unreliable. Recently, some new methods of correlation analysis have been proposed, such as the spatial cross-correlations method [16]. This method combines spatial distribution information and uses semivariance and experimental variograms to calculate the spatial correlation of water quality.…”

Section: Introductionmentioning

confidence: 99%

A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture

Zhang

Zhao

et al. 2019

Sensors

137

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An accurate prediction of cage-cultured water quality is a hot topic in smart mariculture. Since the mariculturing environment is always open to its surroundings, the changes in water quality parameters are normally nonlinear, dynamic, changeable, and complex. However, traditional forecasting methods have lots of problems, such as low accuracy, poor generalization, and high time complexity. In order to solve these shortcomings, a novel water quality prediction method based on the deep LSTM (long short-term memory) learning network is proposed to predict pH and water temperature. Firstly, linear interpolation, smoothing, and moving average filtering techniques are used to repair, correct, and de-noise water quality data, respectively. Secondly, Pearson’s correlation coefficient is used to obtain the correlation priors between pH, water temperature, and other water quality parameters. Finally, a water quality prediction model based on LSTM is constructed using the preprocessed data and its correlation information. Experimental results show that, in the short-term prediction, the prediction accuracy of pH and water temperature can reach 98.56% and 98.97%, and the time cost of the predictions is 0.273 s and 0.257 s, respectively. In the long-term prediction, the prediction accuracy of pH and water temperature can reach 95.76% and 96.88%, respectively.

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Section: Introductionmentioning

confidence: 99%