A data-mining approach to predict influent quality

Kusiak, Andrew; Verma, Ankit; Wei, Xiupeng

doi:10.1007/s10661-012-2701-2

Cited by 34 publications

(21 citation statements)

References 21 publications

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“…The Random Forest approach has been used in a number of public health studies such as to predict deer mouse population dynamics [15] and the effect of seasonality on wastewater quality [16]. In the area of predicting avian influenza, Random Forests have been successfully applied to spatial challenges such as [17], which provides the first global-scale model of low-pathogenicity avian influenza in wild birds.…”

Section: Introductionmentioning

confidence: 99%

Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks

et al. 2014

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BackgroundTime series models can play an important role in disease prediction. Incidence data can be used to predict the future occurrence of disease events. Developments in modeling approaches provide an opportunity to compare different time series models for predictive power.ResultsWe applied ARIMA and Random Forest time series models to incidence data of outbreaks of highly pathogenic avian influenza (H5N1) in Egypt, available through the online EMPRES-I system. We found that the Random Forest model outperformed the ARIMA model in predictive ability. Furthermore, we found that the Random Forest model is effective for predicting outbreaks of H5N1 in Egypt.ConclusionsRandom Forest time series modeling provides enhanced predictive ability over existing time series models for the prediction of infectious disease outbreaks. This result, along with those showing the concordance between bird and human outbreaks (Rabinowitz et al. 2012), provides a new approach to predicting these dangerous outbreaks in bird populations based on existing, freely available data. Our analysis uncovers the time-series structure of outbreak severity for highly pathogenic avain influenza (H5N1) in Egypt.

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Section: Introductionmentioning

confidence: 99%

Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks

et al. 2014

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“…The past observation data for forecasting inlet water quality helps to adjust the performance parameters and keep the wastewater treatment plant (WWTP) operating economically and stably. Therefore, inlet water quality forecasting is vital for wastewater treatment [3], which gives messages in advance for guiding the operations with a high efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, various models for dealing with this issue have been proposed, e.g., artificial neural network [4,5], auto-regressive integrated moving average [6], data mining [3], Multiple regression method [7], adaptive recursive least squares [8], support vector machine [9], partial least squares [10], and measured hydraulic dynamics [11]. Among these models, machine learning has attracted much…”

Section: Introductionmentioning

confidence: 99%

Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine

Yang

Bai

et al. 2018

Water

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Abstract:The stable operation of sewage treatment is of great significance to controlling regional water environment pollution. It is also important to forecast the inlet water quality accurately, which may ensure the purification efficiency of sewage treatment at a low cost. In this paper, a combined kernel principal component analysis (KPCA) and extreme learning machine (ELM) model is established to forecast the inlet water quality of sewage treatment. Specifically, KPCA is employed for feature extraction and dimensionality reduction of the inlet wastewater quality and ELM is utilized for the future inlet water quality forecasting. The experimental results indicated that the KPCA-ELM model has a higher accuracy than the other comparison PCA-ELM model, ELM model, and back propagation neural network (BPNN) model for forecasting COD and BOD concentration of the inlet wastewater, with mean absolute error (MAE) values of 2.322 mg/L and 1.125 mg/L, mean absolute percentage error (MAPE) values of 1.223% and 1.321%, and root mean square error (RMSE) values of 3.108 and 1.340, respectively. It is recommended from this research that the method may provide a reliable and effective reference for forecasting the water quality of sewage treatment.

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“…This is for gaining familiarity with all basic types. In majority of studies in order to rainfall estimation, is used several data mining algorithms together [12] Moreover, statistical inference is often thought to be the result of a partnership and is not related to the causes. A machine technique is interpreted easily.…”

Section: Introductionmentioning

confidence: 99%

Advanced of Mathematics-Statistics Methods to Radar Calibration for Rainfall Estimation; A Review

Nikahd¹

2015

IJRITCC

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Abstract-Ground-based radar is known as one of the most important systems for precipitation measurement at high spatial and temporal resolutions. Radar data are recorded in digital manner and readily ingested to any statistical analyses. These measurements are subjected to specific calibration to eliminate systematic errors as well as minimizing the random errors, respectively. Since statistical methods are based on mathematics, they offer more precise results and easy interpretation with lower data detail. Although they have challenge to interpret due to their mathematical structure, but the accuracy of the conclusions and the interpretation of the output are appropriate. This article reviews the advanced methods in using the calibration of ground-based radar for forecasting meteorological events include two aspects: statistical techniques and data mining. Statistical techniques refer to empirical analyses such as regression, while data mining includes the Artificial Neural Network (ANN), data Kriging, Nearest Neighbour (NN), Decision Tree (DT) and fuzzy logic. The results show that Kriging is more applicable for interpolation. Regression methods are simple to use and data mining based on Artificial Intelligence is very precise. Thus, this review explores the characteristics of the statistical parameters in the field of radar applications and shows which parameters give the best results for undefined cases.

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A data-mining approach to predict influent quality

Cited by 34 publications

References 21 publications

Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks

Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks

Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine

Advanced of Mathematics-Statistics Methods to Radar Calibration for Rainfall Estimation; A Review

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