Assessing the quality of eight different maximum temperature time series as inputs when using artificial neural networks to forecast monthly rainfall at Cape Otway, Australia

The objective of this contribution is to prepare a methodology of using artificial neural networks for equalizing time series when considering seasonal fluctuations on the example of the Czech Republic import from the People´s Republic of China. If we focus on the relation of neural networks and time series, it is possible to state that both the purpose of time series themselves and the nature of all the data are what matters. The purpose of neural networks is to record the process of time series and to forecast individual data points in the best possible way. From the discussion part it follows that adding other variables significantly improves the quality of the equalized time series. Not only the performance of the networks is very high, but the individual MLP networks are also able to capture the seasonal fluctuations in the development of the monitored variable, which is the CR import from the PRC.

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Modelling and Forecasting of Monthly Rainfall and Temperature Time Series Using SARIMA for Trend Detection- A Case Study of Umiam, Meghalaya (India)

International Journal of Environment and Climate Change ◽

10.9734/ijecc/2020/v10i1130276 ◽

2020 ◽

pp. 155-172

Author(s):

P. P. Dabral ◽

Issac Tabing

Keyword(s):

Time Series ◽

Minimum Temperature ◽

Moving Average ◽

Temperature Time Series ◽

Monthly Rainfall ◽

Trend Detection ◽

Maximum Temperature ◽

Lm Test ◽

Temperature Time ◽

Sarima Models

Seasonal Auto Regressive Integrative Moving Average Models (SARIMA) were developed for monthly rainfall, mean monthly maximum and minimum temperature time series for Umiam (Barapani), Meghalaya (India). The best model was selected based on the minimum values of AIC and BIC criteria as well as based on observing the ACF and PACF plot of residuals. SARIMA (5,1,2) x (1,1,1)12, SARIMA (2,1,2) x (2,1,1)12, SARIMA (6,1,4) x (2,1,3)12 models were found to be the best fit model for the monthly rainfall, mean monthly maximum and minimum temperatures time series respectively. The adequacy of the SARIMA models was also verified using the Ljung-Box (Q) statistic test. McLeod-Li test and Engle’s ARCH LM test were carried out for residuals. The results indicated that there was no Arch effect in the established SARIMA models and models can be used for forecasting the future values for the year 2013 to 2028. The determination of trend in monthly rainfall, mean maximum and minimum temperatures in the forecasted series were done using different trend analysis techniques. For monthly rainfall and mean monthly minimum temperature time series, all the selected methods supported no significant trend. However, in the case of mean monthly maximum temperature time series, three selected methods supported falling trend.

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An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i9.404 ◽

2017 ◽

Vol 7 (9) ◽

pp. 13

Author(s):

Bhargavi Munnaluri ◽

K. Ganesh Reddy

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Wind Speed ◽

Fossil Fuels ◽

Renewable Energy Sources ◽

Support Vector ◽

Hybrid Forecasting ◽

Artificial Neural ◽

Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed. In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

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Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

10.31221/osf.io/cjv7p ◽

2019 ◽

Author(s):

Chem Int

Keyword(s):

Neural Networks ◽

Water Quality ◽

Wastewater Treatment ◽

Artificial Neural Networks ◽

Performance Modeling ◽

Predictive Performance ◽

Mathematical Tool ◽

Effluent Water ◽

Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

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ARTIFICIAL NEURAL NETWORKS APPLICATION IN TIME SERIES WATER DISCHARGE FOR THE PREDICTION OF FLOODING IN MOHAWK RIVER, NEW YORK

10.1130/abs/2019se-327791 ◽

2019 ◽

Author(s):

Katerina Tsakiri ◽

◽

Antonios E Marsellos ◽

Stelios Kapetanakis

Keyword(s):

Neural Networks ◽

New York ◽

Time Series ◽

Artificial Neural Networks ◽

Water Discharge ◽

Artificial Neural

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Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

Atmospheric Measurement Techniques ◽

10.5194/amt-9-53-2016 ◽

2016 ◽

Vol 9 (1) ◽

pp. 53-62 ◽

Cited By ~ 13

Author(s):

R. D. García ◽

O. E. García ◽

E. Cuevas ◽

V. E. Cachorro ◽

A. Barreto ◽

...

Keyword(s):

Neural Networks ◽

Time Series ◽

Artificial Neural Networks ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Mineral Dust ◽

Dust Particles ◽

Filter Radiometer ◽

Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

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