TY - GEN
T1 - Hybrid approach for water quality UV-Vis absorbance time series forecasting in sewer systems
AU - Torres Abello, Andres Eduardo
AU - Plazas-Nossa, Leonardo
AU - Hofer, Thomas
AU - Jorge, ESCOBAR
AU - Florez Valencia, Leonardo
AU - Gruber, Günter
PY - 2017
Y1 - 2017
N2 - This work presents a hybrid approach based on seven methodologies combined with Principal Component Analysis (PCA) for UV-Vis absorbance time series forecasting. It was applied to four absorbance data sets in four different study sites. Thus, it is important to determine which forecasting methodology is best suited for different wavelengths, according to the target water quality. The Mean Absolute Percentage Error (MAPE) values were obtained in a range between 0% and 57% for all the study sites. Results shown that is not possible to have a best forecasting methodology among the proposed ones, because all of them would complement each other for different forecasting time steps and spectra range.
AB - This work presents a hybrid approach based on seven methodologies combined with Principal Component Analysis (PCA) for UV-Vis absorbance time series forecasting. It was applied to four absorbance data sets in four different study sites. Thus, it is important to determine which forecasting methodology is best suited for different wavelengths, according to the target water quality. The Mean Absolute Percentage Error (MAPE) values were obtained in a range between 0% and 57% for all the study sites. Results shown that is not possible to have a best forecasting methodology among the proposed ones, because all of them would complement each other for different forecasting time steps and spectra range.
KW - Artificial Neural Networks
KW - Discrete Fourier Transform
KW - Polynomial transforms
KW - Principal Component Analysis
KW - UV-Vis time series forecasting
KW - Water Quality
M3 - Conference contribution
BT - 14th International Conference on Urban Drainage-ICUD 2017
ER -