Multi-step Water Quality Forecasting Using a Boosting Ensemble Multi-Wavelet Extreme Learning Machine Model
PBN-AR
Instytucja
Instytut Meteorologii i Gospodarki Wodnej - Państwowy Instytut Badawczy
Informacje podstawowe
Główny język publikacji
en
Czasopismo
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (35pkt w roku publikacji)
ISSN
1436-3240
EISSN
1436-3259
Wydawca
SPRINGER
DOI
URL
Rok publikacji
2018
Numer zeszytu
3
Strony od-do
799-813
Numer tomu
32
Identyfikator DOI
Liczba arkuszy
1,00
Autorzy
Pozostali autorzy
+ 3
Słowa kluczowe
en
Water quality forecasting
Maximal overlap discrete wavelet transform
Extreme learning machine
Boosting ensemble
Aji-Chay River
Open access
Tryb otwartego dostępu
Otwarte czasopismo
Wersja tekstu w otwartym dostępie
Wersja opublikowana
Licencja otwartego dostępu
Creative Commons — Uznanie autorstwa
Czas opublikowania w otwartym dostępie
Przed publikacją
Data udostępnienia w sposób otwarty
2018-02-28
Streszczenia
Język
en
Treść
The use of electrical conductivity (EC) as a water quality indicator is useful for estimating the mineralization and salinity of water. The objectives of this study were to explore, for the first time, extreme learning machine (ELM) and wavelet-extreme learning machine hybrid (WA-ELM) models to forecast multi-step-ahead EC and to employ an integrated method to combine the advantages of WA-ELM models, which utilized the boosting ensemble method. For comparative purposes, an adaptive neuro-fuzzy inference system (ANFIS) model, and a WA-ANFIS model, were also developed. The study area was the Aji-Chay River at the Akhula hydrometric station in Northwestern Iran. A total of 315 monthly EC (µS/cm) datasets (1984–2011) were used, in which the first 284 datasets (90% of total datasets) were considered for training and the remaining 31 (10% of total datasets) were used for model testing. Autocorrelation function (ACF) and partial autocorrelation function (PACF) demonstrated that the 6-month lags were potential input time lags. The results illustrated that the single ELM and ANFIS models were unable to forecast the multi-step-ahead EC in terms of root mean square error (RMSE), coefficient of determination (R2) and Nash–Sutcliffe model efficiency coefficient (NSC). To develop the hybrid WA-ELM and WA-ANFIS models, the original time series of lags as inputs, and time series of 1, 2 and 3 month-step-ahead EC values as outputs, were decomposed into several sub-time series using different maximal overlap discrete wavelet transform (MODWT) functions, namely Daubechies, Symlet, Haar and Coiflet of different orders at level three. These sub-time series were then used in the ELM and ANFIS models as an input dataset to forecast the multi-step-ahead EC. The results indicated that single WA-ELM and WA-ANFIS models performed better than any ELM and ANFIS models. Also, WA-ELM models outperformed WA-ANFIS models. To develop the boosting multi-WA-ELM and multi-WA-ANFIS ensemble models, a least squares boosting (LSBoost) algorithm was used. The results showed that boosting multi-WA-ELM and multi-WA-ANFIS ensemble models outperformed the individual WA-ELM and WA-ANFIS models.
Cechy publikacji
discipline:Inżynieria środowiska
discipline:Environmental engineering
Original article
Original article presents the results of original research or experiment.
Oryginalny artykuł naukowy
Oryginalny artykuł naukowy przedstawia rezultaty oryginalnych badań naukowych lub eksperymentu.
Inne
System-identifier
PBN-R:823234
CrossrefMetadata from Crossref logo
Cytowania
Liczba prac cytujących tę pracę
Brak danych
Referencje
Liczba prac cytowanych przez tę pracę
Brak danych