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101.
Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model 总被引：1，自引：0，他引：1
Rahim?BarzegarEmail author Asghar?Asghari Moghaddam Jan?Adamowski Bogdan?Ozga-Zielinski 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(3):799-813
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. 相似文献
102.
The first step in any seismic hazard study is the definition of seismogenic sources and the estimation of magnitude-frequency relationships for each source. There is as yet no standard methodology for source modeling and many researchers have worked on this topic. This study is an effort to define linear and area seismic sources for Northern Iran. The linear or fault sources are developed based on tectonic features and characteristic earthquakes while the area sources are developed based on spatial distribution of small to moderate earthquakes. Time-dependent recurrence relationships are developed for fault sources using renewal approach while time-independent frequency-magnitude relationships are proposed for area sources based on Poisson process. GIS functionalities are used in this study to introduce and incorporate spatial-temporal and geostatistical indices in delineating area seismic sources. The proposed methodology is used to model seismic sources for an area of about 500 by 400 square kilometers around Tehran. Previous researches and reports are studied to compile an earthquake/fault catalog that is as complete as possible. All events are transformed to uniform magnitude scale; duplicate events and dependent shocks are removed. Completeness and time distribution of the compiled catalog is taken into account. The proposed area and linear seismic sources in conjunction with defined recurrence relationships can be used to develop time-dependent probabilistic seismic hazard analysis of Northern Iran. 相似文献