Elucidating the performance of hybrid models for predicting extreme water flow events through variography and wavelet analyses

Curceac, Stelian; Milne, Alice E.; Atkinson, Peter M.; Wu, Lianhai; Harris, Paul

doi:10.1016/j.jhydrol.2021.126442

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…Wavelet analysis is a common tool to disaggregate the original series and improve model accuracy in load forecasting [33]. A series of functions are used to represent data or functions [34]. The theory behind this is to obtain information at different scales or resolutions by high-pass and low-pass filters and to produce a series of wavelet coefficients that contribute to extracting characteristics in the frequency domain.…”

Section: Weather Data Energy Consumptionmentioning

confidence: 99%

A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest

Ling¹,

Tao²,

Zheng³

et al. 2021

Buildings

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Load monitoring can help users learn end-use energy consumption so that specific energy-saving actions can be taken to reduce the energy consumption of buildings. Nonintrusive monitoring (NIM) is preferred because of its low cost and nondisturbance of occupied space. In this study, a NIM method based on random forest was proposed to determine the energy consumption of building subsystems from the building-level energy consumption: the heating, ventilation and air conditioning system; lighting system; plug-in system; and elevator system. Three feature selection methods were used and compared to achieve accurate NIM based on weather parameters, wavelet analysis, and principal component analysis. The implementation of the proposed method in an office building showed that it can obtain the subloads accurately, with root-mean-square errors of less than 46.4 kW and mean relative errors of less than 12.7%. The method based on weather parameters can provide the most accurate results. The proposed method can help improve the energy efficiency of building service systems during the operation or renovation stage.

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Section: Weather Data Energy Consumptionmentioning

confidence: 99%

A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest

Ling¹,

Tao²,

Zheng³

et al. 2021

Buildings

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“…Precipitation data are needed for solving a large variety of water resource engineering problems (e.g., those investigated in [1][2][3][4][5][6][7]) and can be obtained through either groundbased gauge or satellite networks [8]. The former networks are known to offer more accurate data, while the latter are in general more spatially dense because of their lower cost ( [9][10][11][12]).…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data

Papacharalampous,

Tyralis,

Doulamis

et al. 2023

Remote Sensing

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Regression algorithms are regularly used for improving the accuracy of satellite precipitation products. In this context, satellite precipitation and topography data are the predictor variables, and gauged-measured precipitation data are the dependent variables. Alongside this, it is increasingly recognised in many fields that combinations of algorithms through ensemble learning can lead to substantial predictive performance improvements. Still, a sufficient number of ensemble learners for improving the accuracy of satellite precipitation products and their large-scale comparison are currently missing from the literature. In this study, we work towards filling in this specific gap by proposing 11 new ensemble learners in the field and by extensively comparing them. We apply the ensemble learners to monthly data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets that span over a 15-year period and over the entire contiguous United States (CONUS). We also use gauge-measured precipitation data from the Global Historical Climatology Network monthly database, version 2 (GHCNm). The ensemble learners combine the predictions of six machine learning regression algorithms (base learners), namely the multivariate adaptive regression splines (MARS), multivariate adaptive polynomial splines (poly-MARS), random forests (RF), gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and Bayesian regularized neural networks (BRNN), and each of them is based on a different combiner. The combiners include the equal-weight combiner, the median combiner, two best learners and seven variants of a sophisticated stacking method. The latter stacks a regression algorithm on top of the base learners to combine their independent predictions. Its seven variants are defined by seven different regression algorithms, specifically the linear regression (LR) algorithm and the six algorithms also used as base learners. The results suggest that sophisticated stacking performs significantly better than the base learners, especially when applied using the LR algorithm. It also beats the simpler combination methods.

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“…The proposed hybrid method is used to assess the reliability of the wavelet-M5 model in the presence of diverse hydrological phenomena. The daily and monthly measurements evaluate the model's ability to account for the system's autoregressive and seasonal characteristics (Curceac et al 2021).…”

mentioning

confidence: 99%

Investigating the Accuracy of Hybrid Models with Wavelet Transform in the Forecast of Watershed Runoff

Saravani,

Kashef,

Farmahini

et al. 2023

JWMM

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In the hydrological cycle, runoff precipitation is one of the most significant and complex phenomena. In order to develop and improve predictive models, different perspectives have been presented in its modeling. Hydrological processes can be confidently modeled with the help of artificial intelligence techniques. In this study, the runoff of the Leilanchai watershed was simulated using artificial neural networks (ANNs) and M5 model tree methods and their hybrid with wavelet transform. Seventy percent of the data used in the train state and thirty percent in the test state were collected in this watershed from 2000 to 2021. In addition to daily and monthly scales, simulated and observed results were compared within each scale. Initially, the rainfall and runoff time series were divided into multiple sub-series using the wavelet transform to combat instability. The resultant subheadings were then utilized as input for an ANN and M5 model tree. The results demonstrated that hybrid models with wavelet improved the ANN model's daily accuracy by 4% and its monthly accuracy by 26%. It also improved the M5 model tree's daily and monthly accuracy by 4% and 41%. The wavelet-M5 model's accuracy does not diminish to the same degree as the wavelet-ANN (WANN) model as the forecast horizon lengthens. Consequently, the Leilanchai watershed has a relatively stable behavior pattern. Finally, hybrid models, in conjunction with the wavelet transform, improve forecast accuracy.

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Elucidating the performance of hybrid models for predicting extreme water flow events through variography and wavelet analyses

Cited by 5 publications

References 28 publications

A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest

A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest

Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data

Investigating the Accuracy of Hybrid Models with Wavelet Transform in the Forecast of Watershed Runoff

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