Combining statistical machine learning models with ARIMA for water level forecasting: The case of the Red river

Phan, Thi-Thu-Hong; Nguyen, Xuan Phuong

doi:10.1016/j.advwatres.2020.103656

Cited by 90 publications

(33 citation statements)

References 43 publications

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“…Autoregressive integrated moving average (ARIMA) (Tencaliec et al 2015;Nguyen 2020) model (Eq. 5) is a statistical machine learning model, which shows good performance in the prediction of time series.…”

Section: Seasonal Autoregressive Integrated Moving Average (Sarima)mentioning

confidence: 99%

Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

Liu

Sang

Chang

et al. 2021

Preprint

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The fluctuation of water supply is affected by the living habits and population mobility, so the daily water supply is significantly non-stationarity, which presents a great challenge to the water demand prediction based on data-driven model. To solve this problem, the Hodrick-Prescott (HP) and wavelet transform (WT) time series decomposition methods, and ensemble learning (EL) were introduced, coupling model bidirectional long short term memory (BLSTM), seasonal autoregressive integrated moving average (SARIMA) and Gaussian radial basis function neural network (GRBFNN) were developed, and interval prediction was carried out based on student's t-test (T-test). This research method was applied to the daily water demand prediction in Shenzhen and cross-validation was performed. It is found that the decomposed subseries has obvious law, and WT is superior to HP decomposition method. However, the maximum decomposition level (MDL) of WT should not be set too high, otherwise the trend characteristics of subseries will be weakened. The results show that the potential characteristics and quantitative relationships of historical data can be learned accurately based on WT and coupling model. Although the corona virus disease 2019 (COVID-19) outbreak in 2020 caused a variation in water supply law, this variation is still within the interval prediction. The WT and coupling model satisfactorily predicted water demand and provided the lowest mean square error (0.17%), mean relative error (0.1) and mean absolute error (3.32%) and the highest Nash-Sutcliffe efficiency (97.21%) and correlation coefficient (0.99) in testing set.

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Section: Seasonal Autoregressive Integrated Moving Average (Sarima)mentioning

confidence: 99%

Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

Liu

Sang

Chang

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the results achieved with Support Vector Machine, linear autoregressive models and nonlinear models were accurate for a lag time between input data and forecast horizon lower than 24 h, while the statistical and hydrodynamic models require a large number of input data, making them complex to use. In the past few decades, Artificial Intelligence (AI) models have taken hold for the prediction of complex natural phenomena [16][17][18][19][20][21][22]. However, to date their application for the tide forecasting leads to accurate predictions only for a short forecast horizon [23] or is related to ocean environments with a reduced number of measurement points [24,25].…”

Section: Introductionmentioning

confidence: 99%

Tide Prediction in the Venice Lagoon Using Nonlinear Autoregressive Exogenous (NARX) Neural Network

Nunno

Marinis

Gargano

et al. 2021

Water

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In the Venice Lagoon some of the highest tides in the Mediterranean occur, which have influenced the evolution of the city of Venice and the surrounding lagoon for centuries. The forecast of “high waters” in the lagoon has always been a matter of considerable practical interest. In this study, tide prediction models were developed for the entire lagoon based on Nonlinear Autoregressive Exogenous (NARX) neural networks. The NARX-based model development was performed in two different stages. The first stage was the training and testing of the NARX network, performed on data collected in a given time interval at the tide gauge of Punta della Salute, at the end of Canal Grande. The second stage consisted of a comprehensive validation of the model in the entire Venice Lagoon, with a detailed analysis of data from three measuring stations located in points of the lagoon with different characteristics. Good predictions were achieved regardless of whether the meteorological parameters were considered among input parameters, even with considerable time advance. Furthermore, the forecasting model based on NARX has proved capable of predicting even exceptional high tides. The proposed model could be a useful support tool for the management of the MOSE system, which will protect Venice from high waters.

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“…If these assumptions are not satisfied, the model accuracy is significantly reduced. In order to improve the ARIMA efficiency, a number of papers [16,17,26,27,34,35] suggested a hybrid approach to the time series analysis. For example, the paper [17] proposed to apply ARIMA together with discrete wavelet transform and neural network LSTM.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Model for Time Series of Complex Structure with ARIMA Components

2021

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A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

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Combining statistical machine learning models with ARIMA for water level forecasting: The case of the Red river

Cited by 90 publications

References 43 publications

Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

Tide Prediction in the Venice Lagoon Using Nonlinear Autoregressive Exogenous (NARX) Neural Network

Hybrid Model for Time Series of Complex Structure with ARIMA Components

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