Application of Acoustic tomographic data to the short-term streamflow forecasting using data-driven methods and discrete wavelet transform

Olfatmiri, Yousef; Bahreinimotlagh, Masoud; Jabbari, Ebrahim; Kawanisi, Kiyosi; Hasanabadi, AmirHosein; Sawaf, Mohamad Basel Al

doi:10.1016/j.jhydrol.2022.127739

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…The variation of runoff over time is often influenced by a combination of factors. Runoff is not only characterised by trends and periodicity, but also by abrupt changes and a multi-scale structure, which has a multi-level evolution [39]. The wavelet transform can clearly reveal the multiple cycles of runoff hidden in the time series.…”

Section: Periodicity Analysis By Morlet Continuous Wavelet Transformmentioning

confidence: 99%

Analysis of Runoff Variation and Future Trends in a Changing Environment: Case Study for Shiyanghe River Basin, Northwest China

et al. 2023

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Changes in the hydrological cycle and water resources are inevitable consequences of environmental change, and runoff is an important element of the hydrological cycle. Therefore, the assessment of runoff changes is crucial for water resources management and socio-economic development. As an inland river basin in the arid zone of northwest China, the Shiyang River Basin is very vulnerable to environmental changes. Consequently, this study evaluated the past runoff evolution of the Shiyang River basin using a variety of statistical tools. In addition, the improved Soil and Water Assessment Tool (SWAT) was used to predict runoff trends from 2019 to 2050 under potential future climate change and land use projection scenarios in the future for the Shiyang River Basin. In the inland river basins, water resources mainly come from headwaters of the rivers in the upper mountainous regions, where they are more sensitive. Therefore, this study not only examined the mainstream of the Shiyang River, but also the six tributaries in the upper stream. The results indicate that the mainstream of the Shiyang River Basin and its six upstream tributaries all showed declining trends from the 1950s to 2019, and most of the rivers will continue to insignificantly decrease until 2050. Furthermore, there are two main timescales for runoff in the past as well as future: one is around 40 years and another is 20-30 years. In the meantime, the Shiyang River and its tributaries have relatively consistent change characteristics. The results of this study will provide assistance to basin management agencies in developing more appropriate water resource management plans.

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Section: Periodicity Analysis By Morlet Continuous Wavelet Transformmentioning

confidence: 99%

Analysis of Runoff Variation and Future Trends in a Changing Environment: Case Study for Shiyanghe River Basin, Northwest China

et al. 2023

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“…Based on the previous studies (Wang et al, 2012, Olfatmiri et al, 2022and Liu et al,2022, the present study used the Haar wavelet, which is one of the simplest and most widely used types of wavelet. The more the number of the selected steps for decomposition is, the deeper the signal will be decomposed into high-path and low-path frequencies and the higher the accuracy will be.…”

Section: Series Decomposition By Waveletmentioning

confidence: 99%

Modeling low flows by univariate hybrid wavelet-time series approach

Mohammadifard

Eslamian

Modarres

2023

Preprint

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This research addressed the forecast of 7-day and 30-day low flows using daily flow data at two hydrometric stations of Qaleh-Shahrokh and Eskandari in Isfahan province, Iran. The two methods of ordinary time series modeling and time series modeling with the help of wavelet (wavelet-time series) are used to forecast low flows. The research also employs the wavelet method as a modern and effective way for the analysis of hydrological time series. In the wavelet-time series modeling, the target time series is decomposed into five levels using the Haar wavelet theory. With this decomposition, the series is divided into two parts including approximation a, which is the main nature of the data, and detail sub-series, which includes the white noise of the data. Then, the time series modeling steps are implemented for the approximation a. The results of the forecast of 7-day and 30-day low flow using the time series and wavelet-time series methods are explored by the error assessment criteria including the coefficient of correlation between the predicted and observed values, root mean squared error (RMSE), and mean absolute deviation (MAD). Finally, the coefficients of correlation between the predicted and observed values of 7-day and 30-day low flows in the Qaleh-Shahrokh and Eskandari stations are estimated at 0.87, 0.87, 0.55, and 0.95 by the time series method and 0.99, 0.99, 0.99, and 0.99 by the wavelet-time series method, respectively. The results show that the wavelet-time series method outperforms the time series method in predicting 7-day and 30-day low flows.

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Developing a novel hybrid model based on deep neural networks and discrete wavelet transform algorithm for prediction of daily air temperature

Ghasemlounia,

Gharehbaghi,

Ahmadi

et al. 2024

Air Qual Atmos Health

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Application of Acoustic tomographic data to the short-term streamflow forecasting using data-driven methods and discrete wavelet transform

Cited by 3 publications

References 33 publications

Analysis of Runoff Variation and Future Trends in a Changing Environment: Case Study for Shiyanghe River Basin, Northwest China

Analysis of Runoff Variation and Future Trends in a Changing Environment: Case Study for Shiyanghe River Basin, Northwest China

Modeling low flows by univariate hybrid wavelet-time series approach

Developing a novel hybrid model based on deep neural networks and discrete wavelet transform algorithm for prediction of daily air temperature

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