River discharge prediction using wavelet-based artificial neural network and long short-term memory models: a case study of Teesta River Basin, India

Chakraborty, Swarnadeepa; Biswas, Sujata

doi:10.1007/s00477-023-02443-y

Cited by 2 publications

(1 citation statement)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given the urgent global imperative for sustainable water resource management, these research endeavors take on heightened significance [14]. As time evolved, the methodologies to study these dynamics expanded, including statistical analysis [19][20][21][22], wavelets decomposition [23], artificial intelligence methods-neural networks [24][25][26], support vector regression [27], time series models [28], hydrological simulation [29], etc.…”

Section: Introductionmentioning

confidence: 99%

Study of the River Discharge Alteration

Bărbulescu,

Mohammed

2024

Water

View full text Add to dashboard Cite

This article aims to analyze the alteration in water discharge due to the building of one of the largest dams in Romania. Modifications in the hydrological patterns of the studied river were emphasized by a complex technique that includes decomposition models of the series into trends, seasonal indices, and random components, as well as into Intrinsic Mode Functions (IMFs). The Mann–Kendall trend test indicates the existence of different positive slopes for the subseries S1 and S2 (before and after the inception of the Siriu dam, respectively) built from the raw series, S. The stationarity hypothesis was rejected for all series. The multifractal analysis shows two different patterns of the data series. After decomposing the subseries S1 and S2, it resulted that the seasonality indices are not the same. Moreover, the seasonal variations decreased after building the dam. Empirical Mode Decomposition (EMD) unveils different short- and long-term patterns of the series before and after building the dam, concluding that there is a significant alteration in the river discharge after the dam’s inception.

show abstract

Section: Introductionmentioning

confidence: 99%

Study of the River Discharge Alteration

Bărbulescu,

Mohammed

2024

Water

View full text Add to dashboard Cite

show abstract

A new criteria for determining the best decomposition level and filter for wavelet-based data-driven forecasting frameworks- validating using three case studies on the CAMELS dataset

Mazarei Behbahani,

Mazarei

2023

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

Recently, numerous papers have been published in the eld of using preprocessing models (e.g. Discrete wavelet) in Data-driven Forecasting Frameworks (DDFF). There are some unresolved problems in these models like using future data, boundary affected data, and miss selection of decomposition level and wavelet lter that cause an erroneous result. However, Wavelet-based Data-driven Forecasting Framework (WDDFF) solves these problems. The rst two problems could be solved using Maximal Overlap Discrete Wavelet Transform (MODWT) and a trous algorithm (AT). As the best we know, there is no absolute solution for decomposition level and wavelet lter selection. Meanwhile, as a novel investigation, we are going to use Entropy to nd a solution for these problems. We are using the concept of predictability of time series using entropy for determining decomposition level and suitable lter, to develop the Maximal Overlap Discrete Wavelet-Entropy Transform (MODWET) to apply in WDDFF correctly. We will reveal the effectiveness of MODWET through three realworld case studies on the CAMELS data set. In these case studies, we will forecast the stream ow of determined stations from one month ahead to prove the effectiveness of using preprocessing models on forecasting accuracy. The proposed model is a combination of Input Variable Selection (IVS), preprocessing model, and Data-Driven Model (DDM). In conclusion, we will show that MODWET-ANN is the best model. In addition, we will realize how good entropy could nd decomposition level and lter, which solves the mentioned concerns about using WDDFF in real-world hydrological forecasting problems.

show abstract

River discharge prediction using wavelet-based artificial neural network and long short-term memory models: a case study of Teesta River Basin, India

Cited by 2 publications

References 67 publications

Study of the River Discharge Alteration

Study of the River Discharge Alteration

A new criteria for determining the best decomposition level and filter for wavelet-based data-driven forecasting frameworks- validating using three case studies on the CAMELS dataset

Contact Info

Product

Resources

About