Evaluating ability of three types of discrete wavelet transforms for improving performance of different ML models in estimation of daily-suspended sediment load

Esmaeili-Gisavandani, Hassan; Farajpanah, Hiwa; Adib, Arash; Kişi, Özgür; Riyahi, Mohammad Mehdi; Lotfirad, Morteza; Salehpoor, Jaber

doi:10.1007/s12517-021-09282-7

Cited by 15 publications

(5 citation statements)

References 45 publications

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“…A model with the lowest RMSE and MAE and the highest KGE, WI, and NSE was selected and proposed as an appropriate model. The formulae are as follows [35][36][37][38]:…”

Section: Evaluation Indexmentioning

confidence: 99%

Applicability of a CEEMD–ARIMA Combined Model for Drought Forecasting: A Case Study in the Ningxia Hui Autonomous Region

Ding

Liu

et al. 2022

Atmosphere

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In the context of global warming, the increasing frequency of drought events has caused negative impacts on agricultural productivity and societal activities. However, the drought occurrences have not been well predicted by any single model, and precipitation may show nonstationary behavior. In this study, 60 years of monthly precipitation data from 1960 to 2019 for the Ningxia Hui Autonomous Region were analyzed. The standard precipitation index (SPI) was used to classify drought events. This study combined the strengths of autoregressive integrated moving average (ARIMA) and complementary ensemble empirical mode decomposition (CEEMD) to predict drought. First, based on the precipitation dataset, the SPI at timescales of 1, 3, 6, 9, 12, and 24 months was calculated. Then, each of these SPI time series was predicted using the ARIMA model and the CEEMD–ARIMA combined model. Finally, the models′ performance was compared using statistical metrics, namely, root-mean-square error (RMSE), mean absolute error (MAE), Kling–Gupta efficiency (KGE), Willmott index (WI), and Nash–Sutcliffe efficiency (NSE). The results show that the following: (1) Compared with the ARIMA forecast value, the prediction results of the CEEMD–ARIMA model were in good agreement with the SPI values, indicating that the combined model outperformed the single model. (2) Two different models obtained the lowest accuracy for the SPI1 prediction and the highest accuracy for the SPI24 prediction. (3) The CEEMD–ARIMA model achieved higher prediction accuracy than the ARIMA model at each time scale. The most precise model during the test phase was the CEEMD–ARIMA model at SPI24 at Xiji Station, with error measures of MAE = 0.076, RMSE = 0.100, NSE = 0.994, KGE = 0.993, and WI = 0.999. Such findings will be essential for government to make decisions.

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“…A model with the lowest RMSE and MAE and the highest KGE, WI, and NSE was selected and proposed as an appropriate model. The formulae are as follows [35][36][37][38]:…”

Section: Evaluation Indexmentioning

confidence: 99%

Applicability of a CEEMD–ARIMA Combined Model for Drought Forecasting: A Case Study in the Ningxia Hui Autonomous Region

Ding

Liu

et al. 2022

Atmosphere

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“…Additionally, the research highlighted an improved imputation method that generated prediction accuracy exceeding 95%, a noteworthy achievement in the field of meteorological forecasting. Gisavandani et al (2022) delved into the impact of discrete wave transformations (DMODWTs) on machine learning (ML) models. Their research evaluated the performance of regression and classification models in estimating daily downtime and sediment loading (SSL).…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Predictive Accuracy through the Analysis of Banking Time Series: A Case Study from the Amman Stock Exchange

Al Wadi,

Al Singlawi,

Jaber

et al. 2024

JRFM

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This empirical research endeavor seeks to enhance the accuracy of forecasting time series data in the banking sector by utilizing data from the Amman Stock Exchange (ASE). The study relied on daily closed price index data, spanning from October 2014 to December 2022, encompassing a total of 2048 observations. To attain statistically significant results, the research employs various mathematical techniques, including the non-linear spectral model, the maximum overlapping discrete wavelet transform (MODWT) based on the Coiflet function (C6), and the autoregressive integrated moving average (ARIMA) model. Notably, the study’s findings encompass the comprehensive explanation of all past events within the specified time frame, alongside the introduction of a novel forecasting model that amalgamates the most effective MODWT function (C6) with a tailored ARIMA model. Furthermore, this research underscores the effectiveness of MODWT in decomposing stock market data, particularly in identifying significant events characterized by high volatility, which thereby enhances forecasting accuracy. These results hold valuable implications for researchers and scientists across various domains, with a particular relevance to the fields of business and health sciences. The performance evaluation of the forecasting methodology is based on several mathematical criteria, including the mean absolute percentage error (MAPE), the mean absolute scaled error (MASE), and the root mean squared error (RMSE).

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“…On the other hand, the execution speed of the MARS model is very high and there is no need to spend so much time. The MARS model has high accuracy in simulating PCP (Abraham et al 2001), flow discharge (Kisi et al 2022), suspended sediment load (Esmaeili-Gisavandani et al 2022) and flood (Msilini et al 2020).…”

Section: Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 99%

Improving the accuracy of satellite and reanalysis precipitation data by their ensemble usage

2022

Self Cite

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This study evaluated the outputs of five precipitation (PCP) datasets. These models are ECMWF reanalysis 5th generation (ERA5), precipitation estimation from remotely sensed information using artificial neural networks-climate data record (PERSIANN-CDR), Asian precipitation-highly resolved observational data integration toward evaluation (APHRODITE), The national centers for environmental prediction climate forecast system reanalysis (NCEP CFSR) and climatic research unit (CRU). The PCP outputs of these models were compared with data of nine synoptic stations in the Khuzestan province. The results indicated a better match between the APHRODITE outputs and the PCP data at most stations (R2 > 0.85, root-mean-square error (RMSE) < 17.049 mm and − 4.25 < Bias < 2.633 mm). However, CRU model has the highest critical success index (more than 0.711) and the lowest false alarm ratio (less than 0.2) and ERA5 has the highest probability of detection (more than 0.967) at most stations. Then, PCP outputs of five reanalysis (ERA5), interpolated (APHRODITE, NCEP CFSR and CRU) and satellite (PERSIANN-CDR) PCP datasets were combined to reduce the PCP estimation error. The multivariate adaptive regression splines models were employed for this purpose. The results show that the RMSE of all the stations, except Ahvaz station, decreased and the BIAS decreased too. Given the results, using ensemble data methods is a suitable way for reducing the error and increasing the accuracy of these models.

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Evaluating ability of three types of discrete wavelet transforms for improving performance of different ML models in estimation of daily-suspended sediment load

Cited by 15 publications

References 45 publications

Applicability of a CEEMD–ARIMA Combined Model for Drought Forecasting: A Case Study in the Ningxia Hui Autonomous Region

Applicability of a CEEMD–ARIMA Combined Model for Drought Forecasting: A Case Study in the Ningxia Hui Autonomous Region

Enhancing Predictive Accuracy through the Analysis of Banking Time Series: A Case Study from the Amman Stock Exchange

Improving the accuracy of satellite and reanalysis precipitation data by their ensemble usage

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