On the reliability of a novel MODWT-based hybrid ARIMA-artificial intelligence approach to forecast daily Snow Depth (Case study: The western part of the Rocky Mountains in the U.S.A)

Adib, Arash; Zaerpour, Arash; Lotfirad, Morteza

doi:10.1016/j.coldregions.2021.103342

Cited by 25 publications

(12 citation statements)

References 26 publications

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“…According to Figure 7, the SWAT model had the best performance in simulating peak flows in the basin under study. The models are also compared in Figure 7 in the calibration and validation phases using the Taylor diagram (Adib et al 2021c;Taylor 2001) Figure 7 shows that the models developed for the basin under study had approximately the same standard deviation in the calibration phase. However, all the models underestimated the observed standard deviation in the calibration phase and had insignificant variability.…”

Section: Resultsmentioning

confidence: 99%

Improving the performance of rainfall-runoff models using the gene expression programming approach

Esmaeili-Gisavandani

Lotfirad

Sofla

et al. 2021

Journal of Water and Climate Change

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In this study, five hydrological models, including the soil and water assessment tool (SWAT), identification of unit hydrograph and component flows from rainfall, evapotranspiration, and streamflow (IHACRES), Hydrologiska Byråns Vattenbalansavdelning (HBV), Australian water balance model (AWBM), and Soil Moisture Accounting (SMA), were used to simulate the flow of the Hablehroud River, north-central Iran. All the models were validated based on the root mean square error (RMSE), coefficient of determination (R2), Nash-Sutcliffe model efficiency coefficient (NS), and Kling-Gupta efficiency (KGE). It was found that SWAT, IHACRES, and HBV had satisfactory results in the calibration phase. However, only the SWAT model had good performance in the validation phase and outperformed the other models. It was also observed that peak flows were generally underestimated by the models. The sensitivity analysis results of the model parameters were also evaluated. A hybrid model was developed using gene expression programming (GEP). According to the error measures, the ensemble model had the best performance in both calibration (NS = 0.79) and validation (NS = 0.56). The GEP combination method can combine model outputs from less accurate individual models and produce a superior river flow estimate.

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Section: Resultsmentioning

confidence: 99%

Improving the performance of rainfall-runoff models using the gene expression programming approach

Esmaeili-Gisavandani

Lotfirad

Sofla

et al. 2021

Journal of Water and Climate Change

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“…MODWT is a modified DWT and has been used for real-time intelligent fault disturbance detection [21]. In particular, MODWT can avoid the sub-sampling process and ensure that information is derived only from past and present data during specific wavelet decomposition [22]. Hence, the MODWT algorithm enables efficient and timely extraction of features in both the time and frequency domains.…”

Section: Introductionmentioning

confidence: 99%

Intelligent intrusion detection for optical fiber perimeter security system based on an improved high efficiency feature extraction technique

Sun,

Guo

2024

Meas. Sci. Technol.

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The automated analysis of optical fiber vibration sensing data has been highly demanded in engineering applications. Therefore, intrusion analysis, which aims at detecting, recognizing, and classifying intrusions, holds great importance for optical fiber vibration sensing. In this work, an intelligent intrusion detection scheme employing an improved high-efficiency feature extraction technique and utilizing a dual Mach-Zehnder interferometer (DMZI)-based optical fiber perimeter security system is proposed. So, the DMZI-based perimeter security system in practical settings can be successfully established. Specifically, time-frequency feature vectors with nine features are firstly constructed using a maximal overlap discrete wavelet transformation approach and a zero crossing rate method. Then, the feature vectors are classified into corresponding categories using a radial basis function neural network. The effectiveness of the proposed scheme has been validated using six types of human intrusions, such as knocking, climbing, waggling, cutting, crashing and kicking the fence. The results show that the given intrusions can be accurately and rapidly recognized by the proposed scheme. The average recognition rate of 95.0% is achieved, and the average processing time for each sample data is only 0.033 s, which is significantly lower than the sampling interval (0.3 s) in our experiment. It is believed that the proposed scheme holds promising potential in the field of optical fiber perimeter security systems.

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“…Among the many time series decomposition methods, seasonal decomposition and wavelet decomposition have been widely used (Adib et al 2021;He et al 2022;Khan et al 2020). In the seasonal decomposition method, a time series is divided into trend, periodic and residual terms, which represent different typical characteristics, and the predictability of the trend and periodic terms' is generally high (Zhu et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Rainfall time series prediction based on the DWT-SVR- Prophet hybrid model

Rao

et al. 2023

Preprint

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The discrete wavelet method can be used to decompose rainfall time series into subseries of different frequencies. It would be worthwhile to investigate whether combining forecasting results from different frequency subseries could improve the accuracy of rainfall prediction. A novel DWT-SVR-Prophet (DSP) hybrid model for rainfall prediction is proposed in this paper. First, the rainfall time series is decomposed into high-frequency and low-frequency subseries using discrete wavelet transform (DWT). The SVR and Prophet models are then used to predict high-frequency and low-frequency subsequences, respectively. Finally, the predicted rainfall is determined by summing the predicted values of each subsequence. A case study in China is conducted from January 1, 2014, to June 30, 2016. The results show that the DSP model provides excellent prediction, with RMSE, MAE, R2 values of 6.17, 3.3, and 0.75, respectively. The DSP model yields higher prediction accuracy than the three baseline models considered, with the prediction accuracy ranking as follows: DSP > SSP > Prophet > SVR. In addition, the DSP model is quite stable, and can achieve good results when applied to rainfall data from various climate types, with RMSEs ranging from 1.24 to 7.31, MAEs ranging from 0.52 to 6.14 and R2 values ranging from 0.62 to 0.75. The proposed model may provide a noval approach for rainfall forecasting and is readily adaptable to other time series predictions.

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On the reliability of a novel MODWT-based hybrid ARIMA-artificial intelligence approach to forecast daily Snow Depth (Case study: The western part of the Rocky Mountains in the U.S.A)

Cited by 25 publications

References 26 publications

Improving the performance of rainfall-runoff models using the gene expression programming approach

Improving the performance of rainfall-runoff models using the gene expression programming approach

Intelligent intrusion detection for optical fiber perimeter security system based on an improved high efficiency feature extraction technique

Rainfall time series prediction based on the DWT-SVR- Prophet hybrid model

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