Hybrid SSA-ARIMA-ANN Model for Forecasting Daily Rainfall

Unnikrishnan, Poornima; Jothiprakash, V.

doi:10.1007/s11269-020-02638-w

Cited by 42 publications

(15 citation statements)

References 18 publications

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“…Since its ability to seek out the complicated nonlinear relationships between the given datasets, the ANNs can be applied to complex systems' modeling tasks. In the hydrological field, the ANNs have been used for different aims, for instance, flood or runoff forecasting [17,[22][23][24], rainfall forecasting [25][26][27], and evapotranspiration prediction [28,29].…”

Section: Introductionmentioning

confidence: 99%

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Wang

Liu

Yue

et al. 2020

Water

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A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model’s performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model.

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Section: Introductionmentioning

confidence: 99%

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Wang

Liu

Yue

et al. 2020

Water

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“…The results show that the Fourier series was the best method for flood frequency analysis. Daily rainfall was accurately predicted by the hybrid SSA-ARIMA-ANN model (Unnikrishnan and Jothiprakash, 2020). The simulated flow was better than the observed rainfall data using interpolation rainfall in the SWAT model (Zhang et al 2021).…”

Section: Introductionmentioning

confidence: 86%

Frequency analysis of extreme daily rainfall over an arid zone of Iran using Fourier series method

Jou

Mirhashemi

2022

Appl Water Sci

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In this paper frequency analysis of annual extreme daily rainfall of 14 gauging stations located in an arid zone of Iran were performed using parametric and nonparametric approaches. The parametric methods include normal, two- and three-parameter log-normal, two-parameter gamma, Pearson and log-Pearson type III, extreme value type I (Gumbel), generalized extreme value and generalized logistic distributions. The nonparametric approach is Fourier series method. The data were fitted to all of above mentioned models and the results showed that the goodness of fit of the data to Fourier series is much better than other parametric methods. Thus the Fourier series can be used as an alternative approach for frequency analysis of extreme daily rainfall in an arid zone. In addition, the quantiles can be calculated by the Fourier series.

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“…Khan et al [12] used Wavelet transformation, Autoregressive Integrated Moving Average (ARIMA), and Artificial Neural Network (ANN) strengths were combined to evaluate a new approach of a hybrid model's capability to correctly predict upcoming droughts. Unnikrishnan and Jothiprakash [13] proposed the combined Singular Spectrum Analysis (SSA), Auto Regressive Integrated Moving Average (ARIMA), and Artificial Neural Network (ANN) to create a hybrid model (SSA-ARIMAANN), which may produce accurate daily rainfall forecasts in a river catchment. Devi et.…”

Section: Original Research Articlementioning

confidence: 99%

Time Series Prediction Using Hybrid ARIMA-ANN Models for Sugarcane

Sanjeev

Bhardwaj²

2022

IJPSS

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Recently Hybrid model approach has led to a tremendous surge in many domains of science and engineering. In this study, we present the advantage of ANN to improve time series forecasting precision. The Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) models are used to separately recognize the linear and nonlinear components in the data set respectively. In this manner, the proposed approach tactically utilizes the unique strengths ARIMA and ANN to improve the forecasting accuracy. Our hybrid method is tested on two Yamunanagar and Panipat sugarcane time series of Haryana. Results clearly indicate that Hybrid ARIMA-ANN model was better perform than ARIMA models with smaller values of RMSE and MAPE for both districts.

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Hybrid SSA-ARIMA-ANN Model for Forecasting Daily Rainfall

Cited by 42 publications

References 18 publications

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Frequency analysis of extreme daily rainfall over an arid zone of Iran using Fourier series method

Time Series Prediction Using Hybrid ARIMA-ANN Models for Sugarcane

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