Comparison of wavelet and empirical mode decomposition hybrid models in drought prediction

Özger, Mehmet; Başakın, Eyyup Ensar; Ekmekçioğlu, Ömer; Hacısüleyman, Volkan

doi:10.1016/j.compag.2020.105851

Cited by 63 publications

(22 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results showed the better performance of ANN and ELM models than the LSSVR and MLR models. Özger et al [53] applied standalone and hybrid use of ANFIS, SVM, and M5 models coupled with empirical mode decomposition (EMD-ANFIS, EMD-SVM, EMD-M5) and wavelet decomposition (i.e., WD-ANFIS, WD-SVM, WD-M5) for self-calibrated Palmer Drought Severity Index (SC-PDSI) prediction in the southern part of Turkey. The obtained results indicated the improved performance of hybrid WD-ANFIS, WD-SVM, and WD-M5 models over the other models.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models

Malik

Kumar

Rai

et al. 2021

Climate

View full text Add to dashboard Cite

Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e., 1-, 3-, 6-, 9-, 12-, and 24-month, by deploying monthly rainfall information of available years. The significant lags as inputs for the MLPNN, CANFIS, and MLR models were obtained by utilizing Partial Autocorrelation Function (PACF) with a significant level equal to 5% for SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The predicted multi-scalar SPI values utilizing the MLPNN, CANFIS, and MLR models were compared with calculated SPI of multi-time scales through different performance evaluation indicators and visual interpretation. The appraisals of results indicated that CANFIS performance was more reliable for drought prediction at Dehradun (3-, 6-, 9-, and 12-month scales), Chamoli and Tehri Garhwal (1-, 3-, 6-, 9-, and 12-month scales), Haridwar and Pauri Garhwal (1-, 3-, 6-, and 9-month scales), Rudraprayag (1-, 3-, and 6-month scales), and Uttarkashi (3-month scale) stations. The MLPNN model was best at Dehradun (1- and 24- month scales), Tehri Garhwal and Chamoli (24-month scale), Haridwar (12- and 24-month scales), Pauri Garhwal (12-month scale), Rudraprayag (9-, 12-, and 24-month), and Uttarkashi (1- and 6-month scales) stations, while the MLR model was found to be optimal at Pauri Garhwal (24-month scale) and Uttarkashi (9-, 12-, and 24-month scales) stations. Furthermore, the modeling approach can foster a straightforward and trustworthy expert intelligent mechanism for projecting multi-scalar SPI and decision making for remedial arrangements to tackle meteorological drought at the stations under study.

show abstract

Section: Discussionmentioning

confidence: 99%

Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models

Malik

Kumar

Rai

et al. 2021

Climate

View full text Add to dashboard Cite

show abstract

“…Empirical mode decomposition (EMD) has apparent advantages in the processing of nonlinear and nonstationary signal time-frequency sequences. Özger et al [24] used EMD for decomposing self-calibrated Palmer drought severity index (sc-PDSI) time series into their sub-bands on drought prediction, but this decomposition method has the problem of mode aliasing. As a further improvement of EMD, ensemble empirical mode decomposition (EEMD) effectively reduces the occurrence of mode aliasing.…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…The best architecture of the SVM model was 3-C11-1 and 3-C13-1 respectively, for DTI and STI prediction. The novel MLP-GA hybrid model was found the best architecture (4-32-1) at population size (30), generation (100), crossover (0.9), and mutation (0.001) for prediction of DTI in maize while the best architecture (4-51-1) at population size (32), generation (100), crossover (0.9) and mutation (0.001) for prediction of STI in maize. Correspondingly, the best architecture of SVM-GA models was 3-C31-1 and 3-C18-1 respectively, to simulate DTI and STI of maize.…”

Section: Artificial Intelligence Techniquesmentioning

confidence: 99%

“…The results suggested that the performance of MLP and remote sensing were satisfactory for the estimation of coffee tree volume. Mehmet et al [30] used the SVM and MLP model for the prediction of drought and also compared the potential of empirical decomposition and the different wavelet networks. Sareh et al [31] studied the five different AI and Hybrid AI algorithms to predict water infiltration.…”

Section: Introductionmentioning

confidence: 99%

Developing a Novel Hybrid Model for Prediction of Drought and Stress-Tolerant Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques

Singh¹,

Kumar²,

Saran³

et al. 2022

Preprint

View full text Add to dashboard Cite

Maize (Zea mays subsp. mays) is the staple food crop in the world. In this study, multi-layer perceptron (MLP), support vector machine (SVM), genetic algorithm-based multi-layer perceptron (MLP-GA), and genetic algorithm-based support vector machine (SVM-GA) hybrid artificial intelligence algorithms were used for the prediction of drought tolerance and stress tolerance indices in teosinte maize lines. Correspondingly, the gamma test technique was applied to determine efficient input and output vectors. The potential of developed models was evaluated based on statistical indices and graphical representation. Results of gamma test based on the least value of gamma and standard error indices show that day of anthesis (DOA), day of silking (DOS), yield index (YI), and gross yield per plant (GYP) information vector arrangements were determined as efficient information vector combination for drought-tolerant index (DTI) as well as the stress-tolerant index (STI). The results of MLP, SVM, MLP-GA, and SVM-GA algorithms were compared based on statistical indices and visual interpretation that have satisfactory for prediction of the drought-tolerant index and stress-tolerant index in maize crop. It has also seemed that genetic algorithm-based hybrid models (MLP-GA and SVM-GA) were found a better prediction of the drought-tolerant index and stress-tolerant index in maize crop. Similarly, the SVM-GA model has the highest potential to forecast the DTI and STI in maize crops as compared to MLP, SVM, MLP-GA models.

show abstract

Comparison of wavelet and empirical mode decomposition hybrid models in drought prediction

Cited by 63 publications

References 27 publications

Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models

Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models

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

Developing a Novel Hybrid Model for Prediction of Drought and Stress-Tolerant Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques

Contact Info

Product

Resources

About