Multi-time-scale analysis of hydrological drought forecasting using support vector regression (SVR) and artificial neural networks (ANN)

Borji, Moslem; Malekian, Arash; Salajegheh, Ali; Ghadimi, Mehrnoosh

doi:10.1007/s12517-016-2750-x

Cited by 70 publications

(34 citation statements)

References 35 publications

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“…Using similar methods, Fung et al (2019) also discussed the waveletboosting-support vector regression (W-BS-SVR), multiinput wavelet-fuzzy-support vector regression (multi-input W-F-SVR), and weighted wavelet-fuzzy-support vector regression (weighted W-F-SVR) models for meteorological drought predictions downstream of the Langat River basin, with lead times of 1, 3, and 6 months. The similarity between this study and Soh et al's (2018) study is that the hybrid model is used to predict the drought index, and the difference is this study used the SVR model with high longterm prediction accuracy (Borji et al 2016) to replace the ANN model. The purpose of this paper is to propose a hybrid model to improve the application of a single linear or nonlinear model in drought prediction.…”

Section: Introductionmentioning

confidence: 91%

“…To effectively predict nonlinear data, an increasing number of researchers have begun to use artificial neural networks (ANNs) to predict hydrological data in the past decade (Kousari et al 2017;Seibert et al 2017;Marj and Meijerink 2011;Ochoa-Rivera 2008;Sigaroodi et al 2013). Artificial neural networks have been used as drought prediction tools in many studies (Seibert et al 2017;Borji et al 2016;Deo and Ş ahin 2015;Chen et al 2017;Belayneh and Adamowski 2012;Belayneh et al 2016) and achieved good results.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have used SVR in hydrological and drought forecasting (Khan and Coulibaly 2006;Belayneh et al 2016Belayneh et al , 2014Belayneh and Adamowski 2012). Borji et al (2016) used two models including the SVR and ANN to forecast the streamflow drought index (SDI) of different time scales in Latian watershed, Iran, which is one of the most important sources of water for the large metropolitan Tehran. The results showed, that the SVR approach showed a better efficiency in the forecast of long-term droughts compared to the ANN.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China

Zhang

Ding

et al. 2020

Journal of Applied Meteorology and Climatology

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Drought forecasts could effectively reduce the risk of drought. Data-driven models are suitable forecast tools because of their minimal information requirements. The motivation for this study is that because most data-driven models, such as autoregressive integrated moving average (ARIMA) models, can capture linear relationships but cannot capture nonlinear relationships they are insufficient for long-term prediction. The hybrid ARIMA–support vector regression (SVR) model proposed in this paper is based on the advantages of a linear model and a nonlinear model. The multiscale standard precipitation indices (SPI: SPI1, SPI3, SPI6, and SPI12) were forecast and compared using the ARIMA model and the hybrid ARIMA–SVR model. The performance of all models was compared using measures of persistence, such as the coefficient of determination, root-mean-square error, mean absolute error, Nash–Sutcliffe coefficient, and kriging interpolation method in the ArcGIS software. The results show that the prediction accuracies of the multiscale SPI of the combined ARIMA–SVR model and the single ARIMA model were related to the time scale of the index, and they gradually increase with an increase in time scale. The predicted value decreases with increase in lead time. Comparing the measured data with the predicted data from the model shows that the combined ARIMA–SVR model had higher prediction accuracy than the single ARIMA model and that the predicted results 1–2 months ahead show reasonably good agreement with the actual data.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China

Zhang

Ding

et al. 2020

Journal of Applied Meteorology and Climatology

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“…There exist many methodologies for forecasting drought events based on drought indices, such as regression analysis [9,10], stochastic models [7,[11][12][13][14], probability models [15], artificial intelligence (AI)-based models [16][17][18][19], and dynamic modeling [20,21].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Model for Drought Forecasting in the Southern Taiwan Basin

Yeh

Hsu

2019

Water

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The global rainfall pattern has changed because of climate change, leading to numerous natural hazards, such as drought. Because drought events have led to many disasters globally, it is necessary to create an early warning system. Drought forecasting is an important step toward developing such a system. In this study, we utilized the stochastic, autoregressive integrated moving average (ARIMA) model to predict drought conditions based on the standardized precipitation index (SPI) in southern Taiwan. We employed data from 1967 to 2006 to train the model and data from 2007 to 2017 for model validation. The results showed that the coefficients of determination (R2) were over 0.80 at each station, and the root-mean-square error and mean absolute error were sufficiently low, indicating that the ARIMA model is effective and adequate for our stations. Finally, we employed the ARIMA model to forecast future drought conditions from 2019 to 2022. The results yielded relatively low SPI values in southern Taiwan in future summers. In summary, we successfully constructed an ARIMA model to forecast drought. The information in this study can act as a reference for water resource management.

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“…Predicting drought to decrease the damage made by the drought phenomenon is an inevitable issue. The most commonly utilized prediction models are artificial neural network (ANN) Borji et al, (2016),support vector machines (SVMs) (Ahmad et al 2010;Weng 2012). Khan et al (2020)developed wavelet-based hybrid ANN-autoregressive integrated moving average (ARIMA) models for predicting the SPI and the Standard Index of Annual Precipitation (SIAP).…”

Section: Introductionmentioning

confidence: 99%

Applying GRACE satellite data and hydro-meteorological parameters for predicting the combined terrestrial evapotranspiration index (CTEI) based on an improved SVM and a greedy regression approach

Elbeltagi

Jamei²,

Srivastava

et al. 2021

Preprint

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Drought has become relatively one of the widespread extreme events that affect socio-economic sphere, ecological balance, environmental conditions, crop-yields, climate feedback mechanism, and water resources management. Monitoring of the drought is one of the challenging tasks, hence required to develop new techniques in prediction of this natural calamity accurately and timely. In this work, Combined Terrestrial Evapotranspiration Index (CTEI) that is forced through precipitation (P) and potential evapotranspiration (PET) is used for estimating drought. Also, this is a novel index developed for Indus-Ganga river basin, which also uses a Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomalies (TWSA) data. Thirteen input-variables comprised of GRACE-TWSA, Global Land Data Assimilation System GLDAS-TWSA, Groundwater Storage Anomaly (GWSA), P, Land Surface Temperature ( LST), Wind Speed (WS), Evapotranspiration (ET), Runoff, air temperature ( ), PET, net-shortwave (SWN) and long-wave (LWN) along with net radiation (NR) radiations were implemented for predicting CTEI. Further, thirteen different combinations were analyzed using an improved Sequential Minimal Optimization (SMO) algorithm for support vector machine (SVM) and a greedy linear regression method. Therefore, the objectives of this study are to develop several combinations based on machine learning models used for modeling CTEI, compare the accuracy and stability of these models in CTEI prediction, and select the best outcomes have been provided from these models. The performed error measures and statistical indicators prove that Combo 2 and 3 owing to SVM model are superior to the best combinations of Greedy model. Overall based on the results, the improved SVM is superior to Greedy model in estimating CTEI. SVM (Combo2: R=0.81) in spite of having a higher range of relative error, is superior to Greedy model (Combo3, R=0.77) for estimating CTEI. This method can be one of promising alternatives for estimating CTEI over major river basins across the world.

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Multi-time-scale analysis of hydrological drought forecasting using support vector regression (SVR) and artificial neural networks (ANN)

Cited by 70 publications

References 35 publications

Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China

Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China

Stochastic Model for Drought Forecasting in the Southern Taiwan Basin

Applying GRACE satellite data and hydro-meteorological parameters for predicting the combined terrestrial evapotranspiration index (CTEI) based on an improved SVM and a greedy regression approach

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