Drought prediction using hybrid soft-computing methods for semi-arid region

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

doi:10.1007/s40808-020-01010-6

Cited by 40 publications

(17 citation statements)

References 20 publications

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“…Besides, a few studies employed all the data pre-processing steps. No Yes Yes Khan et al [31] No Yes No Pham et al [35] Yes Yes Yes Banadkooki et al [38] Yes No Yes Adnan et al [32] Yes No No Nabipour et al [20] Yes No No Mohamadi et al [68] Yes No Yes Djerbouai and Souag-Gamane [37] Yes Yes No Wu et al [40] Yes Yes No Soh et al [52] Yes Yes No Başakın et al [27] Yes Yes No Das et al [62] Yes Yes No Belayneh et al [23] Yes Yes No Kisi et al [39] Yes No Yes…”

Section: A Selecting Appropriate Descriptorsmentioning

confidence: 99%

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Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-Processing

et al. 2022

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Drought is a prolonged period of low precipitation that negatively impacts agriculture, animals, and people. Over the last decades, gradual changes in drought indices have been observed. Therefore, understanding and forecasting drought is essential to avoid its economic impacts and appropriate water resource planning and management. This paper presents a recent literature review, including a brief description of data pre-processing, data-driven modelling strategies (i.e., univariate or multivariate), machine learning algorithms (i.e., advantages and disadvantages), hybrid models, and performance metrics. Combining various prediction methods to create efficient hybrid models has become the most popular use in recent years. Accordingly, hybrid models have been increasingly used for predicting drought. As such, these models will be extensively reviewed, including preprocessing-based hybrid models, parameter optimisation-based hybrid models, and hybridisation of components combination-based with preprocessing-based hybrid models. In addition, using statistical criteria, such as RMSE, MAE, NSE, MPE, SI, BIC, AIC, and AAD, is essential to evaluate the performance of the models.

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Section: A Selecting Appropriate Descriptorsmentioning

confidence: 99%

“…Moreover, various studies have indicated that AI algorithms outperform traditional methods [22,23]. These AI algorithms are, for example, artificial neural networks (ANNs) [24], support vector machines (SVMs) [25], random forests [26], and the adaptive neuro-fuzzy inference system (ANFIS) [27].…”

Section: Introductionmentioning

confidence: 99%

Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-Processing

et al. 2022

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“…The drought prediction model EMD-ANFIS was constructed by combining empirical mode decomposition (EMD) and adaptive neuro-fuzzy inference system (ANFIS) models. When the prediction step length was 3 and 6 months, the E ns of ANFIS was 0.52 and 0.17, respectively, whereas that of EMD-ANFIS was 0.81 and 0.77, respectively [ 16 ]. However, the EMD methods often have issues with modal aliasing [ 17 ] and end effects [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Drought prediction based on an improved VMD-OS-QR-ELM model

Liu

Wang

et al. 2022

PLoS ONE

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To overcome the low accuracy, poor reliability, and delay in the current drought prediction models, we propose a new extreme learning machine (ELM) based on an improved variational mode decomposition (VMD). The model first redefines the output of the hidden layer of the ELM model with orthogonal triangular matrix decomposition (QR) to construct an orthogonal triangular ELM (QR-ELM), and then introduces an online sequence learning mechanism (OS) into the QR-ELM to construct an online sequence OR-ELM (OS-QR-ELM), which effectively improves the efficiency of the ELM model. The mutual information extension method was then used to extend both ends of the original signal to improve the VMD end effect. Finally, VMD and OS-QR-ELM were combined to construct a drought prediction method based on the VMD-OS-QR-ELM. The reliability and accuracy of the VMD-OS-QR-ELM model were improved by 86.19% and 93.20%, respectively, compared with those of the support vector regression model combined with empirical mode decomposition. Furthermore, the calculation efficiency of the OS-QR-ELM model was increased by 88.65% and 85.32% compared with that of the ELM and QR-ELM models, respectively.

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“…Therefore, considering the complexity of the drought episode and some weaknesses of the dynamic models, researchers have developed and successfully applied different stochastic and soft computing models. In particular, soft computing models are gaining high interest and are being applied to solve different water resource management problems, including drought prediction [20][21][22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

“…Başakın et al [22] predicted meteorological drought in a semi-arid region using empirical and soft computing models. Their findings indicated that a hybridized approach of ANFIS with empirical mode decomposition (EMD) shows better results than the empirical and standalone ANFIS model.…”

Section: Introductionmentioning

confidence: 99%

Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods

Adnan

Mostafa

Islam

et al. 2021

Water

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Drought modeling is essential in water resources planning and management in mitigating its effects, especially in arid regions. Climate change highly influences the frequency and intensity of droughts. In this study, new hybrid methods, the random vector functional link (RVFL) integrated with particle swarm optimization (PSO), the genetic algorithm (GA), the grey wolf optimization (GWO), the social spider optimization (SSO), the salp swarm algorithm (SSA) and the hunger games search algorithm (HGS) were used to forecast droughts based on the standard precipitation index (SPI). Monthly precipitation data from three stations in Bangladesh were used in the applications. The accuracy of the methods was compared by forecasting four SPI indices, SPI3, SPI6, SPI9, and SPI12, using the root mean square errors (RMSE), the mean absolute error (MAE), the Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2). The HGS algorithm provided a better performance than the alternative algorithms, and it considerably improved the accuracy of the RVFL method in drought forecasting; the improvement in RMSE for the SPI3, SP6, SPI9, and SPI12 was by 6.14%, 11.89%, 14.14%, 24.5% in station 1, by 6.02%, 17.42%, 13.49%, 24.86% in station 2 and by 7.55%, 26.45%, 15.27%, 13.21% in station 3, respectively. The outcomes of the study recommend the use of a HGS-based RVFL in drought modeling.

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Drought prediction using hybrid soft-computing methods for semi-arid region

Cited by 40 publications

References 20 publications

Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-Processing

Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-Processing

Drought prediction based on an improved VMD-OS-QR-ELM model

Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods

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