Drought forecasting using novel heuristic methods in a semi-arid environment

Kisi, Özgür; Gorgij, Alireza Docheshmeh; Zounemat‐Kermani, Mohammad; Mahdavi‐Meymand, Amin; Kim, Sungwon

doi:10.1016/j.jhydrol.2019.124053

Cited by 102 publications

(36 citation statements)

References 58 publications

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“…Kisi et al. and Zounemat‐Kermani and Mahdavi‐Meymand demonstrated that the ANFIS‐PSO results were better than those of ANFIS‐GA and other hybrid methods . Accordingly, PSO is a proper potential candidate as a fundamental nature‐inspired algorithm to compare the performance of more recent optimization algorithms.…”

Section: Methodsmentioning

confidence: 99%

Seasonal Short‐Term Prediction of Dissolved Oxygen in Rivers via Nature‐Inspired Algorithms

Fadaee

Mahdavi‐Meymand

Zounemat‐Kermani

2020

CLEAN Soil Air Water

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This study challenges the use of three nature-inspired algorithms as learning frameworks of the adaptive-neuro-fuzzy inference system (ANFIS) machine learning model for short-term modeling of dissolved oxygen (DO) concentrations. Particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and biogeography-based optimization (BBO) are employed for developing predictive ANFIS models using seasonal 15 min data collected from the Rock Creek River in Washington, DC. Four independent variables are used as model inputs including water temperature (T), river discharge (Q), specific conductance (SC), and pH. The Mallow's Cp and R 2 parameters are used for choosing the best input parameters for the models. The models are assessed by several statistics such as the coefficient of determination (R 2 ), root-mean-square error (RMSE), Nash-Sutcliffe efficiency, mean absolute error, and the percent bias. The results indicate that the performance of all-nature-inspired algorithms is close to each other. However, based on the calculated RMSE, they enhance the accuracy of standard ANFIS in the spring, summer, fall, and winter around 13.79%, 15.94%, 6.25%, and 12.74%, respectively. Overall, the ANFIS-PSO and ANFIS-BOA provide slightly better results than the other ANFIS models.

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Section: Methodsmentioning

confidence: 99%

Seasonal Short‐Term Prediction of Dissolved Oxygen in Rivers via Nature‐Inspired Algorithms

Fadaee

Mahdavi‐Meymand

Zounemat‐Kermani

2020

CLEAN Soil Air Water

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“…Results reveal the superior multi-scales SPEI was forecasted by the W-WF-SVR model. Kisi et al [58] examined the potential of hybrid ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm), ANFIS-ACO (ant colony optimizer), ANFIS-BOA (butterfly optimization algorithm) against classical ANFIS to forecast the meteorological drought at three synoptic stations located in Iran, based on multi-scalar SPI. They fund the superior performance of hybrid ANFIS models for forecasting SPI 3, SPI 6, SPI 9, and SPI 12 at study stations.…”

Section: Plos Onementioning

confidence: 99%

Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India

et al. 2020

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A new version of the fuzzy logic model, called the co-active neuro fuzzy inference system (CANFIS), is introduced for predicting standardized precipitation index (SPI). Multiple scales of drought information at six meteorological stations located in Uttarakhand State, India, are used. Different lead times of SPI were computed for prediction, including 1, 3, 6, 9, 12, and 24 months, with inputs abstracted by autocorrelation function (ACF) and partial-ACF (PACF) analysis at 5% significance level. The proposed CANFIS model was validated against two models: classical artificial intelligence model (e.g., multilayer perceptron neural network (MLPNN)) and regression model (e.g., multiple linear regression (MLR)). Several performance evaluation metrices (root mean square error, Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index), and graphical visualizations (scatter plot and Taylor diagram) were computed for the evaluation of model performance. Results indicated that the CANFIS model predicted the SPI better than the other models and prediction results were different for different meteorological stations. The proposed model can build a reliable expert intelligent system for predicting meteorological drought at multi-time scales and decision making for remedial schemes to cope with meteorological drought at the study stations and can help to maintain sustainable water resources management.

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“…To some extent, BOA algorithm has been applied as an excellent algorithm in many fields. It includes not only the feature selection (Anand & Arora, 2020; Arora, Sharma, & Anand, 2020; Liu & Wang, 2019; Sankalap & Priyanka, 2018; Shang, Tan, Gao, & Feng, 2019), but also the engineering design problem and optimization, such as optimization of control system parameters (Lal, Barisal, & Madasu, 2019), shape design of automobile suspension components (Yildiz et al, 2020), constrained engineering problems (Kahraman & Aras, 2020), distinct problems (Mortazavi, 2019), CCHP (Zhi, Weiqing, Haiyun, & Khodaei, 2019), weather forecast (Kisi, Gorgij, Zounemat‐Kermani, Mandavi‐Meymand, & Kim, 2019).…”

Section: Modified Butterfly Optimization Algorithmmentioning

confidence: 99%

A modified butterfly optimization algorithm: An adaptive algorithm for global optimization and the support vector machine

Jiang

et al. 2020

Expert Systems

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A modified adaptive butterfly optimization algorithm is established with the aim of addressing the “early search blindness” and the relatively poor adaptability of the sensory modality. A normal‐distribution‐based model and a Weibull‐distribution‐based adaptive model of sensory modalities are respectively proposed for the global search process and iteration process. Among them, the Weibull‐distribution‐based adaptive model of sensory modalities is mainly manifested as the c value, that is, the adaptive change trend based on the Weibull model. The performance of the modified butterfly optimization algorithm is validated using a 14‐benchmark test function and compared with performances of some latest algorithms. The experimental results indicate that the modified algorithm performs competitively in terms of accuracy and stability. Following the experiment, the modified algorithm is further tested by running a support‐vector‐machine prediction model based on engineering data of a pipe belt conveyor's flat‐pipe/pipe‐flat transition segment. The results of the modified algorithm are then compared with test‐run outcomes of the back‐propagation prediction model and KCV‐SVM model. The results show that the prediction error is well within 10%, demonstrating the method's competence as a reliable reference for future designs of pipe belt conveyors.

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Drought forecasting using novel heuristic methods in a semi-arid environment

Cited by 102 publications

References 58 publications

Seasonal Short‐Term Prediction of Dissolved Oxygen in Rivers via Nature‐Inspired Algorithms

Seasonal Short‐Term Prediction of Dissolved Oxygen in Rivers via Nature‐Inspired Algorithms

Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India

A modified butterfly optimization algorithm: An adaptive algorithm for global optimization and the support vector machine

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