Evaporation process modelling over northern Iran: application of an integrative data-intelligence model with the krill herd optimization algorithm

Ashrafzadeh, Afshin; Ghorbani, Mohammad Ali; Biazar, Seyed Mostafa; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1080/02626667.2019.1676428

Cited by 45 publications

(21 citation statements)

References 71 publications

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“…In these figures, the observed EP is characterized by a red-filled circle (x-axis). Generally, three statistics including correlation coefficient, standard deviation, and RMSE are comprised in the polar system for truthful evaluation of the comparative performance of different models (Ashrafzadeh et al, 2019;Taylor, 2001). The Taylor diagrams show the very close efficacy of hybrid SVR models on Hisar (Figure 8a).…”

Section: Daily Ep Estimation In Two Different Agro-climatic Zonesmentioning

confidence: 99%

“…Considering the limitation of both the methods, the machine learning (ML) technique has been used in recent years as an alternative, such as SVR (support vector regression), MARS (multivariate adaptive regression splines), M5 T (M5Tree), ELM (extreme learning machine), RF (random forest), MLP (multi-layer perceptron), GEP (gene expression programming), & ANFIS (adaptive neuro-fuzzy inference system). Besides, their hybrids with numerous algorithms enthused from nature have been effectively employed in pan-evaporation modeling (Ashrafzadeh et al, 2019;Guan et al, 2020;Seifi & Soroush, 2020;Shabani et al, 2020;Wu et al, 2020;Yaseen et al, 2020a). Ghorbani et al (2018) evaluated hybrid MLP-QPSO (quantum-behaved particle swarm optimization algorithm) against the hybrid MLP-PSO and simple MLP to forecast the daily EP rate at Talesh station of Iran.…”

Section: Introductionmentioning

confidence: 99%

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Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

Malik

Tikhamarine

Al‐Ansari

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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Section: Daily Ep Estimation In Two Different Agro-climatic Zonesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

Malik

Tikhamarine

Al‐Ansari

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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show abstract

“…The 122 articles published related to KH show its application in many areas, which can be classified into continuous optimization, combinatorial optimization, constrained optimization, multi-objective optimization, dynamic, and noisy environment engineering, and fuzzy systems. Table 1 gives a summary of these application areas and the number of publications in each [9,[57][58][59][60][61][62][63][64][65][66][67][68][69][70]. The clustered column chart, presented in Figure 3, visually summarizes and compares the number of publications across the various application areas identified in Table 1.…”

Section: B Application Areas Of Khmentioning

confidence: 99%

Influence of Initializing Krill Herd Algorithm With Low-Discrepancy Sequences

Agushaka

Ezugwu

2020

IEEE Access

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The krill herd (KH) algorithm is a global metaheuristic algorithm that was initially proposed for solving continuous optimization problems. The KH algorithm, since inception, has generated considerable real-world application interests in the research community. The standard algorithm solution implementation steps follow the initialization mechanism, which relies mainly on educated guesses or random initialization solution generation. Therefore, to improve the performance of the KH algorithm, the current study is set to investigate the influence of initializing the KH algorithm with three low-discrepancy sequences, such as the Faure sequence, Sobol sequence, and Van der Corput sequence. These low-discrepancy sequences are known to be more uniformly distributed across the problem search space than the commonly used random number initialization method. The study also evaluates the influence of population size on the performance of the proposed variants of the improved KH algorithms. The experimental results show significant improvements for the enhanced KH algorithms in terms of performance and the quality of solutions obtained; particularly on standard benchmarked high-dimension test problem instances, where the enhanced KH variants outperformed the existing basic KH algorithm for all the test functions evaluated. Similarly, the results for low dimension test cases showed less sensitivity to the initialization schemes, as the performance of our proposed improved scheme was comparable to that of the basic KH algorithm. However, in most cases, as the problem dimension was scaled up, the enhanced KH outperformed the basic KH. Evaluation results based on the population size of the algorithm, revealed that when the number of Krill is set at 25, the Sobol based KH initialization scheme performed better than did the other methods. Although, the Van der Corput and Faure based KH initialization schemes showed similar sensitivity when the dimension was set at 20. As we varied the population size of Krill, it was observed that the performance of the Sobol based KH initialization scheme deteriorated, whereas the other two methods showed superior performance. Overall, the findings from this study revealed that there are significant improvements in the performance of KH algorithm when initialized with low-discrepancy sequences.

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“…In fact, the AI models can predict the drought events that do not have a good and straightforward mathematical solution and were proven to have the ability to capture the white noise, nonstationary, and nonlinearity in the time series [18]. Multilayer Perceptron (MLP) neural network is the most famous type of AIs which has been widely used in hydrological and meteorological modeling studies [19][20][21][22][23][24][25][26][27][28][29][30]. Malik and Kumar [31] used the MLP model for meteorological drought prediction based on Effective Drought Index (EDI) in the Uttarakhand state of India and reported the acceptable performance of this model.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Different Types of Droughts Simultaneously Using Multivariate Standardized Precipitation Index (MSPI), MLP Neural Network, and Imperialistic Competitive Algorithm (ICA)

Aghelpour

Varshavian

2021

Complexity

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Precipitation deficit causes meteorological drought, and its continuation appears as other different types of droughts including hydrological, agricultural, economic, and social droughts. Multivariate Standardized Precipitation Index (MSPI) can show the drought status from the perspective of different drought types simultaneously. Forecasting multivariate droughts can provide good information about the future status of a region and will be applicable for the planners of different water divisions. In this study, the MLP model and its hybrid form with the Imperialistic Competitive Algorithm (MLP-ICA) have been investigated for the first time in multivariate drought studies. For this purpose, two semi-arid stations of western Iran were selected, and their precipitation data were provided from the Iranian Meteorological Organization (IRIMO), during the period of 1988–2017. MSPI was calculated in 5-time windows of the multivariate drought, including MSPI3–6 (drought in perspectives of soil moisture and surface hydrology simultaneously), MSPI6–12 (hydrological and agricultural droughts simultaneously), MSPI3–12 (soil moisture, surface hydrology, and agricultural droughts simultaneously), MSPI12–24 (drought in perspectives of agriculture and groundwater simultaneously), and MSPI24–48 (socio-economical droughts). The results showed acceptable performances in forecasting multivariate droughts. In both stations, the larger time windows (MSPI12–24 and MSPI24–48) had better predictions than the smaller ones (MSPI3–6, MSPI6–12, and MSPI3–12). Generally, it can be reported that, by decreasing the size of the time window, the gradual changes of the index give way to sudden jumps. This causes weaker autocorrelation and consequently weaker predictions, e.g., forecasting droughts from the perspective of soil moisture and surface hydrology simultaneously (MSPI3–6). The hybrid MLP-ICA shows stronger prediction results than the simple MLP model in all comparisons. The ICA optimizer could averagely improve MLP’s accuracy by 28.5%, which is a significant improvement. According to the evaluations (RMSE = 0.20; MAE = 0.15; R = 0.95), the results are hopeful for simultaneous forecasting of different drought types and can be tested for other similar areas.

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Evaporation process modelling over northern Iran: application of an integrative data-intelligence model with the krill herd optimization algorithm

Cited by 45 publications

References 71 publications

Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

Influence of Initializing Krill Herd Algorithm With Low-Discrepancy Sequences

Forecasting Different Types of Droughts Simultaneously Using Multivariate Standardized Precipitation Index (MSPI), MLP Neural Network, and Imperialistic Competitive Algorithm (ICA)

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