Prediction of meteorological drought by using hybrid support vector regression optimized with HHO versus PSO algorithms

Malik, Anurag; Tikhamarine, Yazid; Sammen, Saad Sh.; Abba, S.I.; Shahid, Shamsuddin

doi:10.1007/s11356-021-13445-0

Cited by 69 publications

(20 citation statements)

References 108 publications

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“…Model performance was evaluated using R 2 (coefficient of determination), root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (COC) and Willmott index (WI) values of the testing sets. The R 2 (−∞ < R 2 < 1), RMSE (0 < RMSE < ∞), MAE (0 < MAE < ∞), COC (−1 < COC < 1) and WI (0 < WI ≤ 1) can be expressed as: R 2

R^{2} = 1 ‐ (][, \frac{\sum_{i = 1}^{N} {()(, y_{obs, i} - y_{s i m, i})}^{2}}{\sum_{i = 1}^{N} {()(, y_{obs, i} - {\overset{true¯}{y}}_{obs})}^{2}})

RMSE (Malik, Tikhamarine, Al‐Ansari, et al, 2021; Malik, Tikhamarine, Sammen, et al, 2021; Malik, Tikhamarine, Souag‐Gamane, et al, 2021)

RMSE goodbreak= \sqrt{\frac{1}{N} \sum_{i = 1}^{N} {(y_{obs, i} - y_{sim, i})}^{2}}

MAE (Malik, Tikhamarine, Al‐Ansari, et al, 2021; Malik, Tikhamarine, Sammen, et al, 2021; Malik, Tikhamarine, Souag‐Gamane, et al, 2021)

MAE goodbreak= \frac{1}{N} \sum_{i = 1}^{N} (||, y_{sim, i} goodbreak- y_{obs, i})

COC (Malik, Tikhamarine, Sammen, et al, 2021; Malik, Tikhamarine, Souag‐Gamane, et al, 2021)

COC goodbreak= \frac{\sum_{i = 1}^{N} ()(, y_{obs, i} goodbreak- {\overset{true¯}{y}}_{obs}) ()(, y_{sim, i} goodbreak- {\overset{true¯}{y}}_{sim})}{\sqrt{\sum_{i = 1}^{N}}}

…”

Section: Methodsmentioning

confidence: 99%

Simulation of site‐scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

Xie

Luo

Hellwich

et al. 2021

Hydrological Processes

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The study of water fluxes is important to better understand hydrological cycles in arid regions. Data‐driven machine learning models have been recently applied to water flux simulation. Previous studies have built site‐scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter‐annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R2 = 0.25–0.90) was slightly worse than that of the RF algorithm (R2 = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R2 = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R2 = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non‐growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision‐making basis to the water management in desert and oasis ecosystems.

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R^{2} = 1 ‐ (][, \frac{\sum_{i = 1}^{N} {()(, y_{obs, i} - y_{s i m, i})}^{2}}{\sum_{i = 1}^{N} {()(, y_{obs, i} - {\overset{true¯}{y}}_{obs})}^{2}})

RMSE (Malik, Tikhamarine, Al‐Ansari, et al, 2021; Malik, Tikhamarine, Sammen, et al, 2021; Malik, Tikhamarine, Souag‐Gamane, et al, 2021)

RMSE goodbreak= \sqrt{\frac{1}{N} \sum_{i = 1}^{N} {(y_{obs, i} - y_{sim, i})}^{2}}

MAE (Malik, Tikhamarine, Al‐Ansari, et al, 2021; Malik, Tikhamarine, Sammen, et al, 2021; Malik, Tikhamarine, Souag‐Gamane, et al, 2021)

MAE goodbreak= \frac{1}{N} \sum_{i = 1}^{N} (||, y_{sim, i} goodbreak- y_{obs, i})

COC (Malik, Tikhamarine, Sammen, et al, 2021; Malik, Tikhamarine, Souag‐Gamane, et al, 2021)

COC goodbreak= \frac{\sum_{i = 1}^{N} ()(, y_{obs, i} goodbreak- {\overset{true¯}{y}}_{obs}) ()(, y_{sim, i} goodbreak- {\overset{true¯}{y}}_{sim})}{\sqrt{\sum_{i = 1}^{N}}}

…”

Section: Methodsmentioning

confidence: 99%

Simulation of site‐scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

Xie

Luo

Hellwich

et al. 2021

Hydrological Processes

View full text Add to dashboard Cite

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“…The range of MAE is 0 to ∞. The formula for MAE (Tikhamarine et al 2020a;Malik et al 2021b;Mohammadi et al 2021) is given by:…”

Section: Performance Evaluation Indicatorsmentioning

confidence: 99%

Prediction of aeration efficiency of Parshall and Modified Venturi flumes: application of soft computing versus regression models

et al. 2021

Self Cite

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In this study, the potential of soft computing techniques namely Random Forest (RF), M5P, Multivariate Adaptive Regression Splines (MARS), and Group Method of Data Handling (GMDH) was evaluated to predict the aeration efficiency (AE20) at Parshall and Modified Venturi flumes. Experiments were conducted for 26 various Modified Venturi flumes and one Parshall flume. A total of 99 observations were obtained from experiments. The results of soft computing models were compared with regression-based models (i.e., MLR: multiple linear regression, and MNLR: multiple nonlinear regression). Results of the analysis revealed that the MARS model outperformed other soft computing and regression-based models for predicting the AE20 at Parshall and Modified Venturi flumes with Pearson's correlation coefficient (CC) = 0.9997, and 0.9992, and root mean square error (RMSE) = 0.0015, and 0.0045 during calibration and validation periods. Sensitivity analysis was also carried out by using the best executing MARS model to assess the effect of individual input variables on AE20 of both flumes. Obtained results on sensitivity examination indicate that the oxygen deficit ratio (r) was the most effective input variable in predicting the AE20 at Parshall and Modified Venturi flumes.

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“…Taylor diagram, radar-chart, temporal plots). The MAE (Elbeltagi et al, 2020;Rehamnia et al, 2021), RMSE (Abba et al, 2021;Malik et al, 2021a;Pandey et al, 2020), IOS (Malik et al, 2019;Tao et al, 2018), NSE (Nash & Sutcliffe, 1970), PCC (Malik et al, 2020b(Malik et al, , 2021b, and IOA (Tikhamarine et al, 2020;Willmott, 1981) are stated as:…”

Section: Integrated Hybrid Svr Models and Goodness-of-fit Measuresmentioning

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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Prediction of meteorological drought by using hybrid support vector regression optimized with HHO versus PSO algorithms

Cited by 69 publications

References 108 publications

Simulation of site‐scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

Simulation of site‐scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

Prediction of aeration efficiency of Parshall and Modified Venturi flumes: application of soft computing versus regression models

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

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