Monthly evapotranspiration estimation using optimal climatic parameters: efficacy of hybrid support vector regression integrated with whale optimization algorithm

Tikhamarine, Yazid; Malik, Anurag; Pandey, Kusum; Sammen, Saad Sh.; Souag-Gamane, Doudja; Heddam, Salim; Kişi, Özgür

doi:10.1007/s10661-020-08659-7

Cited by 57 publications

(21 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, the statistical indices were used to assess the error and association between observed and modelled runoff. Many researchers gathered a list of performance indicators that are most commonly used in hydrologic modeling (Ehteram et al, 2018;Torabi Haghighi et al, 2019;Pham et al 2021;Sammen et al 2020;Mohamadi, S. et al 2020;Tikhamarine et al 2020). Some of those indicators including Mean Absolute Error (MAE), Percentage Bias (%BIAS), Root Mean Square Error (RMSE), Regression Coe cient (R2), Pearson Correlation Coe cient (PCC), Nash-Sutcliffe Coe cient of E ciency (NSE), Flow Duration Error Index (EI), Index of Agreement de ned by (IA) and the ratio of RMSE (RSE) are used in this study.…”

Section: Model Assessmentmentioning

confidence: 99%

Assessment of climate change impact on probable maximum floods in a tropical catchment

Sammen

Mohammed

Ghazali

et al. 2022

Theor Appl Climatol

Self Cite

View full text Add to dashboard Cite

Increased extreme rainfall due to climate change will increase the probable maximum ood (PMF) and pose a severe threat the critical hydraulic infrastructure like hydroelectric and ood protection dams. As the rainfall extremes in tropical regions are highly sensitive to global warming, increase PMF can be much higher in the tropics. A study has been conducted to assess the impact of climate change on PMF in a tropical catchment located in peninsular Malaysia. A lumped hydrological model, Mike NAM, is calibrated and validated with observed climate and in ow data of Tenmengor reservoir, located in the state of Perak of Peninsular Malaysia. Regional climate model projected rainfall is used to generate probable maximum precipitation (PMP) for future periods. The hydrological model is used to simulate PMF from PMP estimated for the historical and two future periods, early (2031−2045) and late (2060−2075). The results revealed the NAM model could simulate the river ow with a Nash-Sutcliffe e ciency of 0.74 and root mean square error of 0.51. The application of the model with projected rainfall revealed an increase in PMP by 162 to 507% and 259 to 487% during early and late periods for different return periods ranging from 5 to 1000 years. This would cause an increase in PMF by 48.9% and 122.6% during early and late periods. A large increase in PMF indicates the possibility of devastating oods in the study area due to climate change.

show abstract

Section: Model Assessmentmentioning

confidence: 99%

Assessment of climate change impact on probable maximum floods in a tropical catchment

Sammen

Mohammed

Ghazali

et al. 2022

Theor Appl Climatol

Self Cite

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…The statistical performance indicators are mean absolute error (MAE), root mean square error (RMSE), index of scattering (IOS), coefficient of determination (R 2 ), Pearson correlation coefficient (R), and index of agreement (IOA). They are expressed as follows [54,55]:…”

Section: Statistical Performance Indicatorsmentioning

confidence: 99%

Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques

et al. 2021

View full text Add to dashboard Cite

Estimating the bearing capacity of piles is an essential point when seeking for safe and economic geotechnical structures. However, the traditional methods employed in this estimation are time-consuming and costly. The current study aims at elaborating a new alternative model for predicting the pile-bearing capacity based on eleven new advanced machine-learning methods in order to overcome these limitations. The modeling phase used a database of 100 samples collected from different countries. Additionally, eight relevant factors were selected in the input layer based on the literature recommendations. The optimal inputs were modeled using the machine-learning methods and their performance was assessed through six performance measures using a K-fold cross-validation approach. The comparative study proved the effectiveness of the DNN model, which displayed a higher performance in predicting the pile-bearing capacity. This elaborated model provided the optimal prediction, i.e., the closest to the experimental values, compared to the other models and formulae proposed by previous studies. Finally, a reliable and easy-to-use graphical interface was generated, namely “BeaCa2021”. This will be very helpful for researchers and civil engineers when estimating the pile-bearing capacity, with the advantage of saving time and money.

show abstract

Monthly evapotranspiration estimation using optimal climatic parameters: efficacy of hybrid support vector regression integrated with whale optimization algorithm

Cited by 57 publications

References 70 publications

Assessment of climate change impact on probable maximum floods in a tropical catchment

Assessment of climate change impact on probable maximum floods in a tropical catchment

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

Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques

Contact Info

Product

Resources

About