Accuracy enhancement for monthly evaporation predicting model utilizing evolutionary machine learning methods

Mohamadi, Sedigheh; Ehteram, Mohammad; El-Shafie, Ahmed

doi:10.1007/s13762-019-02619-6

Cited by 25 publications

(11 citation statements)

References 33 publications

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“…Because of the small data sets used, researchers often perform feature selection/reduction to avoid overfitting. Most often, the selected features in the literature are combinations of features derived from previous time steps in the data, for example, a parameter at month n may be predicted based one or more parameter values taken from months previous to n [ [25][26][27]46,63].…”

Section: Literature Review and Scope Of The Researchmentioning

confidence: 99%

“…Some papers use MLR based on previous lags [13,16,17,25], while the authors in [45] used same-month averages. Some papers do not use simple baselines, but rather compare several variations or architectures of more advanced ML methods such as SVR or MLP [26,27,46,63]. In summary, simple baselines are not consistently used in the literature.…”

Section: Literature Review and Scope Of The Researchmentioning

confidence: 99%

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Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

et al. 2021

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Machine learning (ML) has been utilized to predict climatic parameters, and many successes have been reported in the literature. In this paper, we scrutinize the effectiveness of five widely used ML algorithms in the monthly prediction of seasonal climatic parameters using monthly image data. Specifically, we quantify the predictive performance of these algorithms applied to five climatic parameters using various combinations of features. We compare the predictive accuracy of the resulting trained ML models to that of basic statistical estimators that are computed directly from the training data. Our results show that ML never significantly outperforms the statistical baseline, and underperforms for most feature sets. Unlike previous similar studies, we provide error bars for the relative performance of different predictors based on jackknife estimates applied to differences in predictive error magnitudes. We also show that the practice of shuffling data sequences which was employed in some previous references leads to data leakage, resulting in over-estimated performance. Ultimately, the paper demonstrates the importance of using well-grounded statistical techniques when producing and analyzing the results of ML predictive models.

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Section: Literature Review and Scope Of The Researchmentioning

confidence: 99%

Section: Literature Review and Scope Of The Researchmentioning

confidence: 99%

Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

et al. 2021

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“…It is necessary to consider two technical aspects in order to integrate the optimization algorithms with MLP, namely, the method for encoding the agents/solutions and the procedure for determining the objective function. Although the standalone MLP models have high ability, their training algorithms may have slow convergence or may trap in local optimums [21][22][23][24][25][26][27][28][29][30][31][32][33][34]. erefore, it is essential to improve the accuracy of the MLP models.…”

Section: Optimization Algorithms For Training Mlpsmentioning

confidence: 99%

“…Optimization algorithms are considered as suitable alternatives for traditional training algorithms due to their advanced operators, which avoid trapping in local optimums. e optimization algorithms are widely used for training soft computing models [19][20][21][22][23][24][25][26]. e genetic algorithm (GA) and particle swarm optimizations (PSO) are powerful optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Daily and Monthly Reference Evapotranspiration in the Aidoghmoush Basin Using Multilayer Perceptron Coupled with Water Wave Optimization

2021

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The aim of this study is to evaluate the ability of soft computing models including multilayer perceptron- (MLP-) water wave optimization (MLP-WWO), MLP-particle swarm optimization (MLP-PSO), and MLP-genetic algorithm (MLP-GA), to simulate the daily and monthly reference evapotranspiration (ET) at the Aidoghmoush basin (Iran). Principal component analysis (PCA) was used to find the best input combination including the lagged ETs. According to the results, the ET values with 1, 2, and 3 (days) lags as well as those with 1, 2, and 3 (months) lags were the most effective variables in the formation of the PCs. The total variance proportion of inputs and eigenvalues was used to identify the most important variables. The accuracy of the models was assessed based on multiple statistical indices such as the mean absolute error (MAE), Nash–Sutcliff efficiency (NSE), and percent bias (PBIAS). The results showed that the performance of hybrid MLP models was better than that of the standalone MLP. The findings confirmed that the MLP-WWO could precisely predict ET.

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“…In comparison to conventional NNs and linear regression models, the applied quantile regression forest provided better results. Mohammadi et al [31] predicted monthly EP using integrative ANFIS, MLP and RBNN models for two stations in Iran. The results showed that the integrative ANFIS model acted better compared to the MLP and RBNN.…”

Section: Introductionmentioning

confidence: 99%

Towards a Comprehensive Assessment of Statistical versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction

Zounemat‐Kermani

Keshtegar

Kisi

et al. 2021

Water

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This paper evaluates six soft computational models along with three statistical data-driven models for the prediction of pan evaporation (EP). Accordingly, improved kriging—as a novel statistical model—is proposed for accurate predictions of EP for two meteorological stations in Turkey. In the standard kriging model, the input data nonlinearity effects are increased by using a nonlinear map and transferring input data from a polynomial to an exponential basic function. The accuracy, precision, and over/under prediction tendencies of the response surface method, kriging, improved kriging, multilayer perceptron neural network using the Levenberg–Marquardt (MLP-LM) as well as a conjugate gradient (MLP-CG), radial basis function neural network (RBFNN), multivariate adaptive regression spline (MARS), M5Tree and support vector regression (SVR) were compared. Overall, all the applied models were highly capable of predicting monthly EP in both stations with a mean absolute error (MAE) < 0.77 mm and a Willmott index (d) > 0.95. Considering periodicity as an input parameter, the MLP-LM provided better results than the other methods among the soft computing models (MAE = 0.492 mm and d = 0.981). However, the improved kriging method surpassed all the other models based on the statistical measures (MAE = 0.471 mm and d = 0.983). Finally, the outcomes of the Mann–Whitney test indicated that the applied soft computational models do not have significant superiority over the statistical ones (p-value > 0.65 at α = 0.01 and α = 0.05).

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Accuracy enhancement for monthly evaporation predicting model utilizing evolutionary machine learning methods

Cited by 25 publications

References 33 publications

Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

Forecasting Daily and Monthly Reference Evapotranspiration in the Aidoghmoush Basin Using Multilayer Perceptron Coupled with Water Wave Optimization

Towards a Comprehensive Assessment of Statistical versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction

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