Daily pan evaporation modeling using chi-squared automatic interaction detector, neural networks, classification and regression tree

Kisi, Özgür; Genç, Onur; Dinç, Semih; Zounemat‐Kermani, Mohammad

doi:10.1016/j.compag.2016.01.026

Cited by 72 publications

(17 citation statements)

References 31 publications

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“…Goyal, Bharti, Quilty, Adamowski, and Pandey (2014) use the main meteorological parameters in the form of four scenarios to predict daily evaporation amounts in India using a support vector regression (SVR) model, and report RMSE values in the range of 1.92 to 2.12 mm/day. It is impossible to model hydrological systems in their entirety due to the complexity of determining all the relevant parameters and the lack of statistical information; thus, the use of simulation methods such as artificial intelligence models is essential (Kişi, Genc, Dinc, & Zounemat-Kermani, 2016;Mosavi, Bathla, & Varkonyi-Koczy, 2017;Wu, Chau, & Li, 2009). The ANN technique is one such method, and its suitability for hydrological research applications is verified by the results of a number of studies (Cigizoglu & Kişi, 2006;Cobaner, Unal, & Kişi, 2009;Guven & Kişi, 2011;Kumar, Raghuwanshi, Singh, Wallender, & Pruitt, 2002;Moghaddamnia, Ghafari Gousheh, Piri, Amin, & Han, 2009;Taormina, Chau, & Sivakumar, 2015;Wu & Chau, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Allawi and El-Shafie (2016) obtained a correlation coefficient of .96 using an ANFIS model to predict daily evaporation in Johor, southeastern Malaysia. Kişi et al (2016) used three methods to predict daily evaporation in Turkey, and found that their ANN (RMSE = 2 mm/day) performed better than their chi-squared automatic interaction detector (CHAID, RMSE = 2.06 mm/day) and their classification and regression tree (CR-T, RMSE = 2.07 mm/day), although the differences are not significant.…”

Section: Introductionmentioning

confidence: 99%

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Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran

Moazenzadeh

Mohammadi

Shamshirband

et al. 2018

Engineering Applications of Computational Fluid Mechanics

197

100

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Evaporation accounts for varying shares of water balance under different climatic conditions, and its correct prediction poses a significant challenge before water resources management in watersheds. Given the complex and nonlinear behavior of the evaporation component, and according to the fact that this parameter is not measured at many meteorological stations, at least during some timeframes, and that the meteorological stations measuring this component are not properly distributed in many developing countries, including Iran, the main objective of this work was to predict the evaporation component at two meteorological stations (Rasht and Lahijan) located in Gilan province in northern Iran over the 2006-2016 time period. To that end, those meteorological parameters recorded at the two stations which had the highest impact on evaporation prediction were identified using Pearson correlation coefficient. Selected parameters were then used, under separate scenarios, as inputs to support vector regression (SVR) and SVR model coupled with firefly algorithm (SVR-FA) in order to simulate evaporation values on a daily scale. Evaporation amounts showed the highest correlation with net solar radiation and saturation vapor pressure deficit at Lahijan and Rasht stations, respectively. Root mean square error values of evaporation prediction at testing phase of SVR and SVR-FA ranged from 1.05 to 1.43 and 1.02 to 1.31 mm, respectively, at Lahijan station and from 1.02 to 1.28 and 0.88 to 1.17 mm, respectively, at Rasht station for various scenarios. For underpredicted evaporation data set, the magnitude of RMSE reduction from SVR1 to SVR7 was 27% at Lahijan and 18% at Rasht station; whereas RMSE decrement from SVR-FA1 to SVR-FA7 was 18 and 26 percent at Lahijan and Rasht stations, respectively. This means that for the underpredicted data set, the role of increasing the number of SVR and SVR-FA input parameters in decreasing evaporation prediction error has been more conspicuous at Lahijan and Rasht stations, respectively. Analysis of SVR and SVR-FA performance at various 2-mm intervals of measured evaporation showed that prediction error has generally been increasing with increment of evaporation values, with the highest errors observed at the 8-10 mm interval for both Lahijan and Rasht stations (error rates of 3.42 and 2.42 mm/day at Lahijan and 6.13 and 5.84 mm/day at Rasht station, with SVR1 and SVR-FA1 models, respectively).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran

Moazenzadeh

Mohammadi

Shamshirband

et al. 2018

Engineering Applications of Computational Fluid Mechanics

197

100

View full text Add to dashboard Cite

show abstract

“…Other than the aforementioned studies, several researchers have validated the utility of a standalone ANN and a hybrid MLP-PSO model for forecasting pan evaporation rates (Abudu, Cui, King, Moreno, & Bawazir, 2011;Deo & Şahin, 2015a;Deo & Samui, 2017;Keshtegar, Piri, & Kisi, 2016;Kisi, Genc, Dinc, & Zounemat-Kermani, 2016). On the other hand, regardless of the profusely testified prospects of using AI-based methods for their relatively accurate performance, a large proportion of these research works have utilized a standalone AI method.…”

Section: Introductionmentioning

confidence: 99%

Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: a case study in Talesh, Northern Iran

Ghorbani

Kazempour

Chau

et al. 2018

Engineering Applications of Computational Fluid Mechanics

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Accurate simulation of evaporation plays an important role in the efficient management of water Resources. Generally, evaporation is measured using the direct method where Class A panevaporimeter is used, and an indirect method that includes empirical equations. However, despite its widespread usage, Class A pan-evaporimeter method can be affected by human and instrumentation errors. Empirical equations, on the other hand, are generally linked to the different climatic factors that should provide initial or boundary conditions in the mathematical equations that affect the rate of evaporation. Considering these challenging, heuristic soft computing approaches that do not need key information about the physics of evaporation. In this study, a Quantum-behaved Particle Swarm Optimization algorithm, embedded into a multi-layer perceptron technique, is developed to estimate the evaporation rates over a daily forecast horizon. The measured evaporation data from 2012-2014 for Talesh meteorological station located in Northern Iran are employed. The predictive accuracy of the MLP-QPSO model is evaluated with existing methods: i.e. a hybrid MLP-PSO and a standalone MLP model. The results are evaluated in respect to statistical performance criterion: the mean absolute error, root mean square error (RMSE), Willmott's Index and the Nash-Sutcliffe coefficient. In conjunction with these metrics, Taylor diagrams are also utilized to assess the level of agreement between the forecasted and observed evaporation data. Evidently, the hybrid MLP-QPSO model is confirmed to be an optimal forecasting tool applied for estimating daily pan evaporation, outperforming both the hybrid MLP-PSO and the standalone model.In light of these results, the present study justifies the potential utility of the hybrid MLP-QPSO model to be applied for estimating daily evaporation rates in North of Iran.

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“…CART was also developed based on decision trees to explain the variation of the dependent variable by one or more independent variables [79]. As a "white box" algorithm, the relationship between dependent and independent variables is more straightforward with the CART algorithm [80,81]. It does not consider any previous assumptions related to the relationship between variables.…”

Section: Classification and Regression Tree (Cart)mentioning

confidence: 99%

Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO

et al. 2019

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Dust is one of the components causing heavy environmental pollution in open-pit mines, especially PM10. Some pathologies related to the lung, respiratory system, and occupational diseases have been identified due to the effects of PM10 in open-pit mines. Therefore, the prediction and control of PM10 concentration in the production process are necessary for environmental and health protection. In this study, PM10 concentration from drilling operations in the Coc Sau open-pit coal mine (Vietnam) was investigated and considered through a database including 245 datasets collected. A novel hybrid artificial intelligence model was developed based on support vector regression (SVR) and a swarm optimization algorithm (i.e., particle swarm optimization (PSO)), namely PSO-SVR, for estimating PM10 concentration from drilling operations at the mine. Polynomial (P), radial basis function (RBF), and linear (L) kernel functions were considered and applied to the development of the PSO-SVR models in the present study, abbreviated as PSO-SVR-P, PSO-SVR-RBF, and PSO-SVR-L. Also, three benchmark artificial intelligence techniques, such as k-nearest neighbors (KNN), random forest (RF), and classification and regression trees (CART), were applied and developed for estimating PM10 concentration and then compared with the PSO-SVR models. Root-mean-squared error (RMSE) and determination coefficient (R2) were used as the statistical criteria for evaluating the performance of the developed models. The results exhibited that the PSO algorithm had an essential role in the optimization of the hyper-parameters of the SVR models. The PSO-SVR models (i.e., PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF) had higher performance levels than the other models (i.e., RF, CART, and KNN) with an RMSE of 0.040, 0.042, and 0.043; and R2 of 0.954, 0.948, and 0.946; for the PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF models, respectively. Of these PSO-SVR models, the PSO-SVR-L model was the most dominant model with an RMSE of 0.040 and R2 of 0.954. The remaining three benchmark models (i.e., RF, CART, and KNN) yielded a more unsatisfactory performance with an RMSE of 0.060, 0.052, and 0.067; and R2 of 0.894, 0.924, and 0.867, for the RF, CART, and KNN models, respectively. Furthermore, the findings of this study demonstrated that the density of rock mass, moisture content, and the penetration rate of the drill were essential parameters on the PM10 concentration caused by drilling operations in open-pit mines.

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Daily pan evaporation modeling using chi-squared automatic interaction detector, neural networks, classification and regression tree

Cited by 72 publications

References 31 publications

Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran

Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran

Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: a case study in Talesh, Northern Iran

Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO

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