Abstract
Hydrological droughts are characterized based on their duration, severity, and magnitude. Among the most critical factors, precipitation, evapotranspiration, and runoff are essential in modeling the droughts. In this study, three indices of drought, i.e., Standardized Precipitation Index (SPI), Standardized Streamflow Index (SSI), and Standardized Precipitation Evapotranspiration Index (SPEI), are modeled using Support Vector Regression (SVR), Gene Expression Programming (GEP), and M5 model trees (MT). The results indicate that SPI delivered higher accuracy. Moreover, MT model performed better in predicting SSI by a CC of 0.8195 and a RMSE of 0.8186. Abbreviations: ANFIS: adaptive neuro-fuzzy inference system; ANN: artificial neural network; ANN: artificial neural network; BS-SVR: boosted-support Vector Regression; CC: correlation coefficient; ELM: extreme learning machine; GEP: gene Expression Programming; GP: genetic Programming; GPR: Gaussian process regression; KNN: k-nearest neighbor; LSSVM: least squares Support Vector Machine; LSSVR: least support vector regression; MAE: mean absolute error; MARS: multivariate adaptive regression splines; MLP: multilayer perceptron; MLR: multiple linear regression; MT: M5 model tree; P: precipitation; PDSI: palmer drought severity index; PET: potential evapotranspiration; RAE: relative absolute error; RMSE: root mean square error; RVM: relevance vector machine; SAR: sodium absorption index; SDR: standard deviation reduction; SPEI: standardized precipitation evapotranspiration index; SPI: standardized precipitation index; SSI: standardized streamflow index; SVM: support vector machine; SVR: support vector regression; WAANN: Wavelet-ARIMA-ANN; WANFIS: Wavelet-Adaptive Neuro-Fuzzy Inference System; WN: wavelet network
Highlights
Multiphase bubble column reactor (BCR) types are highly important for different industries because of their applications and efficiency (Kumar, Degaleesan, Laddha, & Hoelscher, 1976; Li & Prakash, 2002; Schäfer, Merten, & Eigenberger, 2002)
The computational fluid dynamics (CFD) outputs were assessed by combining the intelligence optimization algorithm of ant colony and fuzzy inference system (FIS) with
The CFD data are considered as training inputs of the ant colony method, and this method predicts the behavior of the bubble column reactor
Summary
Multiphase bubble column reactor (BCR) types are highly important for different industries because of their applications and efficiency (Kumar, Degaleesan, Laddha, & Hoelscher, 1976; Li & Prakash, 2002; Schäfer, Merten, & Eigenberger, 2002). The Fischer–Tropsch process is considered as a major application of the mentioned reactors in the chemical industries (Prakash, Margaritis, Li, & Bergougnou, 2001) It is the process of indirect coal liquefaction, resulting in various kinds of fuels like synthetic fuels, methanol synthesis, and transportation fuels (Chuntian & Chau, 2002; Maalej, Benadda, & Otterbein, 2003; Rabha, Schubert, & Hampel, 2013). The production of these kinds of fuels is environmentally advantageous compared to the fuels derived from petroleum (Behkish, Men, Inga, & Morsi, 2002; Kantarci et al, 2005; Michele & Hempel, 2002). The high heat transfer coefficients are characteristics of the bubble columns
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