On the reliability of soft computing methods in the estimation of dew point temperature: The case of arid regions of Iran

Attar, Nasrin Fathollahzadeh; Khalili, Keivan; Behmanesh, Javad; Khanmohammadi, Neda

doi:10.1016/j.compag.2018.08.029

Cited by 28 publications

(14 citation statements)

References 25 publications

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“…(a) R: The closer is the value to 1, the higher the accuracy. (b) RMSE: The smaller the RMSE, the more precise the prediction will be [61]. (c) NSE: The closer the value is to 1, the higher the accuracy.…”

Section: Performance Metricsmentioning

confidence: 99%

Enhancing the Prediction Accuracy of Data-Driven Models for Monthly Streamflow in Urmia Lake Basin Based upon the Autoregressive Conditionally Heteroskedastic Time-Series Model

et al. 2020

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Hydrological modeling is one of the important subjects in managing water resources and the processes of predicting stochastic behavior. Developing Data-Driven Models (DDMs) to apply to hydrological modeling is a very complex issue because of the stochastic nature of the observed data, like seasonality, periodicities, anomalies, and lack of data. As streamflow is one of the most important components in the hydrological cycle, modeling and estimating streamflow is a crucial aspect. In this study, two models, namely, Optimally Pruned Extreme Learning Machine (OPELM) and Chi-Square Automatic Interaction Detector (CHAID) methods were used to model the deterministic parts of monthly streamflow equations, while Autoregressive Conditional Heteroskedasticity (ARCH) was used in modeling the stochastic parts of monthly streamflow equations. The state of art and innovation of this study is the integration of these models in order to create new hybrid models, ARCH-OPELM and ARCH-CHAID, and increasing the accuracy of models. The study draws on the monthly streamflow data of two different river stations, located in north-western Iran, including Dizaj and Tapik, which are on Nazluchai and Baranduzchai, gathered over 31 years from 1986 to 2016. To ascertain the conclusive accuracy, five evaluation metrics including Correlation Coefficient (R), Root Mean Square Error (RMSE), Nash–Sutcliffe Efficiency (NSE), Mean Absolute Error (MAE), the ratio of RMSE to the Standard Deviation (RSD), scatter plots, time-series plots, and Taylor diagrams were used. Standalone CHAID models have better results than OPELM methods considering sole models. In the case of hybrid models, ARCH-CHAID models in the validation stage performed better than ARCH-OPELM for Dizaj station (R = 0.96, RMSE = 1.289 m3/s, NSE = 0.92, MAE = 0.719 m3/s and RSD = 0.301) and for Tapik station (R = 0.94, RMSE = 2.662 m3/s, NSE = 0.86, MAE = 1.467 m3/s and RSD = 0.419). The results remarkably reveal that ARCH-CHAID models in both stations outperformed all other models. Finally, it is worth mentioning that the new hybrid “ARCH-DDM” models outperformed standalone models in predicting monthly streamflow.

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Section: Performance Metricsmentioning

confidence: 99%

Enhancing the Prediction Accuracy of Data-Driven Models for Monthly Streamflow in Urmia Lake Basin Based upon the Autoregressive Conditionally Heteroskedastic Time-Series Model

et al. 2020

Self Cite

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“…Since last decades, machine learning models like adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), model tree (MT), gene expression programming (GEP), support vector machine (SVM), and extreme learning machine (ELM) have been widely extensively developed designed for solving various environmental engineering and water quality problems (Alizadeh et al, 2018;Anctil et al, 2008;Attar et al, 2018;Chen et al, 2020;Chen & Chau, 2019;Choubin et al, 2019;Hong et al, 2018;Kargar et al, 2020;Mouatadid et al, 2018;Najafzadeh et al, 2016;Noori & Kalin, 2016;Rezaie-Balf & Kisi, 2018;Shamshirband et al, 2019;Shiri et al, 2011;Solomatine & Xue, 2004;Taormina & Chau, 2015;Yassin et al, 2016;Zounemat-Kermani et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

An integrated machine learning, noise suppression, and population-based algorithm to improve total dissolved solids prediction

Sun

Rajabtabar

Phillips

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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Monitoring the water contaminants is of utmost importance in water resource management. Prediction of the total dissolved solid (TDS) is particularly essential for water quality management and planning in the areas exposed to a mixture of pollutants. TDS primarily includes inorganic minerals and organic matters, and various salts and increasing the concentration of TDS causes the esthetic problems. The reflection of the pollutant burden of the aquatic system can remarkably determined by TDS magnitudes. This study focuses on the prediction of TDS and several biochemical parameters such as Na, Ca, HCO3, and Mg in a river system. To overcome nonstationarity, randomness, and nonlinearity of the TDS data, a multi-step supervised machine learning evolutionary algorithm (MSM-LEA) is proposed to improve the model's performance at two gaging stations, namely Rig-Cheshmeh and Soleyman-Tangeh, in the Tajan River, Iran. In addition, a hybrid model that recruits intrinsic time-scale decomposition (ITD) for frequency resolution of the input data as well as a multivariate adaptive regression spline (MARS) were adopted. A novel metaheuristic optimization algorithm, crow search algorithm (CSA), was also implemented to compute the optimal parameter values for the MARS model. To validate the proposed hybrid model, standalone MARS, empirical mode decomposition (EMD)-based models, and hybrid ITD-MARS as well as a MARS-CSA were considered as the benchmark models. Results suggest the ITD-MARS-CSA outperforms other models.

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“…In addition, dew point presented a highly significant hydrological process that contribute to several water resource planning, management and sustainability [5], [6]. Further, there are several watershed hydrological engineering practice, which require precise quantification for the dew point temperature [7]. Moreover, dew point temperature is an important parameter for the prediction of long-term changes in climate [8].…”

Section: Introductionmentioning

confidence: 99%

Dew Point Time Series Forecasting at the North Dakota

Arikan¹,

Jiechen²,

Sabbah³

et al. 2021

Kbes

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Hydrological time series forecasting is one of the hot topics in the domain of statistical hydrology. Providing accurate forecasting can contribute to diverse applications for catchment sustainability and management. Dew point temperature (Tdew) is one of the complex hydrological processes that highly essential to be quantified accurately for several catchment activities such as crops, agriculture, and others. In this study, three types of models’ recursive strategy, direct strategy, and DirRec which is the combination of recursive and direct strategies were adopted to obtain h-steps ahead predictions of Tdew. Ten years monthly scale dataset of Tdew at two meteorological stations (Beach and Cavalier) located at the North Dakota, USA, were used for the modeling development. The performance of the considered models was compared with two benchmark models: autoregressive moving average (ARIMA) and exponential smoothing (ETS). Modeling results indicated that, compared with the benchmark models, the proposed methods gave good results for the multi-ahead forecasting. For instance, for Cavalier station, the root mean squared prediction errors obtained from the proposed and benchmark methods when the forecast horizon is 12 are as follows: recursive strategy (RMSPE = 3.731) direct strategy (RMSPE = 3.385), DirRec (RMSPE = 3.141), ARIMA (RMSPE = 12.957), and ETS (RMSPE = 27.479).

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On the reliability of soft computing methods in the estimation of dew point temperature: The case of arid regions of Iran

Cited by 28 publications

References 25 publications

Enhancing the Prediction Accuracy of Data-Driven Models for Monthly Streamflow in Urmia Lake Basin Based upon the Autoregressive Conditionally Heteroskedastic Time-Series Model

Enhancing the Prediction Accuracy of Data-Driven Models for Monthly Streamflow in Urmia Lake Basin Based upon the Autoregressive Conditionally Heteroskedastic Time-Series Model

An integrated machine learning, noise suppression, and population-based algorithm to improve total dissolved solids prediction

Dew Point Time Series Forecasting at the North Dakota

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