Comparison of ice-affected streamflow estimates computed using artificial neural networks and multiple regression techniques

Chokmani, Karem; Ouarda, Taha B. M. J.; Hamilton, Stuart; Ghedira, Hosni; Gingras, H.

doi:10.1016/j.jhydrol.2007.11.024

Cited by 68 publications

(38 citation statements)

References 28 publications

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“…S mean, Ameanare the mean value of the series. Chokmani, et al [17]; Teryaki, et al [18] the best value of R 2 is 1.0 while . The optimum value of RBias is 0.0 and a better description of RBias and ENash was given also Moriasi, et al [19]; Meral and Cheleng [20].…”

Section: Model Verificationmentioning

confidence: 94%

Weather Forecasting Models Using Neural Networks and Adaptive Neuro Fuzzy Inference for Two Case Studies at Huoston, Texas and Dallas States

Arslan

Kayis

2018

Journal of Asian Scientific Research

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Article History Precipitation.Forecasting of precipitation is one of the most challenging operational tasks done by hydrologists. This operation can be described as most complicated procedure that includes multiple specialized fields of expertise. In this research a comprehensive study was employed to forecast daily precipitation depending on different weather parameters. This was done by using two different methods which are back propagation neural networks BPNN and adaptive neuro inference system ANFIS. Two case studies were selected for this operation which are Huoston, Texas and Dallas, Texas. The high performance of the applied models in forecasting the daily precipitation was concluded especially by using auxiliary weather data with the lagged day precipitation values since the BPNN and ANFIS were able to learn from continuous input data Contribution/ Originality: This study uses new estimation methodology in forecasting the daily precipitation by using different weather parameters for two regions at United states of America. The study can be considered as a comparison between the two well-known methods which are artificial networks and Adaptive Neuro-Fuzzy Inference System.

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Section: Model Verificationmentioning

confidence: 94%

Weather Forecasting Models Using Neural Networks and Adaptive Neuro Fuzzy Inference for Two Case Studies at Huoston, Texas and Dallas States

Arslan

Kayis

2018

Journal of Asian Scientific Research

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“…Secondly, no formal techniques are available to test the relative relevance of the input variables and to make the variable selection process in non-linear methods (24). Thirdly, we may find no etiologic interpretation for the calculated weights in the network as compared in the conventional models (25).…”

Section: History Of Using Alcohol History Of Underlying Diseasementioning

confidence: 99%

Comparison of Artificial Neural Network and Logistic Regression Models for Prediction of Psychological Symptom Six Months after Mild Traumatic Brain Injury

Shafiei

Fakharian

Omidi

et al. 2016

Iran J Psychiatry Behav Sci

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“…Thus, by simultaneously examining the RMSE and the BIAS, one can assess if the error results more from the estimation variability or rather from the bias made on the estimate (Chokmani et al, 2008). Aside from computing the BIAS and RMSE for the estimated statistics, both measures were also applied to compare the extended records with the observed records.…”

Section: Evaluation Proceduresmentioning

confidence: 99%

Record extension for short-gauged water quality parameters using a newly proposed robust version of the Line of Organic Correlation technique

Khalil

Adamowski

2012

Hydrol. Earth Syst. Sci.

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Abstract. In many situations the extension of hydrological or water quality time series at short-gauged stations is required. Ordinary least squares regression (OLS) of any hydrological or water quality variable is a traditional and commonly used record extension technique. However, OLS tends to underestimate the variance in the extended records, which leads to underestimation of high percentiles and overestimation of low percentiles, given that the data are normally distributed. The development of the line of organic correlation (LOC) technique is aimed at correcting this bias. On the other hand, the Kendall-Theil robust line (KTRL) method has been proposed as an analogue of OLS with the advantage of being robust in the presence of outliers. Given that water quality data are characterised by the presence of outliers, positive skewness and non-normal distribution of data, a robust record extension technique is more appropriate. In this paper, four record-extension techniques are described, and their properties are explored. These techniques are OLS, LOC, KTRL and a new technique proposed in this paper, the robust line of organic correlation technique (RLOC). RLOC includes the advantage of the LOC in reducing the bias in estimating the variance, but at the same time it is also robust in the presence of outliers. A Monte Carlo study and empirical experiment were conducted to examine the four techniques for the accuracy and precision of the estimate of statistical moments and over the full range of percentiles. Results of the Monte Carlo study showed that the OLS and KTRL techniques have serious deficiencies as record-extension techniques, while the LOC and RLOC techniques are nearly similar. However, RLOC outperforms OLS, KTRL and LOC when using real water quality records.

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Comparison of ice-affected streamflow estimates computed using artificial neural networks and multiple regression techniques

Cited by 68 publications

References 28 publications

Weather Forecasting Models Using Neural Networks and Adaptive Neuro Fuzzy Inference for Two Case Studies at Huoston, Texas and Dallas States

Weather Forecasting Models Using Neural Networks and Adaptive Neuro Fuzzy Inference for Two Case Studies at Huoston, Texas and Dallas States

Comparison of Artificial Neural Network and Logistic Regression Models for Prediction of Psychological Symptom Six Months after Mild Traumatic Brain Injury

Record extension for short-gauged water quality parameters using a newly proposed robust version of the Line of Organic Correlation technique

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