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Muhammad Tayyab
Huazhong University of Science and Technology

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Streamflow Prediction by Applying Generalized Regression Network with Time Series Decomposition Method Muhammad Tayyab; Jianzhong Zhou; Rana Adnan; Changqing Meng; Aqeela Zahra
Indonesian Journal of Electrical Engineering and Computer Science Vol 4, No 3: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v4.i3.pp611-616

Abstract

Precise and correct estimation of streamflow is important for the operative progression in water resources systems. The artificial intelligence approaches; such as artificial neural networks (ANN) have been applied for efficiently tackling the hydrological matters like streamflow forecasting in this study at upper Yangtze River. The objective is to investigate the certainty of monthly streamflow by applying artificial neural networks including Generalized Regression Network (GRNN). To overcome the non-linearity problem of streamflow, artificial neural networks integrated with discrete wavelet transform (DWT). Data has been analyzed by comparing the simulation outputs of the models with the correlation coefficient (R) root mean square errors (RMSE). It is found that the decomposition technique DWT has ability to improve the forecasting results as compare to single applied artificial neural networks. Moreover, all applied models are separately applies on the peak values as well which also have showed that intergrated model has more ability to catch the peak values
Monthly Precipitation Trend Analysis by Applying Nonparametric Mann- Kendall (MK) and Spearman’s rho (SR) Tests In Dongting Lake, China: 1961-2012 Muhammad Tayyab; Jianzhong Zhou; Rana Adnan; Aqeela Zahra
Indonesian Journal of Electrical Engineering and Computer Science Vol 5, No 1: January 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v5.i1.pp41-47

Abstract

This research highlights the precipitation trends and presents the results of the study in temporal and spatial scales. Precise predictions of precipitation trends can play imperative part in economic growth of a state. This study examined precipitation inconsistency for 23 stations at the Dongting Lake, China, over a 52-years study phase (1961–2012). Statistical, nonparametric Mann- Kendall (MK) and Spearman’s rho (SR) tests were applied to identify trends within monthly, seasonal, and annual precipitation. The trend-free prewhitening method used to exclude sequential correlation in the precipitation time series. The performance of the Mann- Kendall (MK) and Spearman’s rho (SR) tests was steady at the tested significance level. The results showed fusion of increasing (positive) and decreasing (negative) trends at different stations within monthly and seasonal time scale. In case of whole Dongting basin on monthly time scale, significant positive trend is found, while at Yuanjiang River and Xianjiag River both positive and negative significant trends are identified.
Co-Authors Aqeela Zahra Changqing Meng Jianzhong Zhou Rana Adnan