Yuk Feng Huang - Academia.edu (original) (raw)

Papers by Yuk Feng Huang

Stochastic Environmental Research and Risk Assessment, 2015

International Journal of Climatology, 2014

ABSTRACT :Climate change is a global issue that has impact on every living being in the world. On... more ABSTRACT :Climate change is a global issue that has impact on every living being in the world. One of the most noticeable consequences of these global phenomena is the inevitable water cycle modification, with precipitation being a major component in these processes. Consequently, research into the occurrence and distribution of precipitation has increased over the past few decades. As Malaysia is located in the tropical area where there is no distinct four seasons, analysing rainfall has therefore become one of the common assessment tools for climate change. In this study, rainfall trends of Langat River Basin were examined on a monthly and seasonal basis (monsoon and non-monsoon) for the period of 1970–2012. Only rainfall time series with duration more than 25 years and missing data less than 10% have been considered for this study. The Holt's test has been employed to model the rainfall trends for the 10 selected time series; while Kendall's Tau test and Spearman's Rho test were used to test, compare and support for the significance of the trends. For monthly rainfall trends analysis, it was found that March, July and November are among the months those have most of the stations with increasing rainfall trends; while May and September are the months with the highest number of stations showing decreasing rainfall trends. Specifically, station 2815001 shows the highest number of months with changing rainfall trends throughout the year; while station 44255 has the least number of months with changing rainfall trends. Based on the seasonal rainfall trend analysis, there are seven stations during the Northeast Monsoon that revealed upward trends and the result is found to be consistent with the monthly rainfall trend analysis.

Theoretical and Applied Climatology, 2013

ABSTRACT Various hydrological and meteorological variables such as rainfall and temperature have ... more ABSTRACT Various hydrological and meteorological variables such as rainfall and temperature have been affected by global climate change. Any change in the pattern of precipitation can have a significant impact on the availability of water resources, agriculture, and the ecosystem. Therefore, knowledge on rainfall trend is an important aspect of water resources management. In this study, the regional annual and seasonal precipitation trends at the Langat River Basin, Malaysia, for the period of 1982–2011 were examined at the 95 % level of significance using the regional average Mann–Kendall (RAMK) test and the regional average Mann–Kendall coupled with bootstrap (RAMK–bootstrap) method. In order to identify the homogeneous regions respectively for the annual and seasonal scales, firstly, at-site mean total annual and separately at-site mean total seasonal precipitation were spatialized into 5 km × 5 km grids using the inverse distance weighting (IDW) algorithm. Next, the optimum number of homogeneous regions (clusters) is computed using the silhouette coefficient approach. Next, the homogeneous regions were formed using the K-mean clustering method. From the annual scale perspective, all three regions showed positive trends. However, the application of two methods at this scale showed a significant trend only in the region AC1. The region AC2 experienced a significant positive trend using only the RAMK test. On a seasonal scale, all regions showed insignificant trends, except the regions I1C1 and I1C2 in the Inter-Monsoon 1 (INT1) season which experienced significant upward trends. In addition, it was proven that the significance of trends has been affected by the existence of serial and spatial correlations.

Natural Hazards, 2014

Uncertainty in depth-duration-frequency (DDF) curves is usually disregarded in the view of diffic... more Uncertainty in depth-duration-frequency (DDF) curves is usually disregarded in the view of difficulties associated in assigning a value to it. In central Iran, precipitation duration is often long and characterized with low intensity leading to a considerable uncertainty in the parameters of the probabilistic distributions describing rainfall depth. In this paper, the daily rainfall depths from 4 stations in the Zayanderood basin, Iran, were analysed, and a generalized extreme value distribution was fitted to the maximum yearly rainfall for durations of 1, 2, 3, 4 and 5 days. DDF curves were described as a function of rainfall duration (D) and return period (T). Uncertainties of the rainfall depth in the DDF curves were estimated with the bootstrap sampling method and were described by a normal probability density function. Standard deviations were modeled as a function of rainfall duration and rainfall depth using 10 4 bootstrap samples for all the durations and return periods considered for each rainfall station.

Natural Hazards, 2014

Your article is protected by copyright and all rights are held exclusively by Springer Science +B... more Your article is protected by copyright and all rights are held exclusively by Springer Science +Business Media Dordrecht. This e-offprint is for personal use only and shall not be selfarchived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com".

Journal of Water Supply: Research and Technology—AQUA, 2013

This study is designed to consider the uncertainty in the kinematic runoff and erosion model name... more This study is designed to consider the uncertainty in the kinematic runoff and erosion model named KINEROS2. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used for assessing the uncertainty associated with model predictions, which assumes that due to the limitations in model structure, data and calibration scheme, many different parameter sets can make acceptable simulations. GLUE is a Bayesian approach based on the Monte Carlo method for model calibration and uncertainty analysis. The assessment was performed in the Zayanderood River basin located in Central Iran. To make an accurate calibration, five runoff events were selected from three different gauging stations for the purpose. Statistical evaluations for streamflow prediction indicate that there is good agreement between the measured and simulated flows with Nash–Sutcliffe values of efficiency of 0.85 and 0.79 for calibration and validation periods respectively. Uncertainty analysis was carried out on the new distribution of input parameters for model validation.

Agricultural Water Management, 2014

ABSTRACT Evapotranspiration (ET) is a major component of the hydrologic cycle and its accurate fo... more ABSTRACT Evapotranspiration (ET) is a major component of the hydrologic cycle and its accurate forecasting is essential in all water resources applications. In this study, artificial neural network (ANN) and wavelet neural network (WNN) were utilized to forecast daily ET from temperature and wind speed data. The WNN model used in this study is a neural network model with one hidden layer and a wavelet function as an activation function. The climatic data of Redesdale climatology station, Australia for the period 2009–2012 were utilized for the analysis. The daily reference values of ET were calculated by the FAO-PM56 method. The maximum temperature, minimum temperatures and wind speed data were used as the inputs and the reference values of ET data series was utilized as the output of the ANN and WNN models. In order to assess the effect of decomposing the input data by wavelet transform on the models efficiency, the original dataset and separately the decomposed time series were applied for calibrating and validating the models. The influence of using wind speed data as the third input on the performance of models was also investigated. The results showed that both the ANN and WNN models predicted ET at an acceptable accuracy level. However, the wavlet-WNN261 (2 inputs, 6 neurons in the hidden layer and one output) performed the best with the RMSE, APE, N.S. and R values of 1.03 mm/day, 22%, 0.79 and 0.89, respectively.

Stochastic Environmental Research and Risk Assessment, 2015

International Journal of Climatology, 2014

ABSTRACT :Climate change is a global issue that has impact on every living being in the world. On... more ABSTRACT :Climate change is a global issue that has impact on every living being in the world. One of the most noticeable consequences of these global phenomena is the inevitable water cycle modification, with precipitation being a major component in these processes. Consequently, research into the occurrence and distribution of precipitation has increased over the past few decades. As Malaysia is located in the tropical area where there is no distinct four seasons, analysing rainfall has therefore become one of the common assessment tools for climate change. In this study, rainfall trends of Langat River Basin were examined on a monthly and seasonal basis (monsoon and non-monsoon) for the period of 1970–2012. Only rainfall time series with duration more than 25 years and missing data less than 10% have been considered for this study. The Holt's test has been employed to model the rainfall trends for the 10 selected time series; while Kendall's Tau test and Spearman's Rho test were used to test, compare and support for the significance of the trends. For monthly rainfall trends analysis, it was found that March, July and November are among the months those have most of the stations with increasing rainfall trends; while May and September are the months with the highest number of stations showing decreasing rainfall trends. Specifically, station 2815001 shows the highest number of months with changing rainfall trends throughout the year; while station 44255 has the least number of months with changing rainfall trends. Based on the seasonal rainfall trend analysis, there are seven stations during the Northeast Monsoon that revealed upward trends and the result is found to be consistent with the monthly rainfall trend analysis.

Theoretical and Applied Climatology, 2013

ABSTRACT Various hydrological and meteorological variables such as rainfall and temperature have ... more ABSTRACT Various hydrological and meteorological variables such as rainfall and temperature have been affected by global climate change. Any change in the pattern of precipitation can have a significant impact on the availability of water resources, agriculture, and the ecosystem. Therefore, knowledge on rainfall trend is an important aspect of water resources management. In this study, the regional annual and seasonal precipitation trends at the Langat River Basin, Malaysia, for the period of 1982–2011 were examined at the 95 % level of significance using the regional average Mann–Kendall (RAMK) test and the regional average Mann–Kendall coupled with bootstrap (RAMK–bootstrap) method. In order to identify the homogeneous regions respectively for the annual and seasonal scales, firstly, at-site mean total annual and separately at-site mean total seasonal precipitation were spatialized into 5 km × 5 km grids using the inverse distance weighting (IDW) algorithm. Next, the optimum number of homogeneous regions (clusters) is computed using the silhouette coefficient approach. Next, the homogeneous regions were formed using the K-mean clustering method. From the annual scale perspective, all three regions showed positive trends. However, the application of two methods at this scale showed a significant trend only in the region AC1. The region AC2 experienced a significant positive trend using only the RAMK test. On a seasonal scale, all regions showed insignificant trends, except the regions I1C1 and I1C2 in the Inter-Monsoon 1 (INT1) season which experienced significant upward trends. In addition, it was proven that the significance of trends has been affected by the existence of serial and spatial correlations.

Natural Hazards, 2014

Uncertainty in depth-duration-frequency (DDF) curves is usually disregarded in the view of diffic... more Uncertainty in depth-duration-frequency (DDF) curves is usually disregarded in the view of difficulties associated in assigning a value to it. In central Iran, precipitation duration is often long and characterized with low intensity leading to a considerable uncertainty in the parameters of the probabilistic distributions describing rainfall depth. In this paper, the daily rainfall depths from 4 stations in the Zayanderood basin, Iran, were analysed, and a generalized extreme value distribution was fitted to the maximum yearly rainfall for durations of 1, 2, 3, 4 and 5 days. DDF curves were described as a function of rainfall duration (D) and return period (T). Uncertainties of the rainfall depth in the DDF curves were estimated with the bootstrap sampling method and were described by a normal probability density function. Standard deviations were modeled as a function of rainfall duration and rainfall depth using 10 4 bootstrap samples for all the durations and return periods considered for each rainfall station.

Natural Hazards, 2014

Your article is protected by copyright and all rights are held exclusively by Springer Science +B... more Your article is protected by copyright and all rights are held exclusively by Springer Science +Business Media Dordrecht. This e-offprint is for personal use only and shall not be selfarchived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com".

Journal of Water Supply: Research and Technology—AQUA, 2013

This study is designed to consider the uncertainty in the kinematic runoff and erosion model name... more This study is designed to consider the uncertainty in the kinematic runoff and erosion model named KINEROS2. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used for assessing the uncertainty associated with model predictions, which assumes that due to the limitations in model structure, data and calibration scheme, many different parameter sets can make acceptable simulations. GLUE is a Bayesian approach based on the Monte Carlo method for model calibration and uncertainty analysis. The assessment was performed in the Zayanderood River basin located in Central Iran. To make an accurate calibration, five runoff events were selected from three different gauging stations for the purpose. Statistical evaluations for streamflow prediction indicate that there is good agreement between the measured and simulated flows with Nash–Sutcliffe values of efficiency of 0.85 and 0.79 for calibration and validation periods respectively. Uncertainty analysis was carried out on the new distribution of input parameters for model validation.

Agricultural Water Management, 2014

ABSTRACT Evapotranspiration (ET) is a major component of the hydrologic cycle and its accurate fo... more ABSTRACT Evapotranspiration (ET) is a major component of the hydrologic cycle and its accurate forecasting is essential in all water resources applications. In this study, artificial neural network (ANN) and wavelet neural network (WNN) were utilized to forecast daily ET from temperature and wind speed data. The WNN model used in this study is a neural network model with one hidden layer and a wavelet function as an activation function. The climatic data of Redesdale climatology station, Australia for the period 2009–2012 were utilized for the analysis. The daily reference values of ET were calculated by the FAO-PM56 method. The maximum temperature, minimum temperatures and wind speed data were used as the inputs and the reference values of ET data series was utilized as the output of the ANN and WNN models. In order to assess the effect of decomposing the input data by wavelet transform on the models efficiency, the original dataset and separately the decomposed time series were applied for calibrating and validating the models. The influence of using wind speed data as the third input on the performance of models was also investigated. The results showed that both the ANN and WNN models predicted ET at an acceptable accuracy level. However, the wavlet-WNN261 (2 inputs, 6 neurons in the hidden layer and one output) performed the best with the RMSE, APE, N.S. and R values of 1.03 mm/day, 22%, 0.79 and 0.89, respectively.