Streamflow prediction in ungauged catchments in the east coast of peninsular Malaysia using multivariate statistical techniques
The east coast of Peninsular Malaysia is one of the most vulnerable regions of Malaysia to hydrological disasters, which is believed to become more vulnerable due to climate change. Studies to have better understandings of the hydrological processes in the region are therefore, of paramount importan...
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Penerbit UTM Press
2016
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| _version_ | 1796861560016076800 |
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| author | Abdulrazaq, Salaudeen Ismail, Tarmizi Heryansyah, Arien Umar, Faruk Lawan Sahar, Hadi Pour Md., Mahiuddin Alamgir |
| author_facet | Abdulrazaq, Salaudeen Ismail, Tarmizi Heryansyah, Arien Umar, Faruk Lawan Sahar, Hadi Pour Md., Mahiuddin Alamgir |
| author_sort | Abdulrazaq, Salaudeen |
| collection | ePrints |
| description | The east coast of Peninsular Malaysia is one of the most vulnerable regions of Malaysia to hydrological disasters, which is believed to become more vulnerable due to climate change. Studies to have better understandings of the hydrological processes in the region are therefore, of paramount importance for disaster risk mitigation. However, unavailability of long-term river discharge data is one of the major constraints of hydrologic studies in the area. The major objective of this study is to predict river discharge in ungauged river basins in the study area. For this purpose, a set of multiple linear regression equations and exponential functions have been developed, which are expressed in the forms of multivariate equations. Available streamflow data along with other catchment characteristics from gauged catchments were used to develop the equations and were subsequently applied to the poorly gauged or ungauged catchments within the study area for prediction of streamflow. In this present study, 4 to 7 explanatory variables were selected as the input variables, which comprise of climatic, geomorphologic, geographic characteristics, soil properties, land use pattern and land cover of the area. Ten flow metrics as maximum, 0.05, 0.10, 0.25, 0.50, 0.75, 0.90, and 0.95, mean and minimum were therefore predicted. Thus, the results of the developed multivariate equations revealed the model to be capable of predicting the desired flow metrics at ungauged catchments in the area under consideration with reasonable accuracy. |
| first_indexed | 2024-03-05T19:58:15Z |
| format | Article |
| id | utm.eprints-66962 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:58:15Z |
| publishDate | 2016 |
| publisher | Penerbit UTM Press |
| record_format | dspace |
| spelling | utm.eprints-669622017-11-20T08:52:08Z http://eprints.utm.my/66962/ Streamflow prediction in ungauged catchments in the east coast of peninsular Malaysia using multivariate statistical techniques Abdulrazaq, Salaudeen Ismail, Tarmizi Heryansyah, Arien Umar, Faruk Lawan Sahar, Hadi Pour Md., Mahiuddin Alamgir TA Engineering (General). Civil engineering (General) The east coast of Peninsular Malaysia is one of the most vulnerable regions of Malaysia to hydrological disasters, which is believed to become more vulnerable due to climate change. Studies to have better understandings of the hydrological processes in the region are therefore, of paramount importance for disaster risk mitigation. However, unavailability of long-term river discharge data is one of the major constraints of hydrologic studies in the area. The major objective of this study is to predict river discharge in ungauged river basins in the study area. For this purpose, a set of multiple linear regression equations and exponential functions have been developed, which are expressed in the forms of multivariate equations. Available streamflow data along with other catchment characteristics from gauged catchments were used to develop the equations and were subsequently applied to the poorly gauged or ungauged catchments within the study area for prediction of streamflow. In this present study, 4 to 7 explanatory variables were selected as the input variables, which comprise of climatic, geomorphologic, geographic characteristics, soil properties, land use pattern and land cover of the area. Ten flow metrics as maximum, 0.05, 0.10, 0.25, 0.50, 0.75, 0.90, and 0.95, mean and minimum were therefore predicted. Thus, the results of the developed multivariate equations revealed the model to be capable of predicting the desired flow metrics at ungauged catchments in the area under consideration with reasonable accuracy. Penerbit UTM Press 2016-01-06 Article PeerReviewed Abdulrazaq, Salaudeen and Ismail, Tarmizi and Heryansyah, Arien and Umar, Faruk Lawan and Sahar, Hadi Pour and Md., Mahiuddin Alamgir (2016) Streamflow prediction in ungauged catchments in the east coast of peninsular Malaysia using multivariate statistical techniques. Jurnal Teknologi, 78 (6-12). pp. 43-49. ISSN 0127-9696 http://dx.doi.org/10.11113/jt.v78.9231 DOI : 10.11113/jt.v78.9231 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Abdulrazaq, Salaudeen Ismail, Tarmizi Heryansyah, Arien Umar, Faruk Lawan Sahar, Hadi Pour Md., Mahiuddin Alamgir Streamflow prediction in ungauged catchments in the east coast of peninsular Malaysia using multivariate statistical techniques |
| title | Streamflow prediction in ungauged catchments in the east coast of peninsular Malaysia using multivariate statistical techniques |
| title_full | Streamflow prediction in ungauged catchments in the east coast of peninsular Malaysia using multivariate statistical techniques |
| title_fullStr | Streamflow prediction in ungauged catchments in the east coast of peninsular Malaysia using multivariate statistical techniques |
| title_full_unstemmed | Streamflow prediction in ungauged catchments in the east coast of peninsular Malaysia using multivariate statistical techniques |
| title_short | Streamflow prediction in ungauged catchments in the east coast of peninsular Malaysia using multivariate statistical techniques |
| title_sort | streamflow prediction in ungauged catchments in the east coast of peninsular malaysia using multivariate statistical techniques |
| topic | TA Engineering (General). Civil engineering (General) |
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