Earticle

초록

영어

The severe 2014 drought recorded in the Selangor river basin has affected the everyday life of three million people inhabited in the northern area of Selangor and the neighboring federal capital of Malaysia, the city of Kuala Lumpur, where 70% of the source of water supply comes from Selangor dam in the upper reach of Selangor basin. Of particular importance is the water rationing imposed by the water authority in April 2014 lasting for one month and the shortage of food supply in the dry period as a result of reduction in food supply from the Selangor area. As such, drought monitoring, identification and forecasting play an important role in the planning and management of natural resources and water resource systems in the country. The purpose of this paper is to use established scientific methods and available hydrological data to identify, monitor, and forecast droughts for the planning, management and formulating drought strategies to reduce and mitigate the adverse effect of drought impacts. Standardized precipitation index (SPI) has been used as a conventional tool to identify and monitor drought occurrences. To achieve the aims, we use average long term monthly rainfall data for eight stations covering both the dry and wet seasons from Selangor river basin to derive the SPI values for durations of 3 to 9 months. These drought indicators, which are time series derived from rainfall data together with the multi-layer artificial neural networks model were used for drought forecasting for the basin. Forecasting were made for SPI with a one month ahead lead time as forecasting accuracy is reduced for longer lead times. This has been shown by studies carried out elsewhere. Our finding indicates that more accurate predictions are achieved using SPI of longer durations, i.e. 6 and 9 months. This is consistent with findings of studies by others.

목차

Abstract
 1. Introduction
 2. Materials and Methods
  2.1. The Study Area
  2.2. Rainfall Data
  2.3. Infilling of Missing Data
  2.4. Trend of Rainfall Data
  2.5. Outliers
  2.6. Graphical Checks
  2.7. Methodology
 3. Results and Discussion
 4. Conclusion
 References

키워드

저자

• Daniel Hong [ UCSI University, Hong and Associates ]
• Kee An Hong [ UCSI University, Hong and Associates ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605