Wavelet-ANN versus ANN-based model for hydrometeorological drought forecasting

Md Munir H. Khan, Nur Shazwani Muhammad, Ahmed El-Shafie

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Malaysia is one of the countries that has been experiencing droughts caused by a warming climate. This study considered the Standard Index of Annual Precipitation (SIAP) and Standardized Water Storage Index (SWSI) to represent meteorological and hydrological drought, respectively. The study area is the Langat River Basin, located in the central part of peninsular Malaysia. The analysis was done using rainfall and water level data over 30 years, from 1986 to 2016. Both of the indices were calculated in monthly scale, and two neural network-based models and two wavelet-based artificial neural network (W-ANN) models were developed for monthly droughts. The performance of the SIAP and SWSI models, in terms of the correlation coefficient (R), was 0.899 and 0.968, respectively. The application of a wavelet for preprocessing the raw data in the developed W-ANN models achieved higher correlation coefficients for most of the scenarios. This proves that the created model can predict meteorological and hydrological droughts very close to the observed values. Overall, this study helps us to understand the history of drought conditions over the past 30 years in the Langat River Basin. It further helps us to forecast drought and to assist in water resource management.

Original languageEnglish
Article number998
JournalWater (Switzerland)
Volume10
Issue number8
DOIs
Publication statusPublished - 27 Jul 2018

Fingerprint

Drought
Droughts
drought
wavelet
Neural Networks (Computer)
neural network
neural networks
water
Malaysia
Neural networks
water storage
Rivers
Catchments
artificial neural network
Water
river basin
river
Water Resources
Water levels
Water resources

Keywords

  • ANN model
  • Discrete wavelet
  • Drought analysis
  • Drought indices
  • Hydrological drought
  • Meteorological drought
  • SIAP
  • SWSI

ASJC Scopus subject areas

  • Biochemistry
  • Geography, Planning and Development
  • Aquatic Science
  • Water Science and Technology

Cite this

Wavelet-ANN versus ANN-based model for hydrometeorological drought forecasting. / Khan, Md Munir H.; Muhammad, Nur Shazwani; El-Shafie, Ahmed.

In: Water (Switzerland), Vol. 10, No. 8, 998, 27.07.2018.

Research output: Contribution to journalArticle

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