Suitability of ANN applied as a hydrological model coupled with statistical downscaling model: a case study in the northern area of Peninsular Malaysia

Zulkarnain Hassan; Supiah Shamsudin; Sobri Harun; Marlinda Abdul Malek; Nuramidah Hamidon

Suitability of ANN applied as a hydrological model coupled with statistical downscaling model: a case study in the northern area of Peninsular Malaysia

Date

2015

Authors

Zulkarnain Hassan

Supiah Shamsudin

Sobri Harun

Marlinda Abdul Malek

Nuramidah Hamidon

Publisher

Springer

Abstract

The increase in global surface temperature in response to the changing composition of the atmosphere will significantly impact upon local hydrological regimes and water resources. This situation will then lead to the need for an assessment of regional climate change impacts. The objectives of this study are to determine current and future climate change scenarios using statistical downscaling model (SDSM) and to assess climate change impact on river runoff using artificial neural network (ANN) and identification of unit hydrographs and component flows from rainfall, evaporation and streamflow data (IHACRES) models, respectively.

Keywords

Statistical downscaling, IHACRES, Artificial neural network, River runoff, Malaysia

Citation

Hassan, Z., Shamsudin, S., Harun, S., Malek, M. A., & Hamidon, N. (2015). Suitability of ANN applied as a hydrological model coupled with statistical downscaling model: a case study in the northern area of Peninsular Malaysia. Environmental earth sciences, 74, 463-477.