Publication:
Hydrological Drought Forecasting Using Modified Surface Water Supply Index (SWSI) and Streamflow Drought Index (SDI) in Conjunction with Artificial Neural Networks (ANNs)

Simple item page
dc.contributor.authorWambua, Raphael M.
dc.date.accessioned2024-03-06T12:28:35Z
dc.date.available2024-03-06T12:28:35Z
dc.date.issued2019
dc.description.abstractHydrological drought in upper Tana River basin adversely affects water resources. In this study, a hydrological drought was forecasted using a Surface Water Supply Index (SWSI), a Streamflow Drought Index (SDI) and an Artificial Neural Networks (ANNs). The best SWSI involved combinations of rainfall and the index values integrated into ANNs. The best forecasts with SDI entailed composite functions of rainfall, stream flow and SDI. Different ANN models for both SWSI and SDI with lead times of 1 to 24 months were tested at hydrometric stations. Results show that the forecasting ability of all the networks decreased with the increase in lead-time. The best ANNs with specific architecture performed differently based on forecasting lead-time. SWSI drought forecasts were better than those of the SDI for all lead-times. The SWSI and SDI depicted R values of 0.752 and 0.732 for station 4AB05 for one-month lead-time. The findings are useful as an effective hydrological-drought early warning for viable mitigation and preparedness approaches to minimize the negative effects of drought.
dc.description.sponsorshipN/A
dc.identifier.citationWambua, R. M. (2019). Hydrological Drought Forecasting Using Modified Surface Water Supply Index (SWSI) and Streamflow Drought Index (SDI) in Conjunction with Artificial Neural Networks (ANNs). International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 10(4), 39-57. http://doi.org/10.4018/IJSSMET.2019100103
dc.identifier.urihttps://www.igi-global.com/gateway/article/www.igi-global.com/gateway/article/233606
dc.identifier.urihttps://repository.nrf.go.ke/handle/123456789/702
dc.language.isoen
dc.publisherIGI Global
dc.subjectSouth Eastern Kenya University
dc.titleHydrological Drought Forecasting Using Modified Surface Water Supply Index (SWSI) and Streamflow Drought Index (SDI) in Conjunction with Artificial Neural Networks (ANNs)
dc.typeArticle
dspace.entity.typePublication

Files

License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.62 KB
Format:
Item-specific license agreed to upon submission
Description:
Download

Collections

Intergrated Water Resources Management