Please use this identifier to cite or link to this item: https://repository.seku.ac.ke/handle/123456789/6915
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dc.contributor.authorWambua, Raphael M.-
dc.contributor.authorMutua, Benedict M.-
dc.contributor.authorRaude, James M.-
dc.date.accessioned2022-11-04T11:41:27Z-
dc.date.available2022-11-04T11:41:27Z-
dc.date.issued2016-
dc.identifier.citationHandbook of Research on Computational Simulation and Modeling in Engineering, chapter 54, p 508-539 2016en_US
dc.identifier.urihttps://www.igi-global.com/chapter/stochastic-drought-forecasting-exploration-for-water-resources-management-in-the-upper-tana-river-basin-kenya/137452-
dc.identifier.urihttp://repository.seku.ac.ke/handle/123456789/6915-
dc.descriptionDOI: 10.4018/978-1-5225-0788-8.ch054en_US
dc.description.abstractThis chapter presents the trend of drought as a stochastic natural disaster influenced by the climate variability for the upper Tana River basin in Kenya. Drought frequency, duration and intensity in the upper Tana River basin have been increasing over the years. To develop measures for mitigating impacts of drought, the influencing hydro-meteorological parameters and their interaction are necessary. Drought definitions, fundamental concepts of droughts, classification of droughts, types of drought indices, historical droughts and application of artificial neural networks in analyzing impacts of drought on water resources with special focus on a Kenyan river basin is presented. Gaps for more focused research are identified. Although drought forecasting is very vital in managing key sectors such as water, agriculture and hydro-power generation, drought forecasting techniques in Kenya are limited. There is need therefore to develop an effective drought forecasting tool for on-set detection, classification and drought forecasting. The forecasting is necessary for decision making on matters of drought preparedness and proper water resources planning and management.en_US
dc.language.isoenen_US
dc.publisherIGI Globalen_US
dc.subjectDroughten_US
dc.subjectUnsupervised Learningen_US
dc.subjectRemote Sensingen_US
dc.subjectUpper Tana River Basinen_US
dc.subjectDrought Indicesen_US
dc.subjectReinforced Learningen_US
dc.subjectGlobal Warmingen_US
dc.subjectSatellite Based Drought Indicesen_US
dc.subjectArtificial Neural Networksen_US
dc.titleStochastic drought forecasting exploration for water resources management in the upper Tana River basin, Kenyaen_US
dc.typeBook chapteren_US
Appears in Collections:School of Engineering and Technology (BC)

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