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dc.contributor.authorWambua, Raphael M.-
dc.date.accessioned2022-10-26T09:52:06Z-
dc.date.available2022-10-26T09:52:06Z-
dc.date.issued2020-
dc.identifier.citationInternational Journal of Environmental Sustainability and Green Technologies, Volume 11, Issue 1 January-June 2020en_US
dc.identifier.urihttps://www.igi-global.com/article/development-of-a-non-linear-integrated-drought-index-ndi-for-managing-drought-and-water-resources-forecasting-in-the-upper-tana-river-basin-kenya/243402-
dc.identifier.urihttp://repository.seku.ac.ke/handle/123456789/6901-
dc.descriptionDOI: 10.4018/IJESGT.2020010102en_US
dc.description.abstractThis article uses the non-linear integrated drought index (NDI) for managing drought and water resources forecasting in a tropical river basin. The NDI was formulated using principal component analysis (PCA). The NDI used hydro-meteorological data and forecasted using recursive multi-step neural networks. In this article, drought forecasting and projection is adopted for planning ahead for mitigation and for the adaptation of adverse effects of droughts and food insecurity in the river basin. Results that forecasting ability of NDI model using ANNs decreased with increase in lead time. The formulated NDI as a tool for projecting into the future.en_US
dc.language.isoenen_US
dc.subjectAbsolute Sensitivityen_US
dc.subjectANNen_US
dc.subjectDrought Forecastingen_US
dc.subjectNon-Linear Integrated-Drought Index (NDI)en_US
dc.titleDevelopment of a non-linear integrated drought index (NDI) for managing drought and water resources forecasting in the upper Tana river basin-Kenyaen_US
dc.typeArticleen_US
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