A major requirement for the assessment, development and sustainable use of water resources is the availability of good quality hydrological time series data of sufficiently long duration.However, it is not uncommon to find data that are riddled with gaps, characterized by questionable quality and short durations. Sometimes, the data are just not available. Such situations are most prevalent in developing countries and the consequence is a high degree of uncertainty in the assessed characteristics of water management schemes and ultimately its ineffectual performance. Thus dealing with these problems is an important exercise inhydrological analyses. This paper focuses on the multi-variate infilling of gaps for rainfall and streamflow data in the Shire River basin in Malawi, using a Self Organizing Map (SOM) approach, which is a form of unsupervised artificial neural networks. The results show that this approach can produce reliable estimates of hydro-meteorological data thus offering promise for reducing the uncertainties associated with the use of insufficient data for water resourcesassessment.
- Self-organizing maps
- rainfall and streamflow data