Infilling of missing rainfall and streamflow data in the Shire River basin,Malawi – A self organizing map approach |
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| Institution: | 1. School of Built Environment, Heriot Watt University, Riccarton, Edinburgh EH14 4AS, UK;2. School of Built Environment, Heriot Watt University, Dubai Campus, United Arab Emirates;1. Faculty of Information Engineering, China University of Geosciences, Wuhan, China;2. Department of Computer Science and Engineering, University of North Texas, Denton, TX 76210, USA;3. Hubei Jinlang Survey and Design Co. LTD, Luoshi Road, Wuhan, Hubei 430074, China;1. University of Bergen, Geophysical Institute, Allegaten 70, 5007 Bergen, Norway;2. University of Bergen, Bjerknes Centre for Climate Research, Jahnebakken 5, 5007 Bergen, Norway;3. French National Institute for Agriculture, Food, and Environment (INRAE), Riverly-Lyon Research Unit, 5 rue de la Doua, 69625 Villeurbanne, France;4. Norwegian Water Resources and Energy Directorate (NVE), Middelthuns gate 29, 0368 Oslo, Norway;5. IHE Delft Institute for Water Education, Westvest 7, 2611 AX Delft, Netherlands;1. Geography Section, School of Humanities, Universiti Sains Malaysia, Penang, Malaysia;2. State Key Laboratory of Hydrology – Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China;3. Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China;1. State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, PR China;2. Waterborne Transport Research Institute, Ministry of Transport of the People’s Republic of China, Beijing 100088, PR China |
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| Abstract: | 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 in hydrological analyses. This paper focuses on the multivariate 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 resources assessment. |
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