Disaggregation of Rainfall Time Series using Artificial Neural Network in Case of Limited Data
Saravanan Poomalai1, Sivapragasam Chandrasekaran2
1Saravanan Poomalai, Center for Water Technology, Department of Civil Engineering, Kalasalingam Academy of Research and Education, Srivilliputtur (Tamil Nadu), India.
2Sivapragasam Chandrasekaran, Center for Water Technology, Department of Civil Engineering, Kalasalingam Academy of Research and Education, Srivilliputtur (Tamil Nadu), India.
Manuscript received on 23 November 2019 | Revised Manuscript received on 17 December 2019 | Manuscript Published on 30 December 2019 | PP: 177-181 | Volume-9 Issue-1S4 December 2019 | Retrieval Number: A10051291S419/19©BEIESP | DOI: 10.35940/ijeat.A1005.1291S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Temporal resolution of rainfall series needs to be necessarily less to use it in many engineering applications. But most of the simulated and observed rainfall series are coarser than 3hours. Hence, it is imperative to disaggregate coarser rainfall to finer. The quantum of necessary fineness depends on application in which the rainfall data is going to be used. In this paper, the competency of Artificial Neural Network to disaggregate 3 hour rainfall into hourly, in case of limited data is verified. It is found that the disaggregation is viable with the constraint of limited data also. The rainfall is disaggregated using three models, of which, performance of the second model is much better than the others. Nonetheless the constraint of limited data, the rationale behind the better performance of the second model, is clearly discussed.
Keywords: Artificial Neural Network, Rainfall Disaggregation, Temporal Resolution of Rainfall.
Scope of the Article: Data Analytics