Open AccessRainfall disaggregation in non-recording gauge stations using space-time information system
Author(s) -
H Derakhshan,
Nasser Talebbeydokhti
Publication year - 2011
Publication title -
scientia iranica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.299
H-Index - 51
eISSN - 2345-3605
pISSN - 1026-3098
DOI - 10.1016/j.scient.2011.08.003
Subject(s) - unavailability , rain gauge , computer science , artificial neural network , gauge (firearms) , kriging , multilayer perceptron , data mining , artificial intelligence , statistics , machine learning , geography , mathematics , telecommunications , radar , archaeology
The disaggregation of coarser Precipitation data will help to adjust the deficit of unavailability of data in non-recording gauge stations. The Artificial Neural Network (ANN) facilitates to adjust the rainfall time steps into desired small scales. At first, the Geostatistical method of co-kriging was used for mapping purposes to find the missing duration and depth of rainfall of some incomplete data stations in Sydney Australia. Then, since there was no information about the breakpoint data in non-recording target central station 7261, a process was performed to disaggregate the data of recording gauge station sited besides this non-recording one. Definitely, a similar station was delineated, firstly Thiessen polygon was used instead of station 7261 and then the results of applying two different ANN models (a feed forward back propagation multilayer perceptron (MLP) and a Radial Basis Function (RBF) network) were evaluated to disaggregate the data of this station, and the best disaggregation model was introduced