| dc.description.abstract | Most of the countries in the world face with
natural disasters like floods, cyclones, landslides and
droughts .Floods are major contributors to disruption of
human lives ,economy and property damage. Also it can be
strike with proper little warning or prediction. So, this
study proposes a novel flood prediction model for
Rathnapura district in Sri Lanka. Main reason for the flood
in Rathnaura town is Kalu River. Rainfall of five
meteorological stations namely Alupola station,
Hapugasthenna station, Guruluwana station, Lelopitiya
station and Rathnapura station are affected to the water
level of the Kalu River. The methodology of this study is
running under two main phases. In the first phase, K-mean
clustering is used to cluster the water level of the Kalu River
according to the rainfall of five meteorological stations. In
the second phase, the Artificial Neural Network (ANN)
model is successfully implemented for forecasting flood in
Rathnapura town according to the rainfall of above
mentioned five stations. Two model accuracy standards
were employed. Such as mean absolute error and meansquare error. The novel ANN model gives the minimum
error accuracies in both training and testing stages. Data
set for this study is obtained from Department of Irrigation,
Sri Lanka and it contains 1955 data. The new proposed
model is useful to avoid or minimize the social and
economic losses that may occur in the flood. | |
