ALGORITMA GENETIK UNTUK MENINGKATKAN KINERJA MODEL TANGKI STANDAR PADA ANALISA TRANSFORMASI DATA HUJAN MENJADI DATA ALIRAN SUNGAI
Abstract: Fundamental weakness
of the tank model application is so much value parameters must first be defined
simultaneously before the model was applied. This condition causes tank models
are considered not efficient to solve practical problems. This research is an
attempt to improve the performance of Standard Tank Model that can be applied
more effectively, especially for the transformation of climate data into the
stream data. The discussion focused on efforts to complete the system of
equations in standard tank model using genetic algorithms for optimization
parameters, so that the resulting equation system can determine the appropriate
model parameters automatically at a watershed in the study. Standard tank model
is a system composed tank 4 series and has 17 parameters. Results of research
on the Konto Watershed and the Lekso Watershed show that Standard Tank
Model-based Genetic Algorithm can present relationships very well climate data
and streams data. At the maximum generation value of 500 obtained root mean
square error (RMSE) of 0.241 m3/sec for the Konto Watershed and the Lekso
Watershed of 0.30 m3/sec.
Penulis: Sulianto
Kode Jurnal: jptsipildd120077
