Non-Linear Autoregressive Dissolved Oxygen Prediction Model for Paddy Irrigation Channel
Non-Linear Autoregressive Dissolved Oxygen Prediction Model for Paddy Irrigation Channel
Author(s): Syafira Mohd Aisha, Norashikin M. Thamrin, Muhammad Fariq Ghazali, Nik Nor Liyana Nik Ibrahim, Megat Syahirul Amin Megat AliSubject(s): Agriculture
Published by: UIKTEN - Association for Information Communication Technology Education and Science
Keywords: Paddy; non-linear autoregressive; neural network; dissolved oxygen; Levenberg-Marquardt;
Summary/Abstract: This study has proposed a non-linear autoregressive model to predict one-day ahead dissolved oxygen in paddy field irrigation channel. A 32-day data is obtained from Kampung Padang To’ La in Pasir Mas, Kelantan using off-the shelf water quality parameter sensors. Analysis has revealed no correlation between dissolved oxygen with pH and electrical conductivity. A non-linear autoregressive model is then developed using the dissolved oxygen measurements and artificial neural network. A prediction model developed using Levenberg- Marquardt algorithm yielded the best results with overall regression of 0.9253. The model has also passed all correlation tests and can therefore, be accepted.
Journal: TEM Journal
- Issue Year: 11/2022
- Issue No: 2
- Page Range: 842-850
- Page Count: 9
- Language: English
