Prediction of dissolved oxygen in harbours using artificial neural networks: an application to the port of Colombo

Abstract

Maintenance of high dissolved oxygen (DO) level in harbours is highly important as it could give rise to catastrophic effects if it is depleted affecting day- to- day port functions such as dredging activities and other maintenance work. The depletion of DO results not only in toxic gases such as methane and hydrogen sulfide but also in accumulation of wastes. Frequent monitoring of DO is therefore imperative, but makes practical difficulties due to ship movements and other activities. Hence, prediction of DO with an empirical model using Artificial Neural Networks (ANNs) was done with success with an application to the Port of Colombo (PoC). This model aims to lessen the frequency of monitoring DO and to foresee the responses of the system due to environmental changes. The performances of ANNs were compared with Multiple Linear Regression (MLR) . Monthly values of 14 water quality parameters at several depths for the period of four years from 1997 to year 2000 were collected. The values of weather parameters of rainfall and wind velocity for the corresponding period were also collected. The neural network possessing 7 inputs and 45 hidden neurons, performed well giving rise to correlation coefficient (R) as 0.87 and 0.67 for calibration and verification respectively. The inputs are temperature, depth and five rainfall intensities (including values on four immediate previous days). A sensitivity analysis was carried out to assess the potentials of small changes in each input on the neural network output. MLR model with 7 input variables indicated R to be 0.45 for calibration after several transformations. The temperature was the most influential variable among the ANN inputs affecting the output. In conclusion, it could be inferred that the ANN model is capable of predicting DO in PoC considerably well compared with MLR.