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 sulphide but also in accumulation of wastes. Frequent monitoring of DO is therefore imperative, but creates 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 reduce 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 a particular period were collected. The values of weather parameters of rainfall and wind velocity for the corresponding period were also collected. The inputs of the best model were temperature, depth and five rainfall intensities (including rainfall 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 the same number of input variables indicated a low value for R after several transformations. The rainfall intensity of the 3rd previous day 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.