Estimation of Atmospheric Precipitable Water in Thailand using an Artificial Neural Network - Buntoung, Sumaman; Pariyothon, Jindarat; Detkhon, Patsakorn (Naresuan University Journal: Science and Technology (NUJST), 2020)
In this work, an Artificial Neural Network (ANN) was proposed to estimate monthly average precipitable water (PW) using ambient air relative humidity, ambient air temperature, saturated water vapour pressure and the order of the month as input data. The PW data measured from ground-based sunphotometer were used as output data. The multilayer perceptron ANN using the back propagation training algorithm with two hidden layers was employed for deriving the PW. A five-year period (2009-2013) of the data collected from four meteorological stations, namely Chiang Mai (18.98°N, 98.98°E), Ubon Ratchathani (15.25°N, 104.87°E), Bangkok (13.67°N, 100.60°E) and Songkhla (7.20°N, 100.60°E) were used to train the ANN. An independent two-year period (2014-2015) of the data from the same stations were used to evaluate the performance of the trained ANN model. The result shows that PW at the four stations derived from the ANN agrees well with those obtained from the measurement, with the discrepancy in terms of root mean square error (RMSE) and mean bias error (MBE) of 7.5% and -0.1%, respectively. Keywords: precipitable water, artificial neural network, back propagation algorithm