Intelligent Remote Calibration Method for Cosmic Ray Sensor Using Supervised Machine Learning:A Comparative Study

In this paper a comparative study of supervised machine learning methods has been investigated online dynamic calibrate cosmic ray based bulk soil moisture sensor. Data collected from the Australian Water Availability Project (AWAP) database has been used as independent ground truth and the Hydroinnova CRS-1000 cosmic ray probe deployed in Tullochgorum, Australia was experimented for this study. Prediction performance of the five supervised artificial neural network (ANN) estimators, namely Sugano type Adaptive Neuro-Fuzzy Inference System (S-ANFIS), Multilayer Perceptron Neural Network (MLPNN), Elman Neural Network (ENN), Learning Vector Quantization Neural Network (LVQN) and Radial Basis Function Network (RBFN) were evaluated using various incremental training and testing paradigms to establish the best generalisation methodology to calibrate the probes remotely. AWAP trained five estimators was able to predict bulk soil moisture directly from cosmic ray neutron counts within the range of 74%-91% as best accuracies, whereas best sensitivity and specificity was 89% and 93%. These results proved that supervised artificial neural network based paradigm could be a valuable alternative calibration method for cosmic ray sensors against the current expensive and hydrological assumption based field calibration method.