See more from this Session: General Biometry & Statistical Computing: II
The inputs for PTF range from inexpensive soil texture related limited data (e.g. silt, sand, clay contents) to detailed particle size distribution, as well as other expensive and inexpensive data. An understanding of relative importance of inputs for SHP prediction contributes towards better ANN PTF model development. One noted practice in ANN PTF literature for identifying the importance of inputs is the use of stepwise model development. We reviewed other alternative approaches in ANN literature that can be potentially used for identifying the importance of inputs. In addition, we compare a few popular neural network learning methods in Matlab environment (e.g. Levenberg Marquardt “trainlm”, bayesian regularized “trainbr”, etc.) under data limited condition. ANN developed with bayesian regularization provided better solution compared to others under such situation. Finally, we introduced an alternative approach for bias correction that can be applied within ANN modeling framework.