A Comparative Study of Machine Learning Regression Methods on LiDAR Data: A Case Study

Light Detection and Ranging (LiDAR) is a remote sensor able to extract vertical information from sensed objects. LiDAR-derived information is nowadays used to develop environmental models for describing fire behaviour or quantifying biomass stocks in forest areas. A multiple linear regression (MLR) with previous stepwise feature selection is the most common method in the literature to develop LiDAR-derived models. MLR defines the relation between the set of field measurements and the statistics extracted from a LiDAR flight. Machine learning has recently been paid an increasing attention to improve classic MLR results. Unfortunately, few studies have been proposed to compare the quality of the multiple machine learning approaches. This paper presents a comparison between the classic MLR-based methodology and common regression techniques in machine learning (neural networks, regression trees, support vector machines, nearest neighbour, and ensembles such as random forests). The selected techniques are applied to real LiDAR data from two areas in the province of Lugo (Galizia, Spain). The results show that support vector regression statistically outperforms the rest of techniques when feature selection is applied. However, its performance cannot be said statistically different from that of Random Forests when previous feature selection is skipped

Measurement of the forward energy flow in pp collisions at &#8730;<span style="text-decoration:overline">s</span>=7 TeV

The energy flow created in pp collisions at s√=7 TeV is studied within the pseudorapidity range 1.9&#60;η&#60;4.9 with data collected by the LHCb experiment. The measurements are performed for inclusive minimum-bias interactions, hard scattering processes and events with an enhanced or suppressed diffractive contribution. The results are compared to predictions given by Pythia-based and cosmic-ray event generators, which provide different models of soft hadronic interactions