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Parametric and nonparametric modeling methods have been widely used for the estimation of forest attributes from airborne laser-scanning data and aerial photographs. However, the methods adopted suffered from complex remote-sensed data structures involving high dimensions, nonlinear relationships, different statistical distributions, and outliers. In this context, artificial neural networks (ANNs) are of interest as they have many clear benefits over conventional modeling methods and could then enhance the accuracy of current forest-inventory methods. This paper examines the ability of common ANN modeling techniques for the prediction of species-specific forest attributes, as exemplified here with the prediction stem volumes (cubic meters per hectare) at the field plot and forest stand levels. Three modeling methods were evaluated, namely, the multilayer perceptron (MLP), support vector regression (SVR), and self-organizing map, and intercompared with the corresponding nonparametric k most similar neighbor method using cross-validated statistical performance indexes. To decrease the number of model-input variables, a multiobjective input-selection method based on genetic algorithm is adopted. The numerical results obtained in the study suggest that ANNs are appropriate and accurate methods for the assessment of species-specific forest attributes, which can be used as alternatives to multivariate linear regression and nonparametric nearest neighbor models. Among the ANN models, SVR and MLP provide the best choices for prediction purposes as they yielded high prediction accuracies for species-specific tree volumes throughout.

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Please cite this article as: Pekkonen M, Du L, Skön J-P, Raatikainen M, Haverinen-Shaughnessy U, The influence of tenure status on housing satisfaction and indoor environmental quality in Finnish apartment buildings, Building and Environment (2015), doi: 10.1016/j.buildenv.2015 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPTBased on a previous national scale housing and health questionnaire survey, we observed significant differences in many housing quality attributes by dwelling types and tenure status.Respondents living in apartment buildings and rental houses reported being less satisfied with their housing conditions than respondents living in owner-occupied apartments or houses inFinland. In this subsequent work, we aim to study the associations between tenure status and housing satisfaction among respondents living in apartment buildings (N=397). Further on, we used measurement data collected from 28 apartments in six buildings to determine if the differences in housing satisfaction could be related to objectively measured indoor environmental quality indicators: indoor temperature, relative humidity, and carbon dioxide concentrations. Based on the results, the respondents from rental flats were significantly more unlikely to be satisfied with their dwelling, and to report their dwellings suitable warm in winter than the respondents from owner-occupied flats. Based on the measurement data, small differences were observed in thermal conditions by tenure status, however, a large portion of all apartments appeared to be overheated, and only one apartment experienced room temperatures below 18 o C during winter. In conclusion, there were large differences between occupant self-reported satisfaction and thermal comfort by tenure status, but differences in measured parameters were relatively small. The results indicate that occupant characteristics are likely to explain majority of differences by tenure status, which should be taken into account when assessing the overall relationships between housing and health.