Hydrological modelling depends highly on the accuracy and uncertainty of model input parameters such as soil properties. Since most of these data are field surveyed, geostatistical techniques such as kriging, classification and regression trees or more sophisticated soil-landscape models need to be applied to interpolate point information to the area. Most of the existing interpolation techniqu...

The forecasting of energy consumption in China is a key requirement for achieving national energy security and energy planning. In this study, multi-variable linear regression (MLR) and support vector regression (SVR) were utilized with a gated recurrent unit (GRU) artificial neural network of Chinese energy to establish a forecasting model. The derived model was validated through four economic...

Assessment of vegetation water content is critical for monitoring vegetation condition, detecting plant water stress, assessing the risk of forest fires and evaluating water status for irrigation. The main objective of this study was to investigate the performance of various monoand multi-variate statistical methods for estimating vegetation water content (VWC) from hyper-spectral data. Hyper-s...

This working notes paper describes the system proposed by THU-HCSIL team for dynamic music emotion recognition. The procedure is divided into two module feature extraction and regression. Both feature selection and feature combination are used to form the final THU feature set. In regression module, a Booster-based Multi-level Regression method is presented, which outperforms the baseline signi...

BACKGROUND We present a novel feature selection algorithm, Winnowing Artificial Ant Colony (WAAC), that performs simultaneous feature selection and model parameter optimisation for the development of predictive quantitative structure-property relationship (QSPR) models. The WAAC algorithm is an extension of the modified ant colony algorithm of Shen et al. (J Chem Inf Model 2005, 45: 1024-1029)....

Model predictions of biogeochemical fluxes at the landscape scale are highly uncertain, both with respect to stochastic (parameter) and structural uncertainty. In this study 5 different models (LASCAM, LASCAM-S, a selfdeveloped tool, SWAT and HBV-N-D) designed to simulate hydrological fluxes as well as mobilisation and transport of one or several nitrogen species were applied to the mesoscale R...

Assessing the quality of a protein structure model is essential for protein structure prediction. Here, we developed a Support Vector Machine (SVM) method to predict the quality score (GDT-TS score) of a protein structure model from the features extracted from the sequence alignment used to generate the model. We developed a Support Vector Machine (SVM) model quality assessment method, taking e...

In this study, 27 polarimetric parameters were extracted from Radarsat-2 polarimetric synthetic aperture radar (SAR) at each growth stage of the rape crop. The sensitivity to growth parameters such as stem height, leaf area index (LAI), and biomass were investigated as a function of days after sowing. Based on the sensitivity analysis, five empirical regression models were compared to determine...

A generalized dynamic fuzzy neural network (GDFNN) was created to estimate heavy metal concentrations in rice by integrating spectral indices and environmental parameters. Hyperspectral data, environmental parameters, and heavy metal content were collected from field experiments with different levels of heavy metal pollution (Cu and Cd). Input variables used in the GDFNN model were derived from...