Reducing the impact of the siricid wasp, Sirex noctilio is crucial for the future productivity and sustainability of commercial pine resources in South Africa. In this study we present a machine learning model that serves as a spatial guide and allows forest managers to focus their existing detection and monitoring efforts on key areas and proactively adopt the most appropriate course of interv...

Random forests have proven to be reliable predictive algorithms in many application areas. Not much is known, however, about the statistical properties of random forests. Several authors have established conditions under which their predictions are consistent, but these results do not provide practical estimates of random forest errors. In this paper, we analyze a random forest model based on s...

Global solar radiation estimation is crucial for regional climate assessment and crop growth. Therefore, studies on the prediction of are emerging. With availability public data radiation, computerized models have been developed as well. These predictive play significant role in determining potentials regions suitable renewable energy generation required by engineering agricultural activities. ...

Recently, Wordle has become popular worldwide as a daily puzzle game launched by the New York Times. Players try to solve guessing five-letter word in six tries or less. According Wordle's statistical data, this paper first uses K-means algorithm cluster difficulty of solution words quantify English and analyzes accuracy scientificity clustering results. Then, Random Forest model classify into ...

Eucalyptus (Eucalyptus urograndis) production has significantly advanced over the past few years in Brazil, especially with regard to acreage and productivity. Machine learning made significant advances most varied fields of agrarian sciences. In this context, study aimed use physicochemical variables soil as well climatic dendrometric eucalyptus predict its height using random forest algorithm...