Evaluation of the k-nearest neighbor method for forecasting the influent characteristics of wastewater treatment plant

The k-nearest neighbor (k-NN) method was evaluated to predict the influent flow rate and four water qualities, namely chemical oxygen demand (COD), suspended solid (SS), total nitrogen (T-N) and total phosphorus (T-P) at a wastewater treatment plant (WWTP). The search range and approach for determining the number of nearest neighbors (NNs) under dry and wet weather conditions were initially optimized based on the root mean square error (RMSE). The optimum search range for considering data size was one year. The square root-based (SR) approach was superior to the distance factor-based (DF) approach in determining the appropriate number of NNs. However, the results for both approaches varied slightly depending on the water quality and the weather conditions. The influent flow rate was accurately predicted within one standard deviation of measured values. Influent water qualities were well predicted with the mean absolute percentage error (MAPE) under both wet and dry weather conditions. For the seven-day prediction, the difference in predictive accuracy was less than 5% in dry weather conditions and slightly worse in wet weather conditions. Overall, the k-NN method was verified to be useful for predicting WWTP influent characteristics.     

Mapping forest ecosystem functions for landscape planning in a mountain Natura2000 site,Central Italy

In last decades, numerous efforts have been carried out by the scientific community to assess the multifunctional role of forests in supporting the forest decision-making processes. Recently developed in Italy, the Forest Landscape Management Planning seems to be the most suitable tool to maintain the sustainability balance between forests and local populations. This research identifies and maps different forest ecosystem functions in a mountain Natura2000 Network site by performing a spatial estimation of forest inventory parameters, and implementing a multi-criteria and multi-level approach. Final results on mapping of forest functions show acceptable values of classification accuracy.