Soil erosion after forest fires: evaluation of mitigation measures applied to drainage channels in the northwest of Portugal

The development and implementation of measures which promote the reduction of the impacts of forest fires on soils is imperative and should be part of any strategy for forest and soil preservation and recovery, especially considering the actual scenario of continuous growth in the number of fires and burnt area. Consequently, with the dendrocaustologic reality that has characterized the Portuguese mainland in recent decades, a research project promoted by the Center for the Study of Geography and Spatial Planning (CEGOT) was implemented with the objective of applying several erosion mitigation measures in a burned area of the Peneda-Geres National Park in NW Portugal. This paper therefore seeks to present the measures applied in the study area within the project Soil Protec, relating to triggered channel processes and the results of preliminary observations concerning the evaluation of the effectiveness of erosion mitigation measures implemented, as well as their cost/benefit ratio.

Performance of state space and ARIMA models for consumer retail sales forecasting

Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts.