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Ítem Estimation of diurnal greenhouse gas (GHG) emissions from unfertilized coffee soils using recurrent neural networks (RNN). A case study for Chirinos, San Ignacio Province, Cajamarca, Peru(Clean Energy Science And Technology, 2025-12-10) Huaccha Castillo,Annick EstefanyGlobal warming, driven by rising greenhouse gas (GHG) concentrations, has agriculture as a major source of emissions. In coffee plantations, low sampling frequency and the absence of diurnal baselines introduce bias in emission estimates. The objective of this research was to estimate diurnal CO₂, N₂O, and CH₄ emissions from unfertilized coffee soils using recurrent neural networks (RNN). Gas fluxes were measured with a closed dynamic chamber (CDC) at 20-minute intervals between 8:00 and 18:00 over 22 days. For the estimation of GHG emissions, climatic data measured through a meteorological station were used, in addition to environmental parameters incorporated in the CDC. Five RNN models composed of two hidden layers of 20, 25, and 50 neurons were developed, trained, and validated for each GHG. Results indicate that N₂O contributed most to total emissions (734,689 ppm CO₂-eq), with CO₂ (237,579 ppm CO₂-eq) and CH₄ (215,426 ppm CO₂-eq) contributing less. Model performance was strong, with R² values of 0.98 (CO₂), 0.96 (N₂O), and 0.94 (CH₄). It is concluded that the RNNs proved to be reliable models for predicting GHG emissions in unfertilized coffee soils, with this study presenting a replicable framework with the potential to improve temporal estimation and reduce uncertainty in GHG inventories.Ítem Noise estimation using an artificial neural network in the urban area of Jaen, Cajamarca(Universidad Nacional de Jaén, 2024-03-31) Quiñones Huatangari,Lenin; Ocaña Zuñiga,CandyJaen is a city in constant urban growth which generates an increase in vehicular traffic and active noise pollution. The research presents the development of an artificial neural network (ANN) to estimate the noise produced by vehicular traffic in the urban area of the city. Consequently, information was collected from two investigations coded as T1 and T2, for which a matrix of 10 variables was elaborated with 210 and 273 data respectively. Random random sampling was performed to divide the data matrix into 80% (training) and 20% (validation). Weka software and the multi-layer perceptron (MLP) training algorithm were used to model the ANN. An ANN for T1 with 6-19-1 architecture and an ANN for T2 with 6-15-1 architecture were obtained. The performance of the ANNs was evaluated using the correlation coefficient (R), coefficient of determination (R2) and root mean square error (RMSE). The results show that the MLP networks are able to estimate the sound pressure level with values of R=0.9927, R2=0.9854 and RMSE=0.7313 for T1, R=0.9989, R2=0.9978, and RMSE=0.1515 for T2.