Improved weighted ensemble learning for predicting the daily reference evapotranspiration under the semi-arid climate conditions.

Evapotranspiration is an important quantity required in many applications, such as hydrology and agricultural and irrigation planning. Reference evapotranspiration is particularly important, and the prediction of its variations is beneficial for analyzing the needs and management of water resources. In this paper, we explore the predictive ability of hybrid ensemble learning to predict daily reference evapotranspiration (RET) under the semi-arid climate by using meteorological datasets at 12 locations in the Andalusia province in southern Spain. The datasets comprise mean, maximum, and minimum air temperatures and mean relative humidity and mean wind speed. A new modified variant of the grey wolf optimizer, named the PRSFGWO algorithm, is proposed to maximize the ensemble learning's prediction accuracy through optimal weight tuning and evaluate the proposed model's capacity when the climate data is limited. The performance of the proposed approach, based on weighted ensemble learning, is compared with various algorithms commonly adopted in relevant studies. A diverse set of statistical measurements alongside ANOVA tests was used to evaluate the predictive performance of the prediction models. The proposed model showed high-accuracy statistics, with relative root mean errors lower than 0.999% and a minimum R2 of 0.99. The model inputs were also reduced from six variables to only two for cost-effective predictions of daily RET. This shows that the PRSFGWO algorithm is a good RET prediction model for the semi-arid climate region in southern Spain. The results obtained from this research are very promising compared with existing models in the literature.

Solar radiation estimated from empirical models for the north of Minas Gerais, Brazil / Radiação solar estimada por modelos empíricos para o norte de Minas Gerais, Brasil

Estimating daily solar radiation (Rs) provides an important alternative in situations where it cannot be measured by conventional pyranometers. This study used meteorological data from nine cities in the north of the Minas Gerais state, Brazil, for the period from 2008 to 2010 with the aim of evaluate the accuracy and applicability of some simple models to help regions where Rs is impossible to be measured. Five models were evaluated for their estimates of Rs based on simple available data. For each city studied, the equations were previously calibrated. Meteorologically based empirical models to estimate daily global solar radiation are an appropriate tool if the parameters can be calibrated for different locations. These models have the advantage of using meteorological data, which are commonly available. Based on the overall results, we conclude that the accuracy of estimation by available meteorological data is acceptable and comparable with the accuracy of classical models. Considering the greater availability of air temperature data and application in studies that do not require great accuracy in estimating Rs, all models were adequate for use. The accuracy of Rs was only slightly improved by adding rainfall records as input variable. Therefore, in the region studied, the choice of simpler models, having as input the daily maximum and minimum air temperature would not imply large error in the estimates. For most sites, Bristow and Campbell model had the best estimate of Rs with a RMSE of 2.69 MJ m-2 and R2= 0.69, with the possibility to calibrate with available temperature data, becoming a practical and reliable model. Hargraves model should be avoided due to its lower performance compared to the other models applied.

A estimativa da radiação solar diária (Rs) fornece uma alternativa importante em situações que não pode ser medida por piranômetros convencionais. O estudo utilizou dados meteorológicos de nove cidades do Norte do estado de Minas Gerais, Brasil, durante o período de 2008 a 2010, com o objetivo de mensurar a precisão e aplicabilidade de modelos empíricos simples nas regiões onde a Rs não pode ser medida . Cinco modelos foram avaliados para estimar Rs com base nos dados meteorológicos disponíveis. As equações foram previamente calibradas para cada município estudado. Modelos meteorológicos empíricos que estimam a radiação solar diária são ferramentas adequadas desde que os parâmetros sejam calibrados para os diferentes locais a serem utilizados. Estes modelos têm a vantagem de utilizar dados meteorológicos, que estão comumente disponíveis. Todos os modelos foram considerados adequados para o uso, considerando-se a maior disponibilidade de dados de temperatura do ar e aplicação em estudos que não exigem grande precisão na estimativa da Rs. A precisão da Rs apenas foi melhorada pela adição de registros de precipitação como variável de entrada. Assim, na região estudada, a escolha de um modelo mais simples, tendo como entrada a temperatura mínima e máxima do ar diária, não implica um grande erro na estimativa. Para a maioria das regiões, o modelo de Bristow e Campbell teve a melhor estimativa da Rs com um RMSE de 2.69 MJ m-2 e R2= 0.69, e a possibilidade de calibração com os dados de temperatura disponíveis, tornando-se um modelo prático e confiável. O modelo de Hargraves deve ser evitado devido seu pior desempenho comparado aos outros modelos propostos.

Dynamics of pruning waste and spent horse litter co-composting as determined by chemical parameters.

Co-composting of pruning waste and horse manure was monitored by different parameters. A windrow composting pile, having the dimensions 2.5m (height) x 30m (length) was established. The maturation of pruning waste and horse manure compost was accompanied by a decline in NH(4)(+)-N concentration, water soluble C and an increase in NO(3)(-)-N content. Organic matter (OM) content during composting followed a first-order kinetic equation. This result was in agreement with the microbiological activity measured by the CO(2) respiration during the process. The correlation at a high level of probability found between the OM loss and CO(2) evolution showed that both parameters could be used to indicate the degree of OM degradation that is the maturity and stability phases of the compost studied. Humification parameters data from the organic matter fractionation did not show a clear tendency during the composting time, suggesting that these parameters are not suitable for evaluating the dynamics of the process.