Prediction of IoT Traffic Using the Gated Recurrent Unit Neural Network- (GRU-NN-) Based Predictive Model

Prediction of IoT traffic in the current era has attracted noteworthy attention to utilize the bandwidth and channel capacity optimally. In this paper, the problem of IoT traffic prediction has been studied, and solutions have been proposed by using machine learning method ARIMA and learning time series algorithms such as LSTM and gated recurrent unit (GRU-NN) based on neural networks. The proposed GRU-NN predicts the traffic on the basis of transfer learning. The advantage of the GRU-NN over LSTM is also highlighted by solving the problem of gradient disappearance. The proposed GRU-NN memorizes the traffic characteristics of the IoT environment for a long time which eventually helps the system to forecast the upcoming traffic from the existing traces of the traffic. The proposed GRU-NN makes use of the transfer learning technique to handle the problem of insufficient IoT traffic data along with the gradient boosting training method for achieving better accuracy in predicting the network traffic in the IoT environment. The results reveal that the proposed GRU-NN model outperforms the other traffic predictors in terms of statistical performance evaluation parameters such as MAE, RMSE, MRE, and MSE. The results show that the GRU-NN provides the most accurate predictions followed by the LSTM predictor and then ARIMA and other approaches taken up for the comparative study.

Effect of local calibration on the performance of the Hargreaves reference crop evapotranspiration equation

Obtaining accurate estimates of reference crop evapotranspiration (ET0) using limited climatic inputs is essential in data-short situations where the preferred FAO-56 Penman–Monteith (PM) equation cannot be implemented. Among several available for ET0 estimation, the empirical temperature-based Hargreaves–Samani (HG) equation remains a popular alternative. However, accurate HG estimates can be obtained by local calibration and replacing the mean daily temperature with the effective daily temperature. Therefore, the present study was taken up to evaluate the effects of site-specific calibration of model parameters and the use of effective air temperature on the accuracy of ET0 estimates by the HG model. For this purpose, climate records for the historical period 2006–2016 of 67 stations located across 10 agro-climatic zones of Karnataka State, India, were used and the analysis was carried out using a monthly time step. Calibration and statistical performance evaluation was performed using FAO-56 PM ET0 estimates as a reference. Overall results showed significant improvement in HG estimates across all zones with the use of locally calibrated parameters, whereas the use of effective air temperature did not lead to any significant gain in prediction accuracies. The derived information on the spatial distribution of calibrated parameters will help obtain accurate ET0 estimates with only air temperature inputs.

DINÂMICA E ESTIMATIVAS DA TEMPERATURA E UMIDADE RELATIVA DO AR EM VIVEIROS PROTEGIDOS COM DIFERENTES SOMBREAMENTOS

Objetivou-se descrever a dinâmica diária da temperatura (Tar) e umidade relativa do ar (UR) em ambientes protegidos com diferentes telas poliefinas de sombreamento, na região de transição Cerrado-Amazônia do Mato Grosso; ademais, foram avaliados os usos de regressões de estimativa de Tar e UR nos ambientes sombreados com base nas mesmas variáveis medidas em pleno sol. As avaliações micrometeorológicas foram realizadas em viveiros florestais modulares suspensos, alinhados no sentido Leste-Oeste, entre junho de 2017 e abril de 2019, sob telas pretas com níveis crescentes de sombreamento (35, 50, 65 e 80%) e coloridas/espectrais (termorefletora, vermelha, azul e verde, todas com 50% de sombreamento). Os dados do monitoramento micrometeorológico foram agrupados em função das estações hídricas regionais (seca, seca-chuvosa, chuvosa e chuvosa-seca), com separação da base de dados por decêndios. Foram empregados 70 e 30% dos dados para geração e validação das regressões, em cada agrupamento de dados, respectivamente. Na avaliação do desempenho estatístico das regressões foram empregados os indicadores estatísticos: coeficiente de determinação (R2), erro absoluto médio (MBE), raiz quadrada do erro quadrático médio (RMSE) e índice de Willmott (d). Houve dinâmica similar de Tar e UR entre a condição de pleno sol e as telas poliefinas pretas e coloridas ao longo do dia e do ano; porém, com aumento expressivo nos valores médios da Tar e UR nesses ambientes protegidos. Os valores de R² foram satisfatórios, demonstrando que mais de 60% da variável dependente (Tar nas telas de sombreamento) está relacionada à variável independente (Tar na condição de pleno sol). O d variou de 0,96 a 0,99, indicando que as regressões de estimativas da Tar e UR ajustadas apresentam desempenho satisfatório para todas as estações hídricas regional nos ambientes sombreados. Palavras-chave: ambientes protegidos; micrometeorologia; indicadores estatísticos; transição Cerrado-Amazônia. Dynamics and estimates of air temperature and relative humidity in nurseries protected with different shading ABSTRACT: The objective was to describe the daily dynamics of temperature (Tar) and relative humidity (RH) in protected environments with different polyolefin shading screens in transition region of Cerrado-Amazonia of Mato Grosso; in addition, the uses regressions of estimation of Tar and RH in shaded environments based on the same variables measured in full sun were evaluated. Micrometeorological assessments were performed in suspended modules forest nurseries, aligned to the East-West direction, between June 2017 and April 2019, under black screens with increasing levels of shading (35, 50, 65 and 80%) and colored / spectral (thermo-reflector, red, blue and green, all with 50% shading). The data of the micrometeorological monitoring were grouped according to the regional water stations (dry, dry-rainy, rainy and rainy-dry), with separation of the database for ten years. 70 and 30% of the data were used to generation and validation the regressions, in each data group, respectively. In the Statistical performance evaluation of the regressions were used the statistical indicators: coefficient of determination (R2), the mean error (MBE), root mean square error (RMSE) and Willmott's index of adjustment (d). There was similar dynamic of Tar and UR between the condition of full sun and the black and colored polyolefin screens throughout the day and year; however, with a significant increase in the mean values of Tar and UR in these protected environments. The R² values were satisfactory, showing that more than 60% of the dependent variable (Tar in the shading screens) is related to the independent variable (Tar in full sun). The d ranged from 0.96 to 0.99, indicating that the adjusted regressions of Tar and UR present satisfactory performance for all regional water stations in shaded environments. Palavras-chave: protected environments; micrometeorology; statistical indicators; Cerrado-Amazônia transition.

Statistical Performance Evaluation of Various Metaheuristic Scheduling Techniques for Cloud Environment

Cloud computing has become the need of the hour as almost all businesses have started using the pay per use model proposed by cloud architecture instead of buying their own resources. Scheduling tasks to these sharable resources is a critical aspect of cloud computing and an area which is attracting many researchers. Scheduling workflows on a cloud architecture becomes even more critical as it contains a set of dependant tasks, and is considered an NP-hard problem. In this paper, various traditional meta-heuristic scheduling techniques have been implemented and their performance has been evaluated based on two parameters, Flowtime and Makespan. The various algorithms like PSO, DE, ETC, ABC, GA and FFOA are implemented using CloudSim and their performance is statistically evaluated in order to obtain minimized Flowtime and Makespan.

ÁREA DO LIMBO FOLIAR DA CASTANHEIRA-DO-BRASIL POR MEDIDAS LINEARES

Neste trabalho foram gerados e validados modelos de estimativa da área da folha da espécie Bertholletia excelsa Bonpl. (castanheira-do-Brasil) a partir das medidas lineares do limbo foliar. Foram coletadas 1500 folhas em diferentes posições da copa de árvores adultas e jovens (em função do ciclo reprodutivo), sendo usadas 1000 e 500 folhas para calibração e validação estatísticas, respectivamente. Foram obtidos como medidas do limbo da folha: comprimento (C), largura (no centro da folha, na base e no ápice) e a área foliar real (AFR). A avaliação do desempenho estatístico (validação) foi realizada pelos indicativos erro médio (MBE), raiz quadrática do erro médio (RMSE) e índice de ajustamento de Willmott (dW). Apenas a medida de largura no centro da folha e do comprimento são suficientes para estimar a área da folha de B. excelsa., que pode ser dada pela equação AF = {0,8743*{(C*L)0,9790]}-1,84, independentemente da posição da folha e da idade planta. Palavras-chave: Bertholletia excelsa; análise de regressão; indicativos estatísticos. FIELD OF THE LIMBO FOLIAR OF CASTANHEIRA-DO-BRASIL WITH LINEAR MEASURES ABSTRACT: In this work, models of estimation of the leaf area of the species Bertholletia excelsa Bonpl. (Brazil nut) from the linear measurements of the leaf blade. 1500 leaves were collected in different positions of the crown of adult and young trees (depending on the reproductive cycle), using 1000 and 500 leaves for calibration and statistical validation, respectively. Leaf length (C), width (at the center of the leaf, at the base and at the apex) and leaf area (AFR) were obtained as measures of leaf limb. Statistical performance evaluation (validation) was performed using the mean error (MBE), root mean square error (RMSE) and Willmott's index of adjustment (dW). Only the width measure at the center of the leaf and the length are sufficient to estimate the area of the leaf of B. excelsa., Which can be given by the equation AF = {0.8743 * {(C * L) 0.9790]} -1.84, regardless of leaf position and plant age.Keywords: Bertholletia excelsa; regression analysis; statistical indicatives.

Comparison study of artificial intelligence method for short term groundwater level prediction in the northeast Gachsaran unconfined aquifer

Accurate and reliable groundwater level prediction is an important issue in groundwater resource management. The objective of this research is to compare groundwater level prediction of several data-driven models for different prediction periods. Five different data-driven methods are compared to evaluate their performances to predict groundwater levels with 1-, 2- and 3-month lead times. The four quantitative standard statistical performance evaluation measures showed that while all models could provide acceptable predictions of groundwater level, the least square support vector machine (LSSVM) model was the most accurate. We developed a set of input combinations based on different levels of groundwater, total precipitation, average temperature and total evapotranspiration at monthly intervals. For each model, the antecedent inputs that included Ht-1, Ht-2, Ht-3, Tt, ETt, Pt, Pt-1 produced the best-fit model for 1-month lead time. The coefficient of determination (R2) and the root mean square error (RMSE) were calculated as 0.99%, 1.05 meters for the train data set, and 95%, 2.3 meters for the test data set, respectively. It was also demonstrated that many combinations the above-mentioned approaches could model groundwater levels for 1 and 2 months ahead appropriately, but for 3 months ahead the performance of the models was not satisfactory.

MODELOS ALOMÉTRICOS PARA ESTIMAR A ÁREA DO LIMBO FOLIAR DE TECA

Objetivou-se neste trabalho gerar e validar 16 modelos simplificados para estimar a área do limbo foliar de árvores de Tectona grandis L. Foram coletadas folhas de árvores adultas em plantios homogêneos e em matrizes isoladas, nos estratos superior, médio e basal das copas, totalizando 1800 folhas. A área foliar real foi determinada usando o integrador de área foliar “Area Meter” (LI-3100C). Nos modelos de estimativa, considerou-se a área foliar como variável dependente, massa seca (MS) e as dimensões lineares da folha (comprimento – C e largura do meio da folha - L) como variáveis independentes. Para calibração e validação estatística, utilizou-se 70% e 30% das folhas, nesta ordem. Na avaliação do desempenho estatístico (validação) empregou-se o erro médio (MBE), raiz quadrática do erro médio (RMSE) e índice de ajustamento de Wilmott (dw). Empregou-se o método dos valores ponderados dos indicativos estatísticos (Vp) para definir qual a melhor modelo. Os modelos que empregam medidas conjuntas de C e L proporcionam melhores estimativas da área do limbo foliar de T. grandis, sendo indicado o modelo AF = 0,5776 C*L, que apresenta superestimava de 13,98 cm², espalhamento de 61,99 cm² e ajustamento de 0,99. Considerando a massa seca, recomenda-se o modelo AF = 91,9164 MS.Palavras-chave: Tectona grandis L.; indicativos estatísticos; morfometria foliar. ALLOMETRIC EQUATIONS FOR LEAF BLADE AREA ESTIMATION OF TEAK ABSTRACT: The objective of this work was to generate and validate 16 simplified models to estimate the leaf limb area of Tectona grandis L. trees. Leaves of adult trees were collected at homogeneous plantations and isolated matrices, in the upper, middle and basal strata of the crowns totalizing 1800 sheets. The actual leaf area was determined using the “Area Meter” leaf area integrator (LI-3100C). In the estimation models, the leaf area was considered as a dependent variable, dry mass and leaf linear dimensions (length - C and width of the middle leaf - L) as independent variables. For calibration and statistical validation, 70% and 30% of the leaves were used in this order. In the statistical performance evaluation (validation) we used the mean error (MBE), quadratic root mean error (RMSE) and Wilmott adjustment index (dw). We used the method of weighted values of statistical codes (Vp) to define the best model. Models employing C and L joint measurements provide better estimates of T. grandis leaf limb area, with the AF = 0.5776 C * L model being overestimated 13.98 cm², scattering 61.99. cm² and adjustment of 0.99. Considering the dry mass, the model AF = 91.9164 MS is recommended.Keywords: Tectona grandis L.; statistics indicatives; leaf morphometry.