COMPARAÇÃO ENTRE MÉTODOS DE INTERPOLAÇÃO ESPACIAL PARA A ESTIMATIVA DA DISTRIBUIÇÃO DE PRECIPITAÇÃO NO CEARÁ-BRASIL

COMPARAÇÃO ENTRE MÉTODOS DE INTERPOLAÇÃO ESPACIAL PARA A ESTIMATIVA DA DISTRIBUIÇÃO DE PRECIPITAÇÃO NO CEARÁ-BRASIL DANILO BATISTA NOGUEIRA1; ALEXSANDRO OLIVEIRA DA SILVA 2 E ANA PAULA NUNES DA SILVA 3 1 Departamento de Engenharia Agrícola, Universidade Federal do Ceará, Avenida Mister Hull, S/N-Campus do Pici, Centro de Ciências Agrárias, Bloco 804, CEP 60455-760, Fortaleza, Ceará, Brasil, [email protected] 2 Departamento de Engenharia Agrícola, Universidade Federal do Ceará, Avenida Mister Hull, S/N-Campus do Pici, Centro de Ciências Agrárias, Bloco 804, CEP 60455-760, Fortaleza, Ceará, Brasil, [email protected] 3 Departamento de Engenharia Agrícola, Universidade Estadual do Amapá, Avenida Presidente Vargas, N 650, Centro de Ciências Agrárias, CEP 68906-970, Macapá, Amapá, Brasil, [email protected] 1 RESUMO A distribuição espacial de precipitação ainda é largamente representada por métodos geoestatísticos de interpolação e em áreas de semiárido necessita de estudos recorrentes por conta de sua variabilidade temporal e espacial. Diante disto, visando o aperfeiçoamento dos métodos de interpolação e o desenvolvimento de mapas no estado do Ceará, o presente trabalho teve como objetivo analisar a acurácia de cinco métodos de interpolação: Inverso do Quadrado da Distância (IQD), Krigagem com um semivariograma esférico, Krigagem com um semivariograma exponencial, Vizinho natural e Spline regularizada. Para isto foram utilizados dados de precipitação média anual de um período de vinte anos (1991 a 2010) de 252 postos pluviométricos. Como forma de determinação do método mais adequado optou-se pela técnica de validação cruzada como critério de comparação, determinando o erro médio quadrático (RMSE), o coeficiente de determinação (r²), o coeficiente de correlação de Pearson (r), o índice de concordância (d) e o índice de desempenho (c) entre os dados estimados e os dados observados. Os resultados apontam que a interpolação por Krigagem exponencial apresentou critério de desempenho classificado como bom e um menor erro quadrático médio (164,09 mm), mostrando ser esse o interpolador de melhor representatividade espacial para o conjunto de dados. Keywords: geoestatística, postos pluviométricos, variáveis regionalizadas. NOGUEIRA, D. B.; SILVA, A. O.; SILVA, A. P. N. COMPARISON OF SPATIAL INTERPOLATION METHODS FOR THE ESTIMATION OF PRECIPITATION DISTRIBUTION IN CEARÁ -BRAZIL 2 ABSTRACT The spatial distribution of precipitation is still largely represented by geostatistical methods of interpolation and in semiarid areas it requires recurrent studies due to their temporal and spatial variability. Therefore, aiming at the improvement of interpolation methods and the improvement of rainfall maps in the state of Ceará, this study aimed to analyze the accuracy of five interpolation methods: Inverse of Square Distance (ISD), Kriging with a spherical semivariogram model, Kriging with a exponential semivariogram model, Natural neighbor and Spline regularized. For that, data of annual average rainfall of a period of twenty years (1991 to 2010) of 252 rain gauges were used. As a form of evaluation and determination of the most appropriate method, the technique of cross-validation was chosen as the criterion of comparison, determining the root mean square error (RMSE), coefficient of determination (r²), Pearson correlation coefficient (r), concordance index (d) and performance index (c) between the estimated data and the observed data. The results show that the interpolation by exponential Kriging method presented performance criterion classified as good and lower mean square error (164,09), showing that it is the interpolator of better spatial representation for the data set under study. Keywords: statistical, rain gauges, regionalized variables.

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Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-210066 ◽

2021 ◽

Vol 25 (2) ◽

pp. 227-234

Author(s):

Sandeep Samantaray ◽

Abinash Sahoo

Keyword(s):

Neural Network ◽

Mean Square Error ◽

Water Table ◽

Environmental Sustainability ◽

Model Development ◽

Water Table Depth ◽

Water Levels ◽

Coefficient Of Determination ◽

Mean Square ◽

Inference System

Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.

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IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

Sensors ◽

10.3390/s21051631 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1631

Author(s):

Bruno Guilherme Martini ◽

Gilson Augusto Helfer ◽

Jorge Luis Victória Barbosa ◽

Regina Célia Espinosa Modolo ◽

Marcio Rosa da Silva ◽

...

Keyword(s):

Technology Acceptance ◽

Mean Square Error ◽

Ease Of Use ◽

Coefficient Of Determination ◽

Square Root ◽

Mean Square ◽

Use Context ◽

The Mean ◽

Acceptance Model ◽

The Internet Of Things

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.

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Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽

10.3390/pr9071166 ◽

2021 ◽

Vol 9 (7) ◽

pp. 1166

Author(s):

Bashir Musa ◽

Nasser Yimen ◽

Sani Isah Abba ◽

Humphrey Hugh Adun ◽

Mustafa Dagbasi

Keyword(s):

Support Vector Regression ◽

Mean Square Error ◽

Prediction Accuracy ◽

Demand Forecasting ◽

Load Forecasting ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Mean Square ◽

Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

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A MODIFIED RATIO TYPE ESTIMATOR OF FINITE POPULATION MEAN UNDER STRATIFIED RANDOM SAMPLING SCHEME

10.36106/4917049 ◽

2021 ◽

pp. 58-60

Author(s):

Naziru Fadisanku Haruna ◽

Ran Vijay Kumar Singh ◽

Samsudeen Dahiru

Keyword(s):

Mean Square Error ◽

Random Sampling ◽

Minimum Mean Square Error ◽

Real Life ◽

Population Data ◽

Auxiliary Variable ◽

Mean Square ◽

Data Set ◽

Population Mean ◽

The Mean

In This paper a modied ratio-type estimator for nite population mean under stratied random sampling using single auxiliary variable has been proposed. The expression for mean square error and bias of the proposed estimator are derived up to the rst order of approximation. The expression for minimum mean square error of proposed estimator is also obtained. The mean square error the proposed estimator is compared with other existing estimators theoretically and condition are obtained under which proposed estimator performed better. A real life population data set has been considered to compare the efciency of the proposed estimator numerically.

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Observed and estimated leaf appearance of landrace and improved maize cultivars

Ciência Rural ◽

10.1590/0103-8478cr20160775 ◽

2017 ◽

Vol 48 (1) ◽

Author(s):

Josana Andreia Langner ◽

Nereu Augusto Streck ◽

Angelica Durigon ◽

Stefanía Dalmolin da Silva ◽

Isabel Lago ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Sowing Date ◽

Mean Square ◽

Data Set ◽

Growing Seasons ◽

Maize Cultivars ◽

Maize Varieties ◽

Leaf Appearance

ABSTRACT: The objective of this study was to compare the simulations of leaf appearance of landrace and improved maize cultivars using the CSM-CERES-Maize (linear) and the Wang and Engel models (nonlinear). The coefficients of the models were calibrated using a data set of total leaf number collected in the 11/04/2013 sowing date for the landrace varieties ‘Cinquentinha’ and ‘Bico de Ouro’ and the simple hybrid ‘AS 1573PRO’. For the ‘BRS Planalto’ variety, model coefficients were estimated with data from 12/13/2014 sowing date. Evaluation of the models was with independent data sets collected during the growing seasons of 2013/2014 (Experiment 1) and 2014/2015 (Experiment 2) in Santa Maria, RS, Brazil. Total number of leaves for both landrace and improved maize varieties was better estimated with the Wang and Engel model, with a root mean square error of 1.0 leaf, while estimations with the CSM-CERES-Maize model had a root mean square error of 1.5 leaf.

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Predictive Algorithm for Handover Decisions between LTE and LTE-A Networks

Australian Journal of Telecommunications and the Digital Economy ◽

10.18080/jtde.v9n4.370 ◽

2021 ◽

Vol 9 (4) ◽

pp. 110-126

Author(s):

Wafa Benaatou ◽

Adnane Latif ◽

Vicent Pla

Keyword(s):

Power Consumption ◽

Mean Square Error ◽

Fuzzy Inference ◽

Hybrid Approach ◽

Mobile Terminal ◽

Base Station ◽

Coefficient Of Determination ◽

Mean Square ◽

Statistical Parameters ◽

Inference System

A heterogeneous wireless network needs to maintain seamless mobility and service continuity; for this reason, we have proposed an approach based on the combination of particle swarm optimization (PSO) and an adaptive neuro-fuzzy inference system (ANFIS) to forecast a handover during a movement of a mobile terminal from a serving base station to target base station. Additionally, the handover decision is made by considering several parameters, such as peak data rate, latency, packet loss, and power consumption, to select the best network for handover from an LTE to an LTE-A network. The performance efficiency of the new hybrid approach is determined by computing different statistical parameters, such as root mean square error (RMSE), coefficient of determination (R2), mean square error (MSE), and error standard deviation (StD). The execution of the proposed approach has been performed using MATLAB software. The simulation results show that the hybrid PSO-ANFIS model has better performance than other approaches in terms of prediction accuracy and reduction of handover latency and the power consumption in the network.

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Authentication of Patin Fish Oil (Pangasius micronemus) using FTIR Spectroscopy Combined with Chemometrics

Indonesian Journal of Chemometrics and Pharmaceutical Analysis ◽

10.22146/ijcpa.487 ◽

2020 ◽

pp. 23

Author(s):

Anggita Rosiana Putri ◽

Abdul Rohman ◽

Sugeng Riyanto ◽

Widiastuti Setyaningsih

Keyword(s):

Root Mean Square Error ◽

Fish Oil ◽

Ftir Spectroscopy ◽

Mean Square Error ◽

Root Mean Square ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square

Authentication of Patin fish oil (MIP) is essential to prevent adulteration practice, to ensure quality, nutritional value, and product safety. The purpose of this study is to apply the FTIR spectroscopy combined with chemometrics for MIP authentication. The chemometrics method consists of principal component regression (PCR) and partial least square regression (PLSR). PCR and PLSR were used for multivariate calibration, while for grouping the samples using discriminant analysis (DA) method. In this study, corn oil (MJ) was used as an adulterate. Twenty-one mixed samples of MIP and MJ were prepared with the adulterate concentration range of 0-50%. The best authentication model was obtained using the PLSR technique using the first derivative of FTIR spectra at a wavelength of 650-3432 cm-1. The coefficient of determination (R2) for calibration and validation was obtained 0.9995 and 1.0000, respectively. The value of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.397 and 0.189. This study found that the DA method can group the samples with an accuracy of 99.92%.

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Estimating Center of Mass Kinematics During Perturbed Human Standing Using Accelerometers

Journal of Applied Biomechanics ◽

10.1123/jab.2020-0222 ◽

2021 ◽

pp. 1-10

Author(s):

Sandra K. Hnat ◽

Musa L. Audu ◽

Ronald J. Triolo ◽

Roger D. Quinn

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Mean Squared Error ◽

Balance Control ◽

Center Of Mass ◽

Coefficient Of Determination ◽

Mean Square ◽

Cord Injury ◽

Movement Type

Estimating center of mass (COM) through sensor measurements is done to maintain walking and standing stability with exoskeletons. The authors present a method for estimating COM kinematics through an artificial neural network, which was trained by minimizing the mean squared error between COM displacements measured by a gold-standard motion capture system and recorded acceleration signals from body-mounted accelerometers. A total of 5 able-bodied participants were destabilized during standing through: (1) unexpected perturbations caused by 4 linear actuators pulling on the waist and (2) volitionally moving weighted jars on a shelf. Each movement type was averaged across all participants. The algorithm’s performance was quantified by the root mean square error and coefficient of determination (R2) calculated from both the entire trial and during each perturbation type. Throughout the trials and movement types, the average coefficient of determination was 0.83, with 89% of the movements with R2 > .70, while the average root mean square error ranged between 7.3% and 22.0%, corresponding to 0.5- and 0.94-cm error in both the coronal and sagittal planes. COM can be estimated in real time for balance control of exoskeletons for individuals with a spinal cord injury, and the procedure can be generalized for other gait studies.

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Estimating Maximum Daily Precipitation in the Upper Vistula Basin, Poland

Atmosphere ◽

10.3390/atmos10020043 ◽

2019 ◽

Vol 10 (2) ◽

pp. 43 ◽

Cited By ~ 16

Author(s):

Dariusz Młyński ◽

Andrzej Wałęga ◽

Andrea Petroselli ◽

Flavia Tauro ◽

Marta Cebulska

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Goodness Of Fit ◽

Daily Precipitation ◽

Probability Distributions ◽

Random Variables ◽

Coefficient Of Determination ◽

Mean Square ◽

Probability Of Exceedance

The aim of this study was to determine the best probability distributions for calculating the maximum annual daily precipitation with the specific probability of exceedance (Pmaxp%). The novelty of this study lies in using the peak-weighted root mean square error (PWRMSE), the root mean square error (RMSE), and the coefficient of determination (R2) for assessing the fit of empirical and theoretical distributions. The input data included maximum daily precipitation records collected in the years 1971–2014 at 51 rainfall stations from the Upper Vistula Basin, Southern Poland. The value of Pmaxp% was determined based on the following probability distributions of random variables: Pearson’s type III (PIII), Weibull’s (W), log-normal, generalized extreme value (GEV), and Gumbel’s (G). Our outcomes showed a lack of significant trends in the observation series of the investigated random variables for a majority of the rainfall stations in the Upper Vistula Basin. We found that the peak-weighted root mean square error (PWRMSE) method, a commonly used metric for quality assessment of rainfall-runoff models, is useful for identifying the statistical distributions of the best fit. In fact, our findings demonstrated the consistency of this approach with the RMSE goodness-of-fit metrics. We also identified the GEV distribution as recommended for calculating the maximum daily precipitation with the specific probability of exceedance in the catchments of the Upper Vistula Basin.

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Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

Remote Sensing ◽

10.3390/rs12111814 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1814

Author(s):

Phamchimai Phan ◽

Nengcheng Chen ◽

Lei Xu ◽

Zeqiang Chen

Keyword(s):

Remote Sensing ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Vegetation Index ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Mean Square ◽

Multi Temporal

Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam. Tea yield, however, has stagnated in recent years, due to changes in temperature, precipitation, the age of the tea bushes, and diseases. Developing an approach for monitoring tea bushes by remote sensing and Geographic Information Systems (GIS) might be a way to alleviate this problem. Using multi-temporal remote sensing data, the paper details an investigation of the changes in tea health and yield forecasting through the normalized difference vegetation index (NDVI). In this study, we used NDVI as a support tool to demonstrate the temporal and spatial changes in NDVI through the extract tea NDVI value and calculate the mean NDVI value. The results of the study showed that the minimum NDVI value was 0.42 during January 2013 and February 2015 and 2016. The maximum NDVI value was in August 2015 and June 2017. We indicate that the linear relationship between NDVI value and mean temperature was strong with R 2 = 0.79 Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction. Three models to predict tea yield were conducted: support vector machine (SVM), random forest (RF), and the traditional linear regression model (TLRM). For period 2009 to 2018, the prediction tea yield by the RF model was the best with a R 2 = 0.73 , by SVM it was 0.66, and 0.57 with the TLRM. Three evaluation indicators were used to consider accuracy: the coefficient of determination ( R 2 ), root-mean-square error (RMSE), and percentage error of tea yield (PETY). The highest accuracy for the three models was in 2015 with a R 2 ≥ 0.87, RMSE < 50 kg/ha, and PETY less 3% error. In the other years, the prediction accuracy was higher in the SVM and RF models. Meanwhile, the RF algorithm was better than PETY (≤10%) and the root mean square error for this algorithm was significantly less (≤80 kg/ha). RMSE and PETY showed relatively good values in the TLRM model with a RMSE from 80 to 100 kg/ha and a PETY from 8 to 15%.

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