scholarly journals REDES NEURAIS ARTIFICIAIS, REGRESSÃO E MÉTODOS EMPÍRICOS PARA A MODELAGEM DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA NA CIDADE DE INHAMBANE, MOÇAMBIQUE

Irriga ◽  
2019 ◽  
Vol 24 (4) ◽  
pp. 802-816
Author(s):  
BARTOLOMEU FÉLIX TANGUNE ◽  
Rodrigo Máximo Sánchez Román
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Reference Evapotranspiration ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Empirical Methods ◽  
E Mail

REDES NEURAIS ARTIFICIAIS, REGRESSÃO E MÉTODOS EMPÍRICOS PARA A MODELAGEM DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA NA CIDADE DE INHAMBANE, MOÇAMBIQUE   BARTOLOMEU FÉLIX TANGUNE1 E RODRIGO MÁXIMO SÁNCHEZ ROMÁN2   1 Departamento de Engenharia Rural, Escola Superior de Desenvolvimento Rural, Universidade Eduardo Mondlane, Vilankulo, Inhambane, Moçambique. E-mail: [email protected]. 2 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista (UNESP) Campus de Botucatu. Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP: 18610-034, Botucatu – SP. Brasil. E-mail: [email protected]     1 RESUMO   Estimativa precisa da evapotranspiração de referência (ETo) é importante para dimensionar e fazer manejo de sistemas de irrigação. Métodos de estimativa da ETo (11 métodos empíricos, 10 modelos de regressão múltipla: RLM e 10 redes neurais artificias: RNAs) foram avaliados em relação ao método padrão de Penman Monteith FAO 56, utilizando os seguintes índices: MBE (Mean Bias Error), RMSE (Root Mean Square Error) e R2, sendo RMSE utilizado como critério principal de seleção dos métodos. A significância dos métodos foi avaliada com base no teste t utilizando dados de 1985 a 2009. Os dados meteorológicos utilizados (temperatura máxima: Tmax, temperatura mínima: Tmin e temperatura média: T, umidade relativa, velocidade do vento e insolação) são da estação meteorológica convencional da cidade de Inhambane, Moçambique. Os resultados mostraram que o modelo RLM4 apresentou melhor desempenho (MBE = 0,01 mm.d-1; RMSE = 0,15 mm.d-1; R2 = 0,99). Na falta da radiação solar global, os modelos RLM6 (MBE = -0,01 mm.d-1; RMSE = 0,23 mm.d-1; R2 = 0,97) e RLM10 (MBE = 0,01 mm.d-1; RMSE = 0,23 mm.d-1; R2 = 0,97) podem ser utilizados e exigem a medição da T, Tmax e Tmin, respectivamente. Esses modelos não foram estatisticamente diferentes do método padrão.   Palavras-chave: evapotranspiração, regressão múltipla, redes neurais.     TANGUNE, B. F.; SÁNCHEZ-ROMÁN, R. M. ARTIFICIAL NEURAL NETWORKS, REGRESSION AND EMPIRICAL METHODS FOR REFERENCE EVAPOTRANSPIRATION MODELING IN INHAMBANE CITY, MOZAMBIQUE     2 ABSTRACT   Precise estimation of reference evapotranspiration (ETo) is important for designing and managing irrigation systems. Methods of ETo estimation (11 empirical methods, 10 multiple regression models: RLM and 10 artificial neural networks: RNAs) were evaluated against Penman Monteith FAO 56 method using the following indexes: MBE (Mean Bias Error), RMSE (Root Mean Square Error) and R2, and RMSE was used as the main criterion of method selection. The significance of the methods was evaluated on the basis of the t test using data from 1985 to 2009. The meteorological data used (maximum temperature: Tmax, minimum temperature: Tmin and average temperature: T, relative air humidity, wind speed and solar brightness), from 1985 to 2009, are from the conventional meteorological station of the city of Inhambane, Mozambique. The results showed that the RLM4 model presented better performance (MBE = 0.01 mm.d-1; RMSE = 0.15 mm.d-1; R2 = 0.99). In the absence of global solar radiation, RLM6 (MBE = -0.01 mm.d-1; RMSE = 0.23 mm.d-1; R2 = 0.97) and RLM10 (MBE = 0.01 mm. d-1; RMSE = 0.23 mm.d-1; R2 = 0.97) can be used, which require measurement of T, and Tmax and Tmin, respectively. These models were not statistically different from the standard method.   Keywords: evapotranspiration, multiple regression, neural networks.

scholarly journals Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4572
Author(s):  
Ioannis O. Vardiambasis ◽  
Theodoros N. Kapetanakis ◽  
Christos D. Nikolopoulos ◽  
Trinh Kieu Trang ◽  
Toshiki Tsubota ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sewage Sludge ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Food Waste ◽  
Hydrothermal Carbonization ◽  
Mean Square ◽  
Artificial Neural

In this study, the growing scientific field of alternative biofuels was examined, with respect to hydrochars produced from renewable biomasses. Hydrochars are the solid products of hydrothermal carbonization (HTC) and their properties depend on the initial biomass and the temperature and duration of treatment. The basic (Scopus) and advanced (Citespace) analysis of literature showed that this is a dynamic research area, with several sub-fields of intense activity. The focus of researchers on sewage sludge and food waste as hydrochar precursors was highlighted and reviewed. It was established that hydrochars have improved behavior as fuels compared to these feedstocks. Food waste can be particularly useful in co-hydrothermal carbonization with ash-rich materials. In the case of sewage sludge, simultaneous P recovery from the HTC wastewater may add more value to the process. For both feedstocks, results from large-scale HTC are practically non-existent. Following the review, related data from the years 2014–2020 were retrieved and fitted into four different artificial neural networks (ANNs). Based on the elemental content, HTC temperature and time (as inputs), the higher heating values (HHVs) and yields (as outputs) could be successfully predicted, regardless of original biomass used for hydrochar production. ANN3 (based on C, O, H content, and HTC temperature) showed the optimum HHV predicting performance (R2 0.917, root mean square error 1.124), however, hydrochars’ HHVs could also be satisfactorily predicted by the C content alone (ANN1, R2 0.897, root mean square error 1.289).

scholarly journals Comparison of neural networks and genetic algorithms to determine missing precipitation data (Case study: the city of Sari)

2020 ◽  
Vol 11 (29) ◽  
pp. 114-128
Author(s):  
Ali Mahdavi ◽  
Mohsen Najarchi ◽  
Emadoddin Hazaveie ◽  
Seyed Mohammad Mirhosayni Hazave ◽  
Seyed Mohammad Mahdai Najafizadeh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Programming ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Correlation Coefficients ◽  
Mean Square ◽  
Artificial Neural ◽  
The City

Neural networks and genetic programming in the investigation of new methods for predicting rainfall in the catchment area of the city of Sari. Various methods are used for prediction, such as the time series model, artificial neural networks, fuzzy logic, fuzzy Nero, and genetic programming. Results based on statistical indicators of root mean square error and correlation coefficient were studied. The results of the optimal model of genetic programming were compared, the correlation coefficients and the root mean square error 0.973 and 0.034 respectively for training, and 0.964 and 0.057 respectively for the optimal neural network model. Genetic programming has been more accurate than artificial neural networks and is recommended as a good way to accurately predict.

scholarly journals Solar radiation forecasting by using deep neural networks in Eski̇şehi̇r

2021 ◽  
pp. 159-169
Author(s):  
Mohammed QASEM
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Solar Energy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Clean Energy ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square

According to the World Economic Outlook (WEO), the global demand for energy is presum- ably going to be increased due to growing the world’s population up during the upcoming two decades. As a result of that, apprehensions about environmental effects, which appear as a re- sult of greenhouse gases are grown and cleaner energy technologies are developed. This clearly shows that extended growth of the worldwide market share of clean energy. Solar energy is considered as one of the fundamental types of renewable energy. For this reason, the need for a predictive model that effectively observes solar energy conversion with high performance becomes urgent. In this paper, classic empirical, artificial neural network (ANN), deep neural network (DNN), and time series models are applied, and their results are compared to each other to find the most accurate model for daily global solar radiation (DGSR) estimation. In addition, four regression models have been developed and applied for DGSR estimation. The obtained results are evaluated and compared by the root mean square error (RMSE), rela- tive root mean square error (rRMSE), mean absolute error (MAE), mean bias error (MBE), t-statistic, and coefficient of determination (R2). Finally, simulation results provided that the best result is found by the DNN model.

scholarly journals Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
T. C. Chakraborty ◽  
Xuhui Lee
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Diffuse Radiation ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square

AbstractDiffuse solar radiation is an important, but understudied, component of the Earth’s surface radiation budget, with most global climate models not archiving this variable and a dearth of ground-based observations. Here, we describe the development of a global 40-year (1980–2019) monthly database of total shortwave radiation, including its diffuse and direct beam components, called BaRAD (Bias-adjusted RADiation dataset). The dataset is based on a random forest algorithm trained using Global Energy Balance Archive (GEBA) observations and applied to the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) dataset at the native MERRA-2 resolution (0.5° by 0.625°). The dataset preserves seasonal, latitudinal, and long-term trends in the MERRA-2 data, but with reduced biases than MERRA-2. The mean bias error is close to 0 (root mean square error = 10.1 W m−2) for diffuse radiation and −0.2 W m−2 (root mean square error = 19.2 W m−2) for the total incoming shortwave radiation at the surface. Studies on atmosphere-biosphere interactions, especially those on the diffuse radiation fertilization effect, can benefit from this dataset.

scholarly journals A High-Resolution Topographic Correction Method for Clear-Sky Solar Irradiance Derived with a Numerical Weather Prediction Model

2011 ◽  
Vol 50 (12) ◽  
pp. 2460-2472 ◽  
Author(s):  
José A. Ruiz-Arias ◽  
David Pozo-Vázquez ◽  
Vicente Lara-Fanego ◽  
Francisco J. Santos-Alamillos ◽  
J. Tovar-Pescador
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Numerical Weather Prediction ◽  
Solar Irradiance ◽  
Weather Prediction ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Numerical Weather

AbstractRugged terrain is a source of variability in the incoming solar radiation field, but the influence of terrain is still not properly included by most current numerical weather prediction (NWP) models. In this work, a downscaling postprocessing method for NWP-model solar irradiance through terrain effects is presented. It allows one to decrease the estimation bias caused by terrain shading and sky-view reduction, and to account for elevation variability, surface orientation, and surface albedo. The method has been applied to a case study in southern Spain using the Weather Research and Forecasting (WRF) mesoscale model with a spatial resolution of 30 arc s, resulting in disaggregated maps of 3 arc s. The validation was based on a radiometric network made of eight stations located in the Natural Park of Sierra Mágina over an area of roughly 30 × 35 km2 and 12 carefully selected cloudless days during a year. Three of the stations were equipped with tilted pyranometers. Their inclination and aspect were visually adjusted to the inclination and aspect of the local terrain and then carefully measured. For horizontal surface, the downscaled irradiance has proven to reduce the root-mean-square error of the WRF model by 20% to about 25 W m−2 in winter and autumn and 60 W m−2 in spring and summer. For tilted surface, downscaling to different spatial resolutions resulted in the best performance for 9 arc s, with root-mean-square error of 45% (57 W m−2) and a mean bias error close to zero.

scholarly journals Performance Analysis of Clear Sky Global Horizontal Irradiance Models: Simple Models Adapted for Local Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Nicholas Kwarikunda ◽  
Zivayi Chiguvare
Keyword(s):  
Solar Energy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Measured Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Clear Sky

Evaluation of the maximum solar energy potential of a given area for possible deployment of solar energy technologies requires assessment of clear sky solar irradiance for the region under consideration. Such localized assessment is critical for optimal sizing of the technology to be deployed in order to realize the anticipated output. As the measurements are not always available where they are needed, models may be used to estimate them. In this study, three different models were adapted for the geographical location of the area under study and used to estimate clear sky global horizontal irradiance (GHI) at three locations in the subtropical desert climate of Namibia. The three models, selected on the basis of input requirements, were used to compute clear sky GHI at Kokerboom, Arandis, and Auas. The models were validated and evaluated for performance using irradiance data measured at each of the sites for a period of three years by computing statistical parameters such as mean bias error (MBE), root mean square error (RMSE), and the coefficient of determination (R2), normalized MBE, and normalized RMSE. Comparative results between modelled and measured data showed that the models fit well the measured data, with normalized root mean square error values in the range 4–8%, while the R2 value was above 98% for the three models. The adapted models can thus be used to compute clear sky GHI at these study areas as well as in other regions with similar climatic conditions.

Prediction of penetration rate of rotary-percussive drilling using artificial neural networks – a case study / Prognozowanie postępu wiercenia przy użyciu wiertła udarowo-obrotowego przy wykorzystaniu sztucznych sieci neuronowych – studium przypadku

2012 ◽  
Vol 57 (3) ◽  
pp. 715-728 ◽  
Author(s):  
Seyed Ali Aalizad ◽  
Farshad Rashidinejad
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Penetration Rate ◽  
Rock Properties ◽  
Mean Square ◽  
Percussive Drilling ◽  
Drilling Condition

Abstract Penetration rate in rocks is one of the most important parameters of determination of drilling economics. Total drilling costs can be determined by predicting the penetration rate and utilized for mine planning. The factors which affect penetration rate are exceedingly numerous and certainly are not completely understood. For the prediction of penetration rate in rotary-percussive drilling, four types of rocks in Sangan mine have been chosen. Sangan is situated in Khorasan-Razavi province in Northeastern Iran. The selected parameters affect penetration rate is divided in three categories: rock properties, drilling condition and drilling pattern. The rock properties are: density, rock quality designation (RQD), uni-axial compressive strength, Brazilian tensile strength, porosity, Mohs hardness, Young modulus, P-wave velocity. Drilling condition parameters are: percussion, rotation, feed (thrust load) and flushing pressure; and parameters for drilling pattern are: blasthole diameter and length. Rock properties were determined in the laboratory, and drilling condition and drilling pattern were determined in the field. For create a correlation between penetration rate and rock properties, drilling condition and drilling pattern, artificial neural networks (ANN) were used. For this purpose, 102 blastholes were observed and drilling condition, drilling pattern and time of drilling in each blasthole were recorded. To obtain a correlation between this data and prediction of penetration rate, MATLAB software was used. To train the pattern of ANN, 77 data has been used and 25 of them found for testing the pattern. Performance of ANN models was assessed through the root mean square error (RMSE) and correlation coefficient (R2). For optimized model (14-14-10-1) RMSE and R2 is 0.1865 and 86%, respectively, and its sensitivity analysis showed that there is a strong correlation between penetration rate and RQD, rotation and blasthole diameter. High correlation coefficient and low root mean square error of these models showed that the ANN is a suitable tool for penetration rate prediction.

scholarly journals Subset selection of markers for the genome-enabled prediction of genetic values using radial basis function neural networks

2020 ◽  
Vol 43 ◽  
pp. e46307 ◽  
Author(s):  
Isabela de Castro Sant'Anna ◽  
Gabi Nunes Silva ◽  
Moysés Nascimento ◽  
Cosme Damião Cruz
Keyword(s):  
Neural Networks ◽  
Root Mean Square Error ◽  
Radial Basis Function ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Basis Function ◽  
Stepwise Regression ◽  
Subset Selection ◽  
Mean Square ◽  
Selection Of

This paper aimed to evaluate the effectiveness of subset selection of markers for genome-enabled prediction of genetic values using radial basis function neural networks (RBFNN). To this end, an F1 population derived from the hybridization of divergent parents with 500 individuals genotyped with 1000 SNP-type markers was simulated. Phenotypic traits were determined by adopting three different gene action models – additive, additive-dominant, and epistatic, representing two dominance situations: partial and complete with quantitative traits having a heritability (h2) of 30 and 60%; traits were controlled by 50 loci, considering two alleles per locus. Twelve different scenarios were represented in the simulation. The stepwise regression was used before the prediction methods. The reliability and the root mean square error were used for estimation using a fivefold cross-validation scheme. Overall, dimensionality reduction improved the reliability values for all scenarios, specifically with h2 =30 the reliability value from 0.03 to 0.59 using RBFNN and from 0.10 to 0.57 with RR-BLUP in the scenario with additive effects. In the additive dominant scenario, the reliability values changed from 0.12 to 0.59 using RBFNN and from 0.12 to 0.58 with RR-BLUP, and in the epistasis scenarios, the reliability values changed from 0.07 to 0.50 using RBFNN and from 0.06 to 0.47 with RR-BLUP. The results showed that the use of stepwise regression before the use of these techniques led to an improvement in the accuracy of prediction of the genetic value and, mainly, to a large reduction of the root mean square error in addition to facilitating processing and analysis time due to a reduction in dimensionality.

scholarly journals Prediction of paddy drying kinetics: A comparative study between mathematical and artificial neural network modelling

2017 ◽  
Vol 23 (2) ◽  
pp. 251-258 ◽  
Author(s):  
Mohsen Beigi ◽  
Mehdi Torki-Harchegani ◽  
Mahmood Mahmoodi-Eshkaftaki
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Comparative Study ◽  
Mean Square Error ◽  
Back Propagation ◽  
Drying Kinetics ◽  
Mean Square ◽  
Rough Rice ◽  
Artificial Neural ◽  
Drying Curves

The present study aimed at investigation of deep bed drying of rough rice kernels at various thin layers at different drying air temperatures and flow rates. A comparative study was performed between mathematical thin layer models and artificial neural networks to estimate the drying curves of rough rice. The suitability of nine mathematical models in simulating the drying kinetics was examined and the Midilli model was determined as the best approach for describing drying curves. Different feed forward-back propagation artificial neural networks were examined to predict the moisture content variations of the grains. The ANN with 4-18-18-1 topology, transfer function of hyperbolic tangent sigmoid and a Levenberg-Marquardt back propagation training algorithm provided the best results with the maximum correlation coefficient and the minimum mean square error values. Furthermore, it was revealed that ANN modeling had better performance in prediction of drying curves with lower root mean square error values.

scholarly journals Penerapan Generalized Regression Neural Networks untuk Memprediksi Produksi Padi Terhadap Perubahan Iklim

2017 ◽  
Vol 2 (2) ◽  
pp. 117 ◽  
Author(s):  
Muhammad Alkaff ◽  
Yuslena Sari
Keyword(s):  
Neural Networks ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Input Data ◽  
Mean Square ◽  
Generalized Regression Neural Networks ◽  
Data Output ◽  
Generalized Regression

Padi sebagai bahan makanan pokok utama bagi masyarakat Indonesia merupakan tanaman pangan yang rentan terhadap perubahan iklim. Pendataan dan perhitungan ramalan hasil produksi padi sangat diperlukan untuk mendukung kebijakan yang berkaitan dengan ketahanan pangan. Penelitian ini bertujuan untuk melakukan peramalan terhadap produksi padi di Kabupaten Barito Kuala sebagai kabupaten penghasil padi terbesar di Kalimantan Selatan dengan menggunakan data iklim sebagai input. Data iklim yang digunakan berasal dari Stasiun Meteorologi Syamsudin Noor, sedangkan sebagai data output adalah data produksi padi dari Badan Pusat Statistika (BPS) Provinsi Kalimantan Selatan. Metode yang digunakan untuk melakukan peramalan produksi padi adalah Generalized Regression Neural Networks (GRNN). Dari hasil pengujian didapatkan nilai Root Mean Square Error (RMSE) sebesar 0,296 dengan menggunakan parameter smoothness bernilai 1.Kata kunci: padi, iklim, Barito Kuala, GRNN, RMSE

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