scholarly journals Estimating Inside Air Temperature of a Glasshouse Using Statistical Models

2017 ◽  
Vol 12 (1) ◽  
pp. 01-05 ◽  
Author(s):  
Aristidis Matsoukis ◽  
Konstantinos Chronopoulos
Keyword(s):  
Air Temperature ◽  
Mean Absolute Error ◽  
Predictor Variable ◽  
Absolute Error ◽  
Meteorological Station ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Better Than

The efficiency of applying linear regression (LR) and artificial neural network (ANN) models to estimate inside air temperature (T) of a glasshouse (37o48΄20΄΄N, 23o57΄48΄΄E), Lavreotiki, was investigated in the present work. The T data from an urban meteorological station (MS) at 37058΄55΄΄N, 23o32΄14΄΄E, Athens, Attica, Greece, about 30 Km away from the glasshouse, were used as predictor variable, taking into account the actual time of measurement (ATM) and two hours earlier (ATM-2), depending on the case. Air temperature data were monitored in each examined area (glasshouse and MS) for four successive months (July-October) and averages on a two-hour basis were used for the aforementioned estimation. Results showed that ANN were better than LR models, considering their better performance as shown in the scatterplots of the distribution of observed versus estimated inside T data of the glasshouse, in terms of both higher coefficient of determination (R2) and lower mean absolute error (MAE). The best ANN model (highest R2 and lowest MAE) was achieved by using as predictor variables the T at ATM and the T at ATM-2 from MS. The findings of our study may be a first step towards the estimation of inside T of a glasshouse in Greece, from outside T data of a remote MS. Thus, the operation of the glasshouse could be improved noticeably.

scholarly journals Statistical Models in Estimating Air Temperature in a Mountainous Region of Greece

2017 ◽  
Vol 12 (3) ◽  
pp. 544-549 ◽  
Author(s):  
Stelios Maniatis ◽  
Kostas Chronopoulos ◽  
Aristidis Matsoukis ◽  
Athanasios Kamoutsis
Keyword(s):  
Air Temperature ◽  
Statistical Models ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Multi Layer Perceptron ◽  
Simple Linear Regression ◽  
The Mean ◽  
Of Greece ◽  
Better Than

The current work focuses on the estimation of air temperature (T) conditions in two high altitude (alt) sites (1580 m), each one at different orientation (southeast and northwest) in the mountain (Mt) Aenos in the island of Cephalonia, Greece, by using two well-known statistical models, simple linear regression (SLR) and multi-layer perceptron ( MLP), one of the most commonly used artificial neural networks. More specifically, the estimation of mean, maximum and minimum T in high alt sites was based on the respective T data of two lower alt sites (1100 m), the first at southeast and the second at northwest orientations, and was carried out separately for each orientation. The performance of both SLR and MLP models was evaluated by the coefficient of determination (R2) and the Mean Absolute Error (MAE). Results showed that the examined models (SLR and MLP) provided very satisfactory results with regard to the estimation of mean, maximum and minimum T, regarding southeast orientation (R2 ranging from 0.96 to 0.98), with mean T estimation being relatively better, as confirmed by the lowest MAE (0.83). Regarding northwest orientation, T estimation was less accurate (lower R2 and higher MAE), compared to the respective estimation of southeast orientation, but, the results were considered adequate (R2 and MAE ranging from 0.88 to 0.92 and 1.00 to 1.40, respectively). In general, the estimations of the mean T were better than those of the extreme ones (minimum and maximum T). In addition, better results (higher R2 and lower, in general, MAE) were obtained when T estimations were based on T data derived from sites located at areas with similar surroundings, as in the case of dense and tall vegetation of the sites at southeast orientation, irrespective of applied method.

Determination of number of check dams by artificial neural networks in arid regions of Iran

2015 ◽  
Vol 72 (6) ◽  
pp. 952-959 ◽  
Author(s):  
Seyed Ali Asghar Hashemi ◽  
Hamed Kashi
Keyword(s):  
Curve Number ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Effective Parameters ◽  
Check Dams ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Mlp Model

An artificial neural network (ANN) model with six hydrological factors including time of concentration (TC), curve number, slope, imperviousness, area and input discharge as input parameters and number of check dams (NCD) as output parameters was developed and created using GIS and field surveys. The performance of this model was assessed by the coefficient of determination R2, root mean square error (RMSE), values account and mean absolute error (MAE). The results showed that the computed values of NCD using ANN with a multi-layer perceptron (MLP) model regarding RMSE, MAE, values adjustment factor (VAF), and R2 (1.75, 1.25, 90.74, and 0.97) for training, (1.34, 0.89, 97.52, and 0.99) for validation and (0.53, 0.8, 98.32, and 0.99) for test stage, respectively, were in close agreement with their respective values in the watershed. Finally, the sensitivity analysis showed that the area, TC and curve number were the most effective parameters in estimating the number of check dams.

scholarly journals Modeling of hourly river water temperatures using artificial neural networks

2014 ◽  
Vol 49 (2) ◽  
pp. 144-162 ◽  
Author(s):  
Cindie Hebert ◽  
Daniel Caissie ◽  
Mysore G. Satish ◽  
Nassir El-Jabi
Keyword(s):  
Water Temperature ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Air Temperatures ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Many

Water temperature is an important component for water quality and biotic conditions in rivers. A good knowledge of river thermal regime is critical for the management of aquatic resources and environmental impact studies. The objective of the present study was to develop a water temperature model as a function of air temperatures, water temperatures and water level data using artificial neural network (ANN) techniques for two thermally different streams. This model was applied on an hourly basis. The results showed that ANN models are an effective modeling tool with overall root-mean-square-error of 0.94 and 1.23 °C, coefficient of determination (R2) of 0.967 and 0.962 and bias of −0.13 and 0.02 °C, for Catamaran Brook and the Little Southwest Miramichi River, respectively. The ANN model performed best in summer and autumn and showed a poorer performance in spring. Results of the present study showed similar or better results to those of deterministic and stochastic models. The present study shows that the predicted hourly water temperatures can also be used to estimate the mean and maximum daily water temperatures. The many advantages of ANN models are their simplicity, low data requirements, their capability of modeling long-term time series as well as having an overall good performance.

Using Artificial Neural Network to Predict Blast-Induced Ground Vibration

2012 ◽  
Vol 170-173 ◽  
pp. 1013-1016
Author(s):  
Fu Qiang Gao ◽  
Xiao Qiang Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Vibration ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Monitoring Point ◽  
Blasting Operation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Prediction of peak particle velocity (PPV) is very complicated due to the number of influencing parameters affecting seism wave propagation. In this paper, artificial neural network (ANN) is implemented to develop a model to predict PPV in a blasting operation. Based on the measured parameters of maximum explosive charge used per delay and distance between blast face to monitoring point, a three-layer ANN was found to be optimum with architecture 2-5-1. Through the analysis of coefficient of determination (CoD) and mean absolute error (MAE) between monitored and predicted values of PPV, it indicates that the forecast data by the ANN model is close to the actua1 values.

Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils

2009 ◽  
Vol 46 (8) ◽  
pp. 955-968 ◽  
Author(s):  
Yusuf Erzin ◽  
S. D. Gumaste ◽  
A. K. Gupta ◽  
D. N. Singh
Keyword(s):  
Hydraulic Conductivity ◽  
Absolute Error ◽  
Silty Sand ◽  
Coefficient Of Determination ◽  
Performance Indices ◽  
Fine Grained ◽  
Centrifuge Tests ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This study deals with development of artificial neural networks (ANNs) and multiple regression analysis (MRA) models for determining hydraulic conductivity of fine-grained soils. To achieve this, conventional falling-head tests, oedometer falling-head tests, and centrifuge tests were conducted on silty sand and marine clays compacted at different dry densities and moisture contents. Further, results obtained from ANN and MRA models were compared vis-à-vis experimental results. The performance indices such as the coefficient of determination, root mean square error, mean absolute error, and variance were used to assess the performance of these models. The ANN models exhibit higher prediction performance than the MRA models based on their performance indices. It has been demonstrated that the ANN models developed in the study can be employed for determining hydraulic conductivity of compacted fine-grained soils quite efficiently.

scholarly journals Application of ANN in Predicting the Cantilever Wall Deflection in Undrained Clay

2021 ◽  
Vol 11 (20) ◽  
pp. 9760
Author(s):  
Zhongkai Huang ◽  
Dongmei Zhang ◽  
Dongming Zhang
Keyword(s):  
Pressure Coefficient ◽  
Earth Pressure ◽  
Numerical Data ◽  
Absolute Error ◽  
Ann Model ◽  
Soil Stiffness ◽  
Wall Deflection ◽  
Proposed Model ◽  
Ann Models ◽  
Artificial Neural Network Ann

The main objective of this study is to propose an artificial neural network (ANN)-based tool for predicting the cantilever wall deflection in undrained clay. The excavation width, the excavation depth, the wall thickness, the at-rest lateral earth pressure coefficient, the soil shear strength ratio at mid-depth of the wall, and the soil stiffness ratio at mid-depth of the wall were selected as the input parameters, whereas the cantilever wall deflection was selected as an output parameter. A set of verified numerical data were utilized to train, test, and validate the ANN models. Two commonly used performance indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), were selected to evaluate the performance of the proposed model. The results indicated that the proposed model can reliably predict the cantilever wall deflection in undrained clay. Moreover, the sensitivity analysis showed that the excavation depth is the most important parameter. Finally, a graphical user interface (GUI) tool was developed based on the proposed ANN model, which is much easier and less expensive to be used in practice. The results of this study can help engineers to better understand and predict the cantilever wall deflection in undrained clay.

Using Machine Learning to Estimate Concentrations of Non-Targeted Chemicals Without Analytical Standards

2019 ◽  
Author(s):  
Dimitri Abrahamsson ◽  
June-Soo Park ◽  
Marina Sirota ◽  
Tracey Woodruff
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ionization Mass ◽  
Response Factors ◽  
Relative Response ◽  
Artificial Neural Network Ann ◽  
Analytical Standards

We developed two in silico quantification methods for chemicals analyzed with capillary electrophoresis electrospray ionization-mass spectrometry (CE-ESI-MS) using machine learning - a random forest (RF) and an artificial neural network (ANN). The algorithms can be used to predict chemical concentrations based on the chemicals’ relative response factors (RRFs) and their physicochemical properties. The RF and ANN predicted the measured concentrations with a mean absolute error of 0.2 log units and a coefficient of determination (R2) of about 0.85 for the testing set.

scholarly journals Using Artificial Neural Network (ANN) for prediction of soil coefficient of consolidation

2020 ◽  
Vol 42 (3) ◽  
Author(s):  
Thai Binh Pham ◽  
Sushant K. Singh ◽  
Hai-Bang Ly
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Coefficient Of Consolidation ◽  
Experimental Approaches ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Accuracy Performance

Soil Coefficient of Consolidation (Cv) is a crucial mechanical parameter and used to characterize whether the soil undergoes consolidation or compaction when subjected to pressure. In order to define such a parameter, the experimental approaches are costly, time-consuming, and required appropriate equipment to perform the tests. In this study, the development of an alternative manner to estimate the Cv, based on Artificial Neural Network (ANN), was conducted. A database containing 188 tests was used to develop the ANN model. Two structures of ANN were considered, and the accuracy of each model was assessed using common statistical measurements such as the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). In performing 600 simulations in each case, the ANN structure containing 14 neurons was statistically superior to the other one. Finally, a typical ANN result was presented to prove that it can be an excellent predictor of the problem, with a satisfying accuracy performance that yielded of RMSE = 0.0614, MAE = 0.0415, and R2 = 0.99727. This study might help in quick and accurate prediction of the Cv used in civil engineering problems.

Using Machine Learning to Estimate Concentrations of Non-Targeted Chemicals Without Analytical Standards

2019 ◽  
Author(s):  
Dimitri Abrahamsson ◽  
June-Soo Park ◽  
Marina Sirota ◽  
Tracey Woodruff
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ionization Mass ◽  
Response Factors ◽  
Relative Response ◽  
Artificial Neural Network Ann ◽  
Analytical Standards

We developed two in silico quantification methods for chemicals analyzed with capillary electrophoresis electrospray ionization-mass spectrometry (CE-ESI-MS) using machine learning - a random forest (RF) and an artificial neural network (ANN). The algorithms can be used to predict chemical concentrations based on the chemicals’ relative response factors (RRFs) and their physicochemical properties. The RF and ANN predicted the measured concentrations with a mean absolute error of 0.2 log units and a coefficient of determination (R2) of about 0.85 for the testing set.

scholarly journals Proportional Learning of Semi Indirect Evaporative Cooling System

2019 ◽  
Vol 8 (4) ◽  
pp. 8189-8196
Keyword(s):  
Temperature Change ◽  
Air Temperature ◽  
Cooling System ◽  
Evaporative Cooling ◽  
Test Results ◽  
Ann Model ◽  
Test Information ◽  
Evaporative Cooling System ◽  
Ann Models ◽  
Artificial Neural Network Ann

In this study, the semi evaporative cooling framework is assessed which is rely upon the presentation of a massexchanger (RHMX) and regenerative warmth. Prior specialists find the general numerical modeling techniques to overcome the cumbersome computational burden, and built up the information driven artificial neural network (ANN) model. A 1-D numerical model was used to deliver test information for structure the ANN model. Both the ANN models and numerical and the ANN models were affirmed against test results open in the composition with a satisfactory ordinary error level of around 4% reliant on the air temperature change over the dry channel. The assessment between examination information and ANN prediction exhibited incredible prediction accuracy. The typical prediction error between the envisioned and attempted information was around 4% reliant on the air temperature change over the dry channel. With the information driven model, parametric assessments were made to examine the introduction of the RHMX under different working conditions. Finally, a structure advancement of semi meandering evaporative cooling plan of extraction air proportion was coordinated under different surrounding conditions. It was found that the perfect extraction air proportion reduced with the encompassing temperature and furthermore relative wetness which stretched out from 0.3 to 0.36.

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