Estimation of Groundwater Level Using Artificial Neural Networks: a Case Study of Hatay-Turkey

2017 ◽  
Author(s):  
Fatih Üneş ◽  
Mustafa Demirci ◽  
Eyup Ispir ◽  
Yunus Ziya Kaya ◽  
Mustafa Mamak ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Groundwater Level ◽  
Underground Water ◽  
Population Increase ◽  
Ann Model ◽  
Total Water Consumption ◽  
Ann Models ◽  
Artificial Neural

Groundwater, which is a strategic resource in Turkey, is used for drinking-use, agricultural irrigation and industrial purposes. Population increase and total water consumption are constantly increasing. In order to meet the need for water, over-shoots from underground water have caused significant falls in groundwater level. Estimation of water level is important for planning an efficient and sustainable groundwater management. In this study, groundwater level, monthly mean precipitation and temperature observations of Turkish General Directorate of State Hydraulic Works (DSI) in Hatay, Amik Plain, Kumlu district were used between 2000 and 2015 years. The performance evaluation was done by creating Multi Linear Regression (MLR) and Artificial Neural Networks (ANN) models. The ANN model gave better results than the MLR model.

Modification of PET fabrics by hyperbranched polymer: a comparative study of artificial neural networks (ANN) and statistical approach

2013 ◽  
Vol 33 (5) ◽  
pp. 445-452 ◽  
Author(s):  
Mahdi Hasanzadeh ◽  
Tahereh Moieni ◽  
Bentolhoda Hadavi Moghadam
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Three Dimensional ◽  
Ann Model ◽  
Acid Dyes ◽  
Color Strength ◽  
Ann Models ◽  
Artificial Neural ◽  
Poly Ethylene ◽  
Rsm Model

Abstract Hyperbranched polymers (HBPs) are highly branched, three-dimensional and polydisperse macromolecules and have been employed for modification of poly(ethylene terephthalate) (PET) fabrics. The PET fabrics treatment process parameters, like HBP concentration, temperature and time, play a major role in treatment yield and dyeability of treated PET fabrics by acid dyes. Two different quantitative models, comprising response surface methodology (RSM) and artificial neural networks (ANN), were developed for predicting color strength (K/S value) of treated fabrics. The experiments were conducted based on central composite design (CCD) and a mathematical model was developed. A comparison of the predicted color strength using RSM and ANN was studied. The results obtained indicated that both RSM and ANN models show a very good relationship between the experimental and predicted response values. However, the ANN model shows more accurate results than the RSM model.

Simulation of Grinding by Means of the Finite Element Method and Artificial Neural Networks

2012 ◽  
pp. 193-218
Author(s):  
A.P. Markopoulos
Keyword(s):  
Neural Networks ◽  
Finite Element Method ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Ann Model ◽  
The Finite Element Method ◽  
Ann Models ◽  
Artificial Neural ◽  
Complex Phenomena ◽  
Element Method

Simulation of grinding is a topic of great interest due to the wide application of the process in modern industry. Several modeling methods have been utilized in order to accurately describe the complex phenomena taking place during the process, the most common being the Finite Element Method (FEM) and the Artificial Neural Networks (ANN). In the present work, a FEM model and an ANN model for precision surface grinding, are presented. Furthermore, a new approach, a combination of the aforementioned methods, is proposed, and a hybrid model is presented. This model comprises the advantages of both FEM and ANN models. The three kinds of models described in this work are able to accurately predict several grinding features that define the outcome of the process and the quality of the final product.

Predicting Viscosities of Heavy Oils and Solvent–Heavy Oil Mixtures Using Artificial Neural Networks

2021 ◽  
Vol 143 (11) ◽  
Author(s):  
Zehua Chen ◽  
Daoyong Yang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Performance ◽  
Viscosity Data ◽  
Heavy Oils ◽  
Ann Model ◽  
Wide Range ◽  
Data Points ◽  
Ann Models ◽  
Artificial Neural

Abstract This study investigates the potential of artificial neural networks (ANNs) to accurately predict viscosities of heavy oils (HOs) as well as mixtures of solvents and heavy oils (S–HOs). The study uses experimental data collected from the public domain for HO viscosities (involving 20 HOs and 568 data points) and S–HO mixture viscosities (involving 12 solvents and 4057 data points) for a wide range of temperatures, pressures, and mass fractions. The natural logarithm of viscosity (instead of viscosity itself) is used as predictor and response variables for the ANNs to significantly improve model performance. Gaps in HO viscosity data (with respect to pressure or temperature) are filled using either the existing correlations or ANN models that innovatively use viscosity ratios from the available data. HO viscosities and mixture viscosities (weight-based, molar-based, and volume-based) from the trained ANN models are found to be more accurate than those from commonly used empirical correlations and mixing rules. The trained ANN model also fares well for another dataset of condensate-diluted HOs.

scholarly journals Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Mehdi Nikoo ◽  
Farshid Torabian Moghadam ◽  
Łukasz Sadowski
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Ann Model ◽  
Ann Models ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Different Characteristics ◽  
Mlr Model

Compressive strength of concrete has been predicted using evolutionary artificial neural networks (EANNs) as a combination of artificial neural network (ANN) and evolutionary search procedures, such as genetic algorithms (GA). In this paper for purpose of constructing models samples of cylindrical concrete parts with different characteristics have been used with 173 experimental data patterns. Water-cement ratio, maximum sand size, amount of gravel, cement, 3/4 sand, 3/8 sand, and coefficient of soft sand parameters were considered as inputs; and using the ANN models, the compressive strength of concrete is calculated. Moreover, using GA, the number of layers and nodes and weights are optimized in ANN models. In order to evaluate the accuracy of the model, the optimized ANN model is compared with the multiple linear regression (MLR) model. The results of simulation verify that the recommended ANN model enjoys more flexibility, capability, and accuracy in predicting the compressive strength of concrete.

Employment of artificial neural networks for non-invasive estimation of leaf water status using color features: a case study in Spathiphyllum wallisii

2021 ◽  
Vol 43 (5) ◽  
Author(s):  
Amin Taheri-Garavand ◽  
Abdolhossein Rezaei Nejad ◽  
Dimitrios Fanourakis ◽  
Soodabeh Fatahi ◽  
Masoumeh Ahmadi Majd
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Status ◽  
Leaf Water ◽  
Leaf Water Status ◽  
Non Invasive ◽  
Color Features ◽  
Artificial Neural

scholarly journals Predicting Sooting Propensity of Oxygenated Fuels Using Artificial Neural Networks

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1070
Author(s):  
Abdul Gani Abdul Jameel
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Functional Groups ◽  
Average Error ◽  
Ann Model ◽  
Oh Groups ◽  
Unseen Data ◽  
Learning Capabilities ◽  
Artificial Neural ◽  
Oxygenated Fuels

The self-learning capabilities of artificial neural networks (ANNs) from large datasets have led to their deployment in the prediction of various physical and chemical phenomena. In the present work, an ANN model was developed to predict the yield sooting index (YSI) of oxygenated fuels using the functional group approach. A total of 265 pure compounds comprising six chemical classes, namely paraffins (n and iso), olefins, naphthenes, aromatics, alcohols, and ethers, were dis-assembled into eight constituent functional groups, namely paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic –CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, alcoholic OH groups, and ether O groups. These functional groups, in addition to molecular weight and branching index, were used as inputs to develop the ANN model. A neural network with two hidden layers was used to train the model using the Levenberg–Marquardt (ML) training algorithm. The developed model was tested with 15% of the random unseen data points. A regression coefficient (R2) of 0.99 was obtained when the experimental values were compared with the predicted YSI values from the test set. An average error of 3.4% was obtained, which is less than the experimental uncertainty associated with most reported YSI measurements. The developed model can be used for YSI prediction of hydrocarbon fuels containing alcohol and ether-based oxygenates as additives with a high degree of accuracy.

scholarly journals Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2332
Author(s):  
Cecilia Martinez-Castillo ◽  
Gonzalo Astray ◽  
Juan Carlos Mejuto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forests ◽  
Prediction Models ◽  
Meteorological Station ◽  
Ann Model ◽  
Global Irradiation ◽  
Artificial Neural ◽  
Input Variables ◽  
Using Data

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

scholarly journals Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

Buildings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 44
Author(s):  
Fernando A. N. Silva ◽  
João M. P. Q. Delgado ◽  
Rosely S. Cavalcanti ◽  
António C. Azevedo ◽  
Ana S. Guimarães ◽  
...  
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Cross Sections ◽  
Influential Factors ◽  
Average Error ◽  
Ann Model ◽  
Cement Ratio ◽  
Water Cement Ratio ◽  
Artificial Neural

The work presents the results of an experimental campaign carried out on concrete elements in order to investigate the potential of using artificial neural networks (ANNs) to estimate the compressive strength based on relevant parameters, such as the water–cement ratio, aggregate–cement ratio, age of testing, and percentage cement/metakaolin ratios (5% and 10%). We prepared 162 cylindrical concrete specimens with dimensions of 10 cm in diameter and 20 cm in height and 27 prismatic specimens with cross sections measuring 25 and 50 cm in length, with 9 different concrete mixture proportions. A longitudinal transducer with a frequency of 54 kHz was used to measure the ultrasonic velocities. An ANN model was developed, different ANN configurations were tested and compared to identify the best ANN model. Using this model, it was possible to assess the contribution of each input variable to the compressive strength of the tested concretes. The results indicate an excellent performance of the ANN model developed to predict compressive strength from the input parameters studied, with an average error less than 5%. Together, the water–cement ratio and the percentage of metakaolin were shown to be the most influential factors for the compressive strength value predicted by the developed ANN model.

Sign in / Sign up

Export Citation Format

Share Document