TY  - JOUR
A1  - Band, Shahab S.
A1  - Janizadeh, Saeid
A1  - Chandra Pal, Subodh
A1  - Chowdhuri, Indrajit
A1  - Siabi, Zhaleh
A1  - Norouzi, Akbar
A1  - Melesse, Assefa M.
A1  - Shokri, Manouchehr
A1  - Mosavi, Amir Hosein
T1  - Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration
JF  - Sensors
N2  - Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.
KW  - Grundwasser
KW  - Nitratbelastung
KW  - Künstliche Intelligenz
KW  - ground water contamination
KW  - machine learning
KW  - big data
KW  - hydrological model
KW  - OA-Publikationsfonds2020
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210122-43364
UR  - https://www.mdpi.com/1424-8220/20/20/5763
VL  - 2020
IS  - Volume 20, issue 20, article 5763
SP  - 1
EP  - 23
PB  - MDPI
CY  - Basel
ER  - 
TY  - JOUR
A1  - Meiabadi, Mohammad Saleh
A1  - Moradi, Mahmoud
A1  - Karamimoghadam, Mojtaba
A1  - Ardabili, Sina
A1  - Bodaghi, Mahdi
A1  - Shokri, Manouchehr
A1  - Mosavi, Amir Hosein
T1  - Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication
JF  - polymers
N2  - Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects.
KW  - 3D-Druck
KW  - Polymere
KW  - Maschinelles Lernen
KW  - 3D printing
KW  - machine learning
KW  - fused filament fabrication
KW  - OA-Publikationsfonds2021
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220110-45518
UR  - https://www.mdpi.com/2073-4360/13/19/3219
VL  - 2021
IS  - Volume 13, issue 19, article 3219
SP  - 1
EP  - 21
PB  - MDPI
CY  - Basel
ER  -