scholarly journals Estimation of Drought by Streamflow Drought Index (SDI) and Artificial Neural Networks (ANNs) in Ankara-Nallihan Region

2020 ◽  
Vol 8 (2) ◽  
pp. 348
Author(s):  
Tülay Akkurt Eroğluer ◽  
Halit Apaydin
Keyword(s):  
Drought Index ◽  
Absolute Error ◽  
Ann Model ◽  
Error Performance ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
Streamflow Drought Index ◽  
Predicted Values ◽  
High Consistency ◽  
Error Method

In this study, it is aimed to predict drought in Nallihan region by using streamflow drought index and artificial neural network method which is a part of artificial intelligence approaches. The measured data of some meteorological stations (Nallihan, Beypazari, Mihaliccik, Catacik, Goynuk, Mudurnu, Seben and Eskisehir) in the Sakarya Basin and the Nallihan streamflow observation station between 1996 and 2015 were used to forecast 2015-2030 streamflow values. The correlation coefficient in the education and test stages of the ANN model was realized with a high consistency of 0.990 and 0.967, respectively. According to the mean absolute error method, the error performance values of ANN model are 0.19 for the training phase and 0.26 for the test phase. Cumulative streamflow series were created for the reference periods (k1, October-December; k2, October-March; k3, October-June; k4, October-September) and the streamflow drought index values were obtained using measured and predicted values. According to these values, mild droughts were more frequent between 1997-2015 and 2016-2030, but the number of moderate and severe droughts increased gradually. It is predicted that in the future, it may be seen in extreme arid periods in the region. Drought in the 6-month period between October and March is similar to the average of all periods for 1997-2015 and 2016-2030. The use of 6-month drought data for the streamflow drought index is expected to be useful in predicting future drought.

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 Analysis of Crowd Flow Parameters Using Artificial Neural Network

2018 ◽  
Vol 19 (4) ◽  
pp. 335-345
Author(s):  
Poojari Yugendar ◽  
K.V.R. Ravishankar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Absolute Error ◽  
The Other ◽  
Ann Model ◽  
Deterministic Models ◽  
Accuracy Measurement ◽  
Flow Parameters ◽  
Non Linear ◽  
Artificial Neural

Abstract Research scientists have been developing mathematical tools to detect objects, recognize objects and actions, and discover behaviours and events to human abilities. In all these efforts, the understanding of human actions is of a special interest for both application and research purposes. In this study, crowd flow parameters are analysed by considering linear and non linear relationships between stream flow parameters using conventional and soft computing approach. Deterministics models like Greenshield and Underwood were applied in the study to describe flow characteristics. A non-linear model based on Artificial Neural Network (ANN) approach is also used to build a relationship between different crowd flow parameters and compared with the other deterministic models. ANN model gave good results based on accuracy measurement to deterministic models because of their self-processing and intelligent behaviour. Mean absolute error (MAE) and root mean square error (RMSE) values for the best fitted ANN model are less than those for the other models. ANN model gives better performance in fitness of model and future prediction of flow parameters.

Application of Artificial Neural Networks to Predict Cotton Production: A Case Study in Diyarbakır Province, Turkey

2020 ◽  
Author(s):  
Nazire Mikail ◽  
Mehmet Fırat BARAN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Plant Production ◽  
Good Prediction ◽  
Ann Model ◽  
Cotton Production ◽  
Factors Affecting ◽  
Artificial Neural Network Method ◽  
Artificial Neural

Abstract Cultivators are always curious about the factors affecting yield in plant production. Determining these factors can provide information about the yield in the future. The reliability of information is dependent on a good prediction model. According to the operating process, artificial neural networks imitate the neural network in humans. The ability to make predictions for the current situation by combining the information people have gained from different experiences is designed in artificial neural networks. Therefore, in complex problems, it gives better results than artificial neural networks.In this study, we used an artificial neural network method to model the production of cotton. From a comprehensive datum collection spanning 73 farms in Diyarbakır, Turkey, the mean cotton production was 559.19 kg da-1. There are four factors that are selected as pivotal inputs into this model. As a result, the ultimate ANN model is able to forshow cotton production, which is built on elements such as farm states (cotton area and irrigation periodicity), machinery usage and fertilizer consumption.At the end of the study, cotton yield was estimated with 84% accuracy.

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.

scholarly journals Prediction the Vapor-Liquid Equilibria of CO2-Containing Binary Refrigerant Mixtures Using Artificial Neural Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ahmad Azari ◽  
Saeid Atashrouz ◽  
Hamed Mirshekar
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Ann Model ◽  
Artificial Neural ◽  
Predicted Values ◽  
Artificial Neural Network Ann ◽  
Vapor Liquid Equilibria ◽  
Binary Refrigerant ◽  
Binary Refrigerant Mixtures ◽  
Vapor Liquid

Artificial neural network (ANN) technique has been applied for estimation of vapor-liquid equilibria (VLE) for eight binary refrigerant systems. The refrigerants include difluoromethane (R32), propane (R290), 1,1-difluoroethane (R152a), hexafluoroethane (R116), decafluorobutane (R610), 2,2-dichloro-1,1,1-trifluoroethane (R123), 1-chloro-1,2,2,2-tetrafluoroethane (R124), and 1,1,1,2-tetrafluoroethane (R134a). The related experimental data of open literature have been used to construct the model. Furthermore, some new experimental data (not applied in ANN training) have been used to examine the reliability of the model. The results confirm that there is a reasonable conformity between the predicted values and the experimental data. Additionally, the ability of the ANN model is examined by comparison with the conventional thermodynamic models. Moreover, the presented model is capable of predicting the azeotropic condition.

scholarly journals Soil moisture level prediction using optical technique and artificial neural network

2021 ◽  
Vol 11 (2) ◽  
pp. 1752
Author(s):  
Mars Hong Xuan Wai ◽  
Audrey Huong ◽  
Xavier Ngu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Near Infrared ◽  
Light Attenuation ◽  
Moisture Level ◽  
Ann Model ◽  
Error Performance ◽  
Soil Moisture Level ◽  
Artificial Neural

This research describes the use of an optical system combined with artificial neural network (ANN) for wireless and nondestructive prediction of soil moisture level. The former system comprising of near infrared (NIR) emitters of wavelengths 1200 nm and 1450 nm, and a photodetector for near real time soil moisture measurement in loams and peats holding different amount of water. There were 63 and 90 sets of data from loams and peats, respectively, used in the development of the dual stage-multiclass ANN model, wherein measurement of light attenuation (from nondestructive system) was correlated with percent soil moisture (from destructive gold standard approach) in pre-measurement stage. The result revealed a relatively good performance in the training of the NN with regression, R, of 0.8817 and 0.8881, and satisfactory error performance of 0.7898 and 1.172, for loams and peats, respectively. The testing of the system on 50 new samples of loam and peat showed a considerably high mean accuracy of 92 % for loams while 82 % was observed for peats. This study attributes the poorer performance of the system used on peats to the detection resolution of percent soil moisture, and structure and properties of the corresponding soil. This work concluded that the developed technology may be feasible for use in the future design and improvement of agricultural soil management.

scholarly journals Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1653 ◽  
Author(s):  
Amir Hossein Salimi ◽  
Jafar Masoompour Samakosh ◽  
Ehsan Sharifi ◽  
Mohammad Reza Hassanvand ◽  
Amir Noori ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Heavy Precipitation ◽  
Imperialist Competitive Algorithm ◽  
Absolute Error ◽  
Precipitation Data ◽  
Ann Model ◽  
Satellite Precipitation ◽  
Artificial Neural ◽  
Residual Correction

Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical variables. The particle swarm optimization (PSO), imperialist competitive algorithm,(ICA), and genetic algorithm (GA) were utilized to improve the performance of ANN. Moreover, to examine the efficiency of the networks, the downscaled product was evaluated using 54 rain gauges at a daily timescale. In addition, sensitivity analysis was conducted to obtain the most and least influential input parameters. Among the optimized algorithms for network training used in this study, the performance of the ICA slightly outperformed other algorithms. The best-recorded performance for ICA was on 17 April 2015 with root mean square error (RMSE) = 5.26 mm, mean absolute error (MAE) = 6.06 mm, R2 = 0.67, bias = 0.07 mm. The results showed that the prediction of precipitation was more sensitive to cloud optical thickness (COT). Moreover, the accuracy of the final downscaled satellite precipitation was improved significantly through residual correction algorithms.

Predicting the performance of multi-media filters using artificial neural networks

2016 ◽  
Vol 74 (9) ◽  
pp. 2225-2233 ◽  
Author(s):  
Alaa H. Hawari ◽  
Wael Alnahhal
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Real Life ◽  
Absolute Error ◽  
Ann Model ◽  
Model Complex ◽  
Multi Media ◽  
Artificial Neural ◽  
The Impact

The impact of flow rate and turbidity on the performance of multi-media filtration has been studied using an artificial neural network (ANN) based model. The ANN model was developed and tested based on experimental data collected from a pilot scale multi-media filter system. Several ANN models were tested, and the best results with the lowest errors were achieved with two hidden layers and five neurons per layer. To examine the significance and efficiency of the developed ANN model it was compared with a linear regression model. The R2 values for the actual versus predicted results were 0.9736 and 0.9617 for the ANN model and the linear regression model, respectively. The ANN model showed an R-squared value increase of 1.22% when compared to the linear regression model. In addition, the ANN model gave a significant reduction of 91.5% and 97.9% in the mean absolute error and the root mean square error, respectively when compared to the linear regression model. The proposed model has proven to give plausible results to model complex relationships that can be used in real life water treatment plants.

scholarly journals Predicting Cotton Fibre Maturity by Using Artificial Neural Network

2018 ◽  
Vol 18 (4) ◽  
pp. 429-433
Author(s):  
Assad Farooq ◽  
Muhammad Ilyas Sarwar ◽  
Muhammad Azeem Ashraf ◽  
Danish Iqbal ◽  
Azmat Hussain ◽  
...  
Keyword(s):  
Neural Network ◽  
Textile Industry ◽  
Predictive Modelling ◽  
Absolute Error ◽  
Cotton Fibre ◽  
Network Modelling ◽  
Artificial Neural ◽  
Input Variables ◽  
Predicted Values ◽  
Complex Relationships

Abstract Cotton fibre maturity is the measure of cotton’s secondary cell wall thickness. Both immature and over-mature fibres are undesirable in textile industry due to the various problems caused during different manufacturing processes. The determination of cotton fibre maturity is of vital importance and various methods and techniques have been devised to measure or calculate it. Artificial neural networks have the power to model the complex relationships between the input and output variables. Therefore, a model was developed for the prediction of cotton fibre maturity using the fibre characteristics. The results of predictive modelling showed that mean absolute error of 0.0491 was observed between the actual and predicted values, which show a high degree of accuracy for neural network modelling. Moreover, the importance of input variables was also defined.

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.

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