scholarly journals Forecasting monthly rainfall using autoregressive integrated moving average model (ARIMA) and artificial neural network (ANN) model: A case study of Junagadh, Gujarat, India

2019 ◽  
Vol 11 (1) ◽  
pp. 35-41 ◽  
Author(s):  
D. K. Dwivedi ◽  
J.H. Kelaiya ◽  
G. R. Sharma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Back Propagation ◽  
Propagation Model ◽  
Monthly Rainfall ◽  
Hydrological Processes ◽  
Ann Model ◽  
Testing Data ◽  
Back Propagation Model

The onset, withdrawal and quantity of rainfall greatly influence the agricultural yield, economy, water resources, power generation and ecosystem. Time series modelling has been extensively used in stochastic hydrology for predicting various hydrological processes. The principles of stochastic processes have been increasingly and successfully applied in the past three decades to model many of the hydrological processes which are stochastic in nature. Time lagged models extract maximum possible information from the available record for forecasting. Artificial neural network has been found to be effective in modelling hydrological processes which are stochastic in nature. The ARIMA model was used to simulate and forecast rainfall using its linear approach and the performance of the model was compared with ANN. The computational approach of ANN is inspired from nervous system of living beings and the neurons possess the parallel distribution processing nature. ANN has proven to be a reliable tool for modelling compared to conventional methods like ARIMA and therefore ANN has been used in this study to estimate rainfall. In this study, rainfall estimation of Junagadh has been attempted using monthly rainfall training data of 32 years (1980-2011) and testing data of 5 years (2012-2016). A number of ANN model structures were tested, and the appropriate ANN model was selected based on its performance measures like root mean square error and correlation coefficient. The correlation coefficient Seasonal ARIMA (1,0,0)(3,1,1)12 and ANN back-propagation model (5-12-1) on the testing data was found to be 0.75 and 0.79 respectively. Seasonal ARIMA (1,0,0)(3,1,1)12 and ANN back-propagation model (5-12-1) were used for forecasting rainfall of 5 years (2017-2021).

scholarly journals Unbalance Rotor Parameters Detection Based on Artificial Neural Network

2019 ◽  
Vol 24 (No 1) ◽  
pp. 113-118
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Turbine ◽  
Back Propagation ◽  
Propagation Model ◽  
Ann Model ◽  
Acceleration Signal ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Back Propagation Model

Unbalance is an important fault that can damage or shut down vital rotary systems such as the gas turbine, compressors, and others, so to avoid this trouble, the balancing process is very crucial, even though it is time-consuming and costly. Thus, having a technique which can predict the unbalance location and its parameters will be valuable and practical. The current study represents a model that can identify the unbalance’s mass, radius, and location of the eccentric mass based on the artificial neural network (ANN) model. The inputs of the proposed ANN, which is based on a feed forward with back propagation model, is the bearing acceleration signal in the frequency domain. It has 10 hidden layers with 10 neurons through each layer. The accuracy in prediction was acquired at 96%, 96%, and 94% for the disc number (plane), the eccentric radius, and eccentric mass values, respectively.

scholarly journals Recognize Facial Impression using Artificial Neural Network

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Montaser Hassan Bashir Ali ◽  
Osman Mudathir Elfadil
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Performance ◽  
Back Propagation ◽  
Propagation Model ◽  
Average Error ◽  
Human Face ◽  
Artificial Neural ◽  
True Values ◽  
Back Propagation Model

This paper aims to design an artificial neural network to discover the impression by recognizing the expression of the human face. To achieve this goal, the artificial neural network was analyzed and to create patterns of the database containing a set of images with different expressions. The learning process of the network was also conducted through patterns training. The extent to which patterns of online training were recognized was compared to the true values of expressions. The grid was trained in 200 patterns and the anomalies were removed. Then re-learned the network again and analyzed the network performance by comparing the real expression with the expected expression and outputting the error for the network appearing. Impression recognition in the grid applied a three-layer back propagation model, with an average error of 0.321. The performance of the artificial neural network in the recognition of impressions was 80%

scholarly journals Prediction of Volatile Organic Compounds (VOCs) From Decomposition of Local Household Food Waste Using the Artificial Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 5773-5779
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Food Waste ◽  
Back Propagation ◽  
Ann Model ◽  
Neuron Network ◽  
Volatile Organic ◽  
Artificial Neural

This study examines the potential of artificial neural network (ANN) to predict Total Volatile Organic Compounds (TVOCs) released via decomposition of local food wastes. To mimic the decomposition process, a bioreactor was designed to stimulate the food waste storage condition. The food waste was modeled based on the waste composition from a residential area. A feed forward multilayer back propagation (Levenberg – Marquardt training algorithm) was then developed to predict the TVOCs. The findings indicate that a two-layer artificial neuron network (ANN) with six input variables and these include (outside and inside temperature, pH, moisture content, oxygen level, relative humidity) with a total of eighty eight (88) data are used for the modeling purpose. The network with the highest regression coefficient (R) is 0.9967 and the lowest Mean Square Error (MSE) is 0.00012 (nearest to the value of zero) has been selected as the Optimum ANN model. The findings of this study suggest the most suitable ANN model that befits the research objective is ANN model with one (1) hidden layer with fifteen (15) hidden neurons. Additionally, it is critical to note that the results from the experiment and predicted model are in good agreement.

Predictive modeling of geometric shapes of different objects using image processing and an artificial neural network

2016 ◽  
Vol 231 (6) ◽  
pp. 1206-1216 ◽  
Author(s):  
Mustafa Ayyıldız ◽  
Kerim Çetinkaya
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Conjugate Gradient ◽  
Back Propagation ◽  
Neural Network Modeling ◽  
Back Propagation Algorithm ◽  
Geometric Shapes ◽  
Testing Data ◽  
Artificial Neural

In this study, an artificial neural network model was developed to predict the geometric shapes of different objects using image processing. These objects with various sizes and shapes (circle, square, triangle, and rectangle) were used for the experimental process. In order to extract the features of these geometric shapes, morphological features, including the area, perimeter, compactness, elongation, rectangularity, and roundness, were applied. For the artificial neural network modeling, the standard back-propagation algorithm was found to be the optimum choice for training the model. In the building of the network structure, five different learning algorithms were used: the Levenberg–Marquardt, the quasi-Newton back propagation, the scaled conjugate gradient, the resilient back propagation, and the conjugate gradient back propagation. The best result was obtained by 6-5-1 network architectures with single hidden layers for the geometric shapes. After artificial neural network training, the correlation coefficients ( R2) of the geometric shape values for training and testing data were very close to 1. Similarly, the root-mean-square error and mean error percentage values for the training and testing data were less than 0.9% and 0.004%, respectively. These results demonstrated that the artificial neural network is an admissible model for the estimation of geometric shapes using image processing.

Water Quality Forecast Based on BP-Artificial Neural Network Model in Qiantang River

2014 ◽  
Vol 668-669 ◽  
pp. 994-998
Author(s):  
Jin Ting Ding ◽  
Jie He
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Ph Value ◽  
Back Propagation ◽  
Ammonia Nitrogen ◽  
Quality Parameters ◽  
Ann Model ◽  
Qiantang River ◽  
Artificial Neural

This study aims at providing a back propagation-artificial neural network (BP-ANN) model on forecasting the water quality change trend of Qiantang River basin. To achieve this goal, a three-layer (one input layer, one hidden layer, and one output layer) BP-ANN with the LM regularization training algorithm was used. Water quality variables such as pH value, dissolved oxygen, permanganate index and ammonia-nitrogen was selected as the input data to obtain the output of the neural network. The ANN structure with 17 hidden neurons obtained the best selection. The comparison between the original measured and forecast values of the ANN model shows that the relative errors, with a few exceptions, were lower than 9%. The results indicated that the BP neural network can be satisfactorily applied to forecast precise water quality parameters and is suitable for pre-alarm of water quality trend.

scholarly journals Monthly Rainfall-Runoff Modeling for Sondur Reservoir

2021 ◽  
Vol 23 (07) ◽  
pp. 1453-1459
Author(s):  
Shashi Kant Jaiswal ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Model Development ◽  
Monthly Rainfall ◽  
Nonlinear Mapping ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Regression Procedure ◽  
Runoff Modeling ◽  
Artificial Neural Network Ann

This study presents the application of Artificial Neural Network (ANN) to modeling the rainfall-inflow relationship for Sondur Reservoir located in Chhattisgarh State of India. ANNs are usually assumed to be powerful tools for nonlinear mapping in various applications. ANN is superior to linear regression procedure used for rainfallinflow modeling. For model development twenty nine years data of monthly rainfall and inflow have been used. The results extracted from study indicated that the ANN model is efficient for rainfall-inflow modeling.

scholarly journals Experimental Investigation and Prediction of Tribological Properties of Titanium Carbide and Multiwalled Carbon Nanotubes Reinforced Aluminium Composites using Artificial Neural Network

2018 ◽  
Vol 3 (8) ◽  
pp. 40
Author(s):  
Mohamed Zakaulla ◽  
Anteneh Mohammed Tahir ◽  
Seid Endro ◽  
Shemelis Nesibu Wodaeneh ◽  
Lulseged Belay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Coefficient Of Friction ◽  
Tribological Properties ◽  
Friction And Wear ◽  
Back Propagation ◽  
Wear Loss ◽  
Ann Model ◽  
Multiwalled Carbon ◽  
Artificial Neural

In this study, the tribological properties of TiC particle and MWCNTs reinforced aluminium (Al7475) hybrid composite synthesized by stir casting method were investigated by experimental and artificial neural network (ANN) model. Al7475 metal matrix composites was produced with different wt% of TiC and MWCNTs. The composite samples were tested at 0.42 ms- 1, 0.84 ms- 1 and 1.68 ms- 1 under three different loads  (10N, 20N and 40N). The results indicated that Al7475+10%TiC+2%MWCNTs composite exhibit lower wear rate and reduced coefficient of friction in compare to other samples. TiC percent, MWCNTs percent, applied weight, sliding speed and Time were used as input values for the theoretical prediction model of the composite. coefficient of friction and Wear loss were the two outputs developed from proposed network. Back propagation neural network with 5 – 6 – 2 architecture that uses Levenberg –Marquardt training algorithm is used to predict the coefficient of friction and wear loss. After comparing experimental and ANNs predicted results it was noted that R2 was 0.992 for wear loss and 0.980 for coefficient of friction. This indicated that developed predicted model has a high state of reliability.

scholarly journals Artificial Neural Network Model for Managing and Forecasting Water Reservoir Discharge (Hemren Reservoir as A Case Study)

2014 ◽  
Vol 7 (4) ◽  
pp. 132-143
Author(s):  
ABBAS M. ABD ◽  
SAAD SH. SAMMEN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Mean Squared Error ◽  
Water Reservoir ◽  
Prediction Method ◽  
Training Data ◽  
Ann Model ◽  
Data Set ◽  
Artificial Neural

The prediction of different hydrological phenomenon (or system) plays an increasing role in the management of water resources. As engineers; it is required to predict the component of natural reservoirs’ inflow for numerous purposes. Resulting prediction techniques vary with the potential purpose, characteristics, and documented data. The best prediction method is of interest of experts to overcome the uncertainty, because the most hydrological parameters are subjected to the uncertainty. Artificial Neural Network (ANN) approach has adopted in this paper to predict Hemren reservoir inflow. Available data including monthly discharge supplied from DerbendiKhan reservoir and rain fall intensity falling on the intermediate catchment area between Hemren-DerbendiKhan dams were used.A Back Propagation (LMBP) algorithm (Levenberg-Marquardt) has been utilized to construct the ANN models. For the developed ANN model, different networks with different numbers of neurons and layers were evaluated. A total of 24 years of historical data for interval from 1980 to 2004 were used to train and test the networks. The optimum ANN network with 3 inputs, 40 neurons in both two hidden layers and one output was selected. Mean Squared Error (MSE) and the Correlation Coefficient (CC) were employed to evaluate the accuracy of the proposed model. The network was trained and converged at MSE = 0.027 by using training data subjected to early stopping approach. The network could forecast the testing data set with the accuracy of MSE = 0.031. Training and testing process showed the correlation coefficient of 0.97 and 0.77 respectively and this is refer to a high precision of that prediction technique.

Simultaneous Quantitative Analysis of Clorsulon and Ivermectin in a Veterinary Formulation by Artificial Neural Network

2008 ◽  
Vol 59 (10) ◽  
Author(s):  
Gozde Pektas ◽  
Erdal Dinc ◽  
Dumitru Baleanu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Simultaneaous spectrophotometric determination of clorsulon (CLO) and invermectin (IVE) in commercial veterinary formulation was performed by using the artificial neural network (ANN) based on the back propagation algorithm. In order to find the optimal ANN model various topogical networks were tested by using different hidden layers. A logsig input layer, a hidden layer of neurons using the logsig transfer function and an output layer of two neurons with purelin transfer function was found suitable for basic configuration for ANN model. A calibration set consisting of CLO and IVE in calibration set was prepared in the concentration range of 1-23 �g/mL and 1-14 �g/mL, repectively. This calibration set contains 36 different synthetic mixtures. A prediction set was prepared in order to evaluate the recovery of the investigated approach ANN chemometric calibration was applied to the simultaneous analysis of CLO and IVE in compounds in a commercial veterinary formulation. The experimental results indicate that the proposed method is appropriate for the routine quality control of the above mentioned active compounds.

scholarly journals Prediction of Drape Coefficient by Artificial Neural Network

2015 ◽  
Vol 15 (4) ◽  
pp. 266-274 ◽  
Author(s):  
Adel Ghith ◽  
Thouraya Hamdi ◽  
Faten Fayala
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Different Diameters

Abstract An artificial neural network (ANN) model was developed to predict the drape coefficient (DC). Hanging weight, Sample diameter and the bending rigidities in warp, weft and skew directions are selected as inputs of the ANN model. The ANN developed is a multilayer perceptron using a back-propagation algorithm with one hidden layer. The drape coefficient is measured by a Cusick drape meter. Bending rigidities in different directions were calculated according to the Cantilever method. The DC obtained results show a good correlation between the experimental and the estimated ANN values. The results prove a significant relationship between the ANN inputs and the drape coefficient. The algorithm developed can easily predict the drape coefficient of fabrics at different diameters.

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