scholarly journals Optimalisasi Model Artificial Neural Network Menggunakan Certainty Factor (C-ANN) Untuk Pemetaan Kerawanan Tanah Longsor Skala Semi-Detil di DAS Bendo, Kabupaten Banyuwangi

2021 ◽  
Vol 35 (1) ◽  
pp. 1
Author(s):  
Syamsul Bachri ◽  
Kresno Sastro Bangun Utomo ◽  
Sumarmi Sumarmi ◽  
Mohammad Naufal Fathoni ◽  
Yulius Eka Aldianto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Landslide Hazard ◽  
Susceptibility Mapping ◽  
Ann Model ◽  
Certainty Factor ◽  
Artificial Neural ◽  
Factor C

Kerawanan longsor di DAS Bendo termasuk dalam kerawanan kelas sedang hingga tinggi. Sampai dengan saat ini, pemetaan rawan longsor di DAS Bendo baru dilakukan pada  skala pemetaan 1:250.000. Penelitian ini bertujuan untuk melakukan pemodelan pemetaan kerawanan longsor di DAS Bendo pada skala semi-detil. Metode yang digunakan dalam penelitian ini adalah optimalisasi model artificial neural network menggunakan certainty factor (C-ANN). Peta kerawanan dibangun berdasarkan faktor pengontrol tanah longsor yang berkorelasi positif terhadap kejadian longsor menggunakan Certainty Factor. Sedangkan pemodelan prediksi kerawanan menggunakan model ANN, khususnya arsitektur BPNN (back-propagation neural network). Hasil pemodelan menunjukkan bahwa model C-ANN (7 variabel independen) memiliki nilai AUC (0,916) lebih tinggi daripada model ANN (0,778). Faktor redundansi data, multikolinieritas data, dan proporsi kejadian longsor terhadap cakupan wilayah penelitian mengakibatkan ketidakpastian dalam data variabel independen. Melalui penelitian ini ditemukan hasil bahwa kondisi kerawanan longsor di DAS Bendo masuk kategori tinggi, khususnya pada lereng atas Gunung Ijen, Rante, dan Merapi. Landslide disaster in DAS Bendo is categorized as moderate to highly susceptible. Until today, landslide hazard mapping in DAS Bendo has been carried out with a scale 1:250.000. This study aimed to model landslide susceptibility mapping on a semi-detailed scale. The method used in this research was the integration of the Certainty Factor with Artificial Neural Network models (C-ANN).The development of susceptibility mapping based on factors that positively correlate to landslide events using Certainty Factor. While the susceptibility prediction model using the ANN model, specifically the BPNN (back-propagation neural network) architecture. Modelling results show that the C-ANN model (7 independent variables) has an AUC value (0.916) higher than the ANN model (0.778). Data redundancy factors, multicollinearity of data, and the proportion of landslide events to the study area's coverage resulted in uncertainty in the independent variable data. This research found that the Landslide hazard in the Bendo Watershed is in the high category, especially on the upper slopes of Mount Ijen, Rante, and Merapi.

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.

Application of Neural Network in Urban Land Use Suitability Evaluation

2011 ◽  
Vol 474-476 ◽  
pp. 681-686
Author(s):  
Xiao Rui Zhang ◽  
Gang Chen
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Urban Land ◽  
Urban Land Use ◽  
Suitability Evaluation ◽  
Land Use Suitability ◽  
Artificial Neural

Urban land use suitability evaluation is the basic work of urban land use planning and management. The evaluation method is a core in urban land use suitability evaluation. Traditional urban land use suitability evaluation methods are GIS-based methods which often can not get satisfactory results for the complex nonlinear urban land use system. Artificial neural network is a frontier theory of complex non-linearity scientific and artificial intelligence science. It is a new method to evaluate urban land use suitability. This paper took the land use suitability evaluation of Hefei city as an example, building a back propagation neural network with 8 neurous of input layer, 5 neurons of hide layer and 3 neurons of output layer. The analysis shows: the high suitability area is 682.27 km2in Hefei city, being about 8.73% of the total study area; the middle suitability area is 5965.76 km2, or about 76.33% of the total area and the low suitability area is 1167.35 km2, or about 14.94% of the total area. The results reflect the actual situation in Hefei city. The study shows that the back propagation neural network model can overcome the shortcomings of traditional evaluation methods. It means that artificial neural network is suitable for urban land use suitability evaluation. This reflects that artificial neural network has great academic value and application prospect in urban land use suitability evaluation. It also reflects that this study can provide a new idea and method for urban land use suitability evaluation.

The use of artificial neural networks for modelling rumen fill

2020 ◽  
Author(s):  
Rasheed Adekunle Adebayo ◽  
Mehluli Moyo ◽  
Evariste Bosco Gueguim-Kana ◽  
Ignatius Verla Nsahlai
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Random Forest Model ◽  
Ann Model ◽  
Forest Model ◽  
Artificial Neural ◽  
Cattle And Sheep

Artificial Neural Network (ANN) and Random Forest models for predicting rumen fill of cattle and sheep were developed. Data on rumen fill were collected from studies that reported body weights, measured rumen fill and stated diets fed to animals. Animal and feed factors that affected rumen fill were identified from each study and used to create a dataset. These factors were used as input variables for predicting the weight of rumen fill. For ANN modelling, a three-layer Levenberg-Marquardt Back Propagation Neural Network was adopted and achieved 96% accuracy in prediction of the weight of rumen fill. The precision of the ANN model’s prediction of rumen fill was higher for cattle (80%) than sheep (56%). On validation, the ANN model achieved 95% accuracy in prediction of the weight of rumen fill. A Random Forest model was trained using a binary tree-based machine-learning algorithm and achieved 87% accuracy in prediction of rumen fill. The Random Forest model achieved 16% (cattle) and 57% (sheep) accuracy in validation of the prediction of rumen fill. In conclusion, the ANN model gave better predictions of rumen fill compared to the Random Forest model and should be used in predicting rumen fill of cattle and sheep.

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 Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhou Yang ◽  
Unsong Pak ◽  
Cholu Kwon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reliability Analysis ◽  
Sampling Method ◽  
Back Propagation ◽  
Simulation Method ◽  
Back Propagation Neural Network ◽  
Element Analysis ◽  
Analysis Methods ◽  
Artificial Neural

This research aims to evaluate the calculation accuracy and efficiency of the artificial neural network-based important sampling method (ANN-IS) on reliability of structures such as drum brakes. The finite element analysis (FEA) result is used to establish the ANN sample in ANN-based reliability analysis methods. Because the process of FEA is time-consuming, the ANN sample size has a very important influence on the calculation efficiency. Two types of ANNs used in this study are the radial basis function neural network (RBF) and back propagation neural network (BP). RBF-IS and BP-IS methods are used to conduct reliability analysis on training samples of three different sizes, and the results are compared with several reliability analysis methods based on ANNs. The results show that the probability of failure of the RBF-IS method is closer to that of the Monte-Carlo simulation method (MCS) than those of other methods (including BP-IS). In addition, the RBF-IS method has better calculation efficiency than the other methods considered in this study. This research demonstrates that the RBF-IS method is well suited to structure reliability problems.

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.

Prediction of infection and death ratio of CoVID-19 virus in Turkey by using artificial neural network (ANN)

Coronaviruses ◽  
2020 ◽  
Vol 01 ◽  
Author(s):  
Andaç Batur Çolak
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Death Rate ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
World Health ◽  
Feed Forward Back Propagation ◽  
The World ◽  
Artificial Neural ◽  
Health Organization

Background: For the first time in December 2019 as reported in the Whuan city of China COVID-19 deadly virus, spread rapidly around the world and the first cases were seen in Turkey on March 11, 2020. On the same day, a pandemic was declared by the World Health Organization due to the rapid spread of the disease throughout the world. Methods: In this study, a multilayered perception feed-forward back propagation neural network has been designed for predicting the spread and mortality rate of COVID-19 virus in Turkey. COVID-19 data from six different countries were used in the design of the artificial neural network, which has 15 neurons in its hidden layer. 70% of these optimized data were used for training, 20% for validation and 10% for testing. Results: The resulting simulation results, COVID-19 virus in Turkey between 20 and 37 days showed the fastest to rise. The number of cases for the 20th day was predicted to be 13.845 and the 51st day for the 37th day. Conclusion: As for the death rate, it was predicted that a rapid rise on the 20th day would start and a slowdown around the 43rd day and progress towards the zero case point. The death rate for the 20th day was predicted to be 170 and the 43rd day for the 1.960s.

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.

scholarly journals Prediction of Neutron Yield of IR-IECF Facility in High Voltages Using Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
A. Sadighzadeh ◽  
A. Salehizadeh ◽  
M. Mohammadzadeh ◽  
F. Shama ◽  
S. Setayeshi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Discharge Current ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Cathode Voltage ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Maximum Voltage

Artificial neural network (ANN) is applied to predict the number of produced neutrons from IR-IECF device in wide discharge current and voltage ranges. Experimentally, discharge current from 20 to 100 mA had been tuned by deuterium gas pressure and cathode voltage had been changed from −20 to −82 kV (maximum voltage of the used supply). The maximum neutron production rate (NPR) of 1.46 × 107 n/s had occurred when the voltage was −82 kV and the discharge current was 48 mA. The back-propagation algorithm is used for training of the proposed multilayer perceptron (MLP) neural network structure. The obtained results show that the proposed ANN model has achieved good agreement with the experimental data. Results show that NPR of 1.855 × 108 n/s can be achieved in voltage and current of 125 kV and 45 mA, respectively. This prediction shows 52% increment in maximum voltage of power supply. Also, the optimum discharge current can increase 1270% NPR.

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