Remote Sensing of Time-Varying Tidal Flat Topography, Jiangsu Coast, China, Based on the Waterline Method and an Artificial Neural Network Model

Measurement of beach heights in the intertidal zone has great importance for dynamic geomorphology research, coastal zone management, and the protection of ecological resources. Based on satellite images, the waterline method based on satellite images is one of the most effective methods for constructing digital elevation models (DEMs) for large-scale tidal flats. However, for fast-changing areas, such as Tiaozini in the Jiangsu coast, timely and detailed topographical data are difficult to obtain due to the insufficient images over a short period of time. In this study, as a supplement to the waterline method, an artificial neural network (ANN) model with the multi-layer feed-forward back propagation algorithm was developed to simulate the topography of variable Tiaozini tidal flats. The “7-15-15-1” double hidden layers with optimized training structures were confirmed via continuous training and comparisons. The input parameters included spectral bands (HJ-1 images B1~B4), geographical coordinates (X, Y), and the distance (D) to waterlines, and the output parameter was the elevation. The model training data were the HJ-1 image for 21 March 2014, and the corresponding topographic data obtained from the waterline method. Then, this ANN model was used to simulate synchronous DEMs corresponding to remote sensing images on 11 February 2012, and 11 July 2013, under low tide conditions. The height accuracy (root mean square error) of the two DEMs was about 0.3–0.4 m based on three transects of the in-situ measured data, and the horizontal accuracy was 30 m—the same as the spatial resolution of the HJ-1 image. Although its vertical accuracy is not very high, this ANN model can quickly provide the basic geomorphological framework for tidal flats based on only one image. This model, therefore, provides an effective way to monitor rapidly changing tidal flats.

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Artificial neural network model for precipitation forecast over Western Himalaya using satellite images

MAUSAM ◽

10.54302/mausam.v73i1.5083 ◽

2022 ◽

Vol 73 (1) ◽

pp. 83-90

Author(s):

PIYUSH JOSHI ◽

M.S. SHEKHAR ◽

ASHAVANI KUMAR ◽

J.K. QUAMARA

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Satellite Images ◽

India Meteorological Department ◽

Western Himalaya ◽

Probability Of Detection ◽

Precipitation Forecast ◽

Ann Model ◽

Skill Scores ◽

Artificial Neural

Kalpana satellite images in real time available by India meteorological department (IMD), contain relevant inputs about the cloud in infra-red (IR), water vapor (WV), and visible (VIS) bands. In the present study an attempt has been made to forecast precipitation at six stations in western Himalaya by using extracted grey scale values of IR and WV images. The extracted pixel values at a location are trained for the corresponding precipitation at that location. The precipitation state at 0300 UTC is considered to train the model for precipitation forecast with 24 hour lead time. The satellite images acquired in IR (10.5 - 12.5 µm) and WV (5.7 - 7.1 µm) bands have been used for developing Artificial Neural Network (ANN) model for qualitative as well as quantitative precipitation forecast. The model results are validated with ground observations and skill scores are computed to check the potential of the model for operational purpose. The probability of detection at the six stations varies from 0.78 for Gulmarg in Pir-Panjal range to 0.95 for Dras in Greater Himalayan range. Overall performance for qualitative forecast is in the range from 61% to 84%. Root mean square error for different locations under study is in the range 5.81 to 8.7.

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

Revista de Chimie ◽

10.37358/rc.08.10.1988 ◽

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.

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Prediction of Drape Coefficient by Artificial Neural Network

Autex Research Journal ◽

10.1515/aut-2015-0045 ◽

2015 ◽

Vol 15 (4) ◽

pp. 266-274 ◽

Cited By ~ 5

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

Journal of Engineering ◽

10.1155/2014/798160 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 1

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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Prediction of the thermo-physical properties of deep-fat frying plantain chips (ipekere) using artificial neural network

Acta Universitatis Cibiniensis Series E Food Technology ◽

10.2478/aucft-2021-0023 ◽

2021 ◽

Vol 25 (2) ◽

pp. 253-260

Author(s):

James Abiodun Adeyanju ◽

John Oluranti Olajide ◽

Emmanuel Olusola Oke ◽

Jelili Babatunde Hussein ◽

Chiamaka Jane Ude

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Physical Properties ◽

Back Propagation ◽

Ann Model ◽

Back Propagation Algorithm ◽

Deep Fat Frying ◽

Feed Forward Back Propagation ◽

Artificial Neural ◽

Thermo Physical Properties

Abstract This study uses artificial neural network (ANN) to predict the thermo-physical properties of deep-fat frying plantain chips (ipekere). The frying was conducted with temperature and time ranged of 150 to 190 °C and 2 to 4 minutes using factorial design. The result revealed that specific heat was most influenced by temperature and time with the value 2.002 kJ/kg°C at 150 °C and 2.5 minutes. The density ranged from 0.997 – 1.005 kg/m3 while thermal diffusivity and conductivity were least affected with 0.192 x 10−6 m2/s and 0.332 W/m°C respectively at 190 °C and 4 minutes. The ANN architecture was developed using Levenberg–Marquardt (TRAINLM) and Feed-forward back propagation algorithm. The experimentation based on the ANN model produced a desirable prediction of the thermo-physical properties through the application of diverse amount of neutrons in the hidden layer. The predictive experimentation of the computational model with R2 ≥ 0.7901 and MSE ≤ 0.1125 does not only show the validity in anticipating the thermo-physical properties, it also indicates the capability of the model to identify a relevant association between frying time, frying temperatures and thermo-physical properties. Hence, to avoid a time consuming and expensive experimental tests, the developed model in this study is efficient in prediction of the thermo-physical properties of deep-fat frying plantain chips.

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APPLICABILITY OF ARTIFICIAL NEURAL NETWORK MODEL FOR SIMULATION OF MONTHLY RUNOFF IN COMPARISON WITH SOM OTHER TRADITIONAL MODELS

Science and Technology Development Journal ◽

10.32508/stdj.v12i4.2237 ◽

2009 ◽

Vol 12 (4) ◽

pp. 94-106 ◽

Cited By ~ 1

Author(s):

Duc Van Le

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Back Propagation ◽

Research Trend ◽

Rainfall Runoff ◽

Ann Model ◽

Back Propagation Algorithm ◽

Runoff Forecasting ◽

Artificial Neural ◽

Artificial Neural Network Ann

Artificial Neural Network (ANN) model along with Back Propagation Algorithm (BPA) has been applied in many fields, especially in hydrology and water resources management to simulate or forecast rainfall runoff process, discharge and water level - time series, and other hydrological variables. Several researches have recently been focusing to compare the applicability of ANN model with other theory-driven and data-driven approaches. The comparison of ANN with M5 model trees for rainfall-runoff forecasting, with ARMAX models for deriving flow series, with AR models and regression models for forecasting and estimating daily river flows have been carried out. The better results that were implemented by ANN model have been concluded. So, this research trend is continued for the comparison of ANN model with Tank, Harmonic, Thomas and Fiering models in simulation of the monthly runoffs at Dong Nai river basin, Viet Nam. The results proved ANN being the best choice among these models, if suitable and enough data sources were available.

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Modeling of Rainfall-Runoff Correlations Using Artificial Neural Network-A Case Study of Dharoi Watershed of a Sabarmati River Basin, India

Civil Engineering Journal ◽

10.28991/cej-2017-00000074 ◽

2017 ◽

Vol 3 (2) ◽

pp. 78-87 ◽

Cited By ~ 7

Author(s):

Ajaykumar Bhagubhai Patel ◽

Geeta S. Joshi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

River Basin ◽

Annual Rainfall ◽

Rainfall Runoff ◽

Ann Model ◽

Back Propagation Algorithm ◽

Feed Forward Back Propagation ◽

Sabarmati River ◽

Artificial Neural

The use of an Artificial Neural Network (ANN) is becoming common due to its ability to analyse complex nonlinear events. An ANN has a flexible, convenient and easy mathematical structure to identify the nonlinear relationships between input and output data sets. This capability could efficiently be employed for the different hydrological models such as rainfall-runoff models, which are inherently nonlinear in nature. Artificial Neural Networks (ANN) can be used in cases where the available data is limited. The present work involves the development of an ANN model using Feed-Forward Back Propagation algorithm for establishing monthly and annual rainfall runoff correlations. The hydrologic variables used were monthly and annual rainfall and runoff for monthly and annual time period of monsoon season. The ANN model developed in this study is applied to Dharoi reservoir watersheds of Sabarmati river basin of India. The hydrologic data were available for twenty-nine years at Dharoi station at Dharoi dam project. The model results yielding into the least error is recommended for simulating the rainfall-runoff characteristics of the watersheds. The obtained results can help the water resource managers to operate the reservoir properly in the case of extreme events such as flooding and drought.

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An Artificial Neural Network Prediction Model for Posttraumatic Epilepsy: Retrospective Cohort Study (Preprint)

10.2196/preprints.25090 ◽

2020 ◽

Author(s):

Xueping Wang ◽

Jie Zhong ◽

Ting Lei ◽

Deng Chen ◽

Haijiao Wang ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Prediction Model ◽

Cross Validation ◽

Ann Model ◽

Back Propagation Algorithm ◽

Training Cohort ◽

Posttraumatic Epilepsy ◽

Artificial Neural ◽

Sensitivity Specificity

BACKGROUND Posttraumatic epilepsy (PTE) is a common sequela after traumatic brain injury (TBI), and identifying high-risk patients with PTE is necessary for their better treatment. Although artificial neural network (ANN) prediction models have been reported and are superior to traditional models, the ANN prediction model for PTE is lacking. OBJECTIVE We aim to train and validate an ANN model to anticipate the risks of PTE. METHODS The training cohort was TBI patients registered at West China Hospital. We used a 5-fold cross-validation approach to train and test the ANN model to avoid overfitting; 21 independent variables were used as input neurons in the ANN models, using a back-propagation algorithm to minimize the loss function. Finally, we obtained sensitivity, specificity, and accuracy of each ANN model from the 5 rounds of cross-validation and compared the accuracy with a nomogram prediction model built in our previous work based on the same population. In addition, we evaluated the performance of the model using patients registered at Chengdu Shang Jin Nan Fu Hospital (testing cohort 1) and Sichuan Provincial People’s Hospital (testing cohort 2) between January 1, 2013, and March 1, 2015. RESULTS For the training cohort, we enrolled 1301 TBI patients from January 1, 2011, to December 31, 2017. The prevalence of PTE was 12.8% (166/1301, 95% CI 10.9%-14.6%). Of the TBI patients registered in testing cohort 1, PTE prevalence was 10.5% (44/421, 95% CI 7.5%-13.4%). Of the TBI patients registered in testing cohort 2, PTE prevalence was 6.1% (25/413, 95% CI 3.7%-8.4%). The results of the ANN model show that, the area under the receiver operating characteristic curve in the training cohort was 0.907 (95% CI 0.889-0.924), testing cohort 1 was 0.867 (95% CI 0.842-0.893), and testing cohort 2 was 0.859 (95% CI 0.826-0.890). Second, the average accuracy of the training cohort was 0.557 (95% CI 0.510-0.620), with 0.470 (95% CI 0.414-0.526) in testing cohort 1 and 0.344 (95% CI 0.287-0.401) in testing cohort 2. In addition, sensitivity, specificity, positive predictive values and negative predictors in the training cohort (testing cohort 1 and testing cohort 2) were 0.80 (0.83 and 0.80), 0.86 (0.80 and 0.84), 91% (85% and 78%), and 86% (80% and 83%), respectively. When calibrating this ANN model, Brier scored 0.121 in testing cohort 1 and 0.127 in testing cohort 2. Compared with the nomogram model, the ANN prediction model had a higher accuracy (<i>P</i>=.01). CONCLUSIONS This study shows that the ANN model can predict the risk of PTE and is superior to the risk estimated based on traditional statistical methods. However, the calibration of the model is a bit poor, and we need to calibrate it on a large sample size set and further improve the model.

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COVID-19 screening: use of an artificial neural network

BMJ Supportive & Palliative Care ◽

10.1136/bmjspcare-2021-003391 ◽

2021 ◽

pp. bmjspcare-2021-003391

Author(s):

Narges Roustaei ◽

Elahe Allahyari

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Clinical Symptoms ◽

Gastrointestinal Symptoms ◽

Sociodemographic Factors ◽

Ann Model ◽

Back Propagation Algorithm ◽

Feed Forward Back Propagation ◽

Artificial Neural ◽

At Home

ObjectivesCOVID-19 is the biggest pandemic of the 21st century. The disease can be influenced by various sociodemographic factors and can manifest as clinical, pulmonary and gastrointestinal symptoms. This study used an artificial neural network (ANN) model with important sociodemographic factors as well as clinical, pulmonary and gastrointestinal symptoms to screen patients for COVID-19. Patients themselves can screen for these symptoms at home.MethodsData on all registered patients were extracted in autumn. The best ANN model was selected from different combinations of connections, some hidden layers and some neurons in each hidden layer. In this study, 70% of the data were used in the network training process and the remaining 30% were used to evaluate the function of the multilayer, feed-forward, back-propagation algorithm.ResultsThe sensitivity and specificity of the ANN model in diagnosing patients with COVID-19 were 94.5% and 17.4%. In order of priority, clinical symptoms, sociodemographic factors, pulmonary symptoms and gastrointestinal symptoms were important predictive factors for COVID-19 using the ANN model. Screening patients for COVID-19 using clinical symptoms and sociodemographic factors (80% importance) remains essential.ConclusionsHome monitoring of oxygen saturation and body temperature as well as old age and drug addiction can be helpful in self-screening symptoms of COVID-19 at home, thereby preventing unnecessary visits to medical centres and reducing burden on medical services.

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Estimation and Mapping of Solar Irradiance for Korea by Using COMS MI Satellite Images and an Artificial Neural Network Model

Energies ◽

10.3390/en13020301 ◽

2020 ◽

Vol 13 (2) ◽

pp. 301 ◽

Cited By ~ 1

Author(s):

YoungHyun Koo ◽

Myeongchan Oh ◽

Sung-Min Kim ◽

Hyeong-Dong Park

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Solar Irradiance ◽

Satellite Images ◽

Accurate Estimation ◽

Ensemble Model ◽

Solar Pv ◽

Ann Model ◽

Pv Systems ◽

Artificial Neural

The power capacity of solar photovoltaics (PVs) in Korea has grown dramatically in recent years, and an accurate estimation of solar resources is crucial for the efficient management of these solar PV systems. Since the number of solar irradiance measurement sites is insufficient for Korea, satellite images can be useful sources for estimating solar irradiance over a wide area of Korea. In this study, an artificial neural network (ANN) model was constructed to calculate hourly global horizontal solar irradiance (GHI) from Korea Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) images. Solar position variables and five COMS MI channels were used as inputs for the ANN model. The basic ANN model was determined to have a window size of five for the input satellite images and two hidden layers, with 30 nodes on each hidden layer. After these ANN parameters were determined, the temporal and spatial applicability of the ANN model for solar irradiance mapping was validated. The final ANN ensemble model, which calculated the hourly GHI from 10 independent ANN models, exhibited a correlation coefficient (R) of 0.975 and root mean square error (RMSE) of 54.44 W/m² (12.93%), which were better results than for other remote-sensing based works for Korea. Finally, GHI maps for Korea were generated using the final ANN ensemble model. This COMS-based ANN model can contribute to the efficient estimation of solar resources and the improvement of the operational efficiency of solar PV systems for Korea.

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