Back-propagation Artificial Neural Network Model Research Articles

Artificial intelligence-based precise prediction of anthropometric data for female garment pattern-making

Anthropometric data form the cornerstone of garment pattern-making. This article introduces an artificial intelligence-driven approach, employing a back-propagation artificial neural network (BP-ANN), to predict the anthropometric data essential for crafting patterns for women’s upper tops. The model adeptly processes minimal critical data from women’s upper bodies, yielding projected dimensions that are arduous to manually measure yet crucial for tailoring body-fitting tops. Utilising a three-dimensional body scanner for accurate anthropometric data collection from 196 women in Sichuan Province, China, our study compares the BP-ANN model with a Linear Regression (LR) model. Results demonstrate superior predictive accuracy for BP-ANN. Notably, the BP-ANN model excels in efficiency and accuracy, particularly in challenging anthropometric parameters. The findings underscore the transformative potential of AI-based models in optimizing garment production processes, offering a precise alternative to traditional methods. This research contributes valuable insights for the integration of AI technology in advancing pattern-making practices.

Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models

Assessing riverine pollutant loads is a more realistic method for analysing point and non-point anthropogenic pollution sources throughout a watershed. This study compares numerous mathematical modelling strategies for estimating riverine loads based on the chosen water quality parameters: Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Suspended Solids (SS), and Ammoniacal Nitrogen (NH3–N). A riverine load model was developed by employing various input variables including river flow and pollutant concentration values collected at several monitoring sites. Among the mathematical modelling methods employed are artificial neural networks with feed-forward backpropagation algorithms and radial basis functions. The classical multiple linear regression (MLR) statistical model was used for the comparison. Four widely used statistical performance assessment metrics were adopted to evaluate the performance of the various developed models: the root mean square error (RMSE), mean absolute error (MAE), mean relative error (MRE), and coefficient of determination (R2). The considerable number of errors (with RMSE, MAE, and MRE) discovered in estimating riverine loads using the multiple linear regression (MLR) statistical model can be attributed to the nonlinear relationship between the independent variables (Q and Cx) and dependent variables (W). The feed-forward neural network model with a backpropagation algorithm and Bayesian regularisation training algorithm outperformed the radial basis neural network. This finding implies that, in addition to suspended sediment loads, riverine loads may be predicted using an artificial neural network using pollutant concentration (Cx) and river discharge (Q) as input variables. Other geographical and temporal fluctuation characteristics that may impact river water quality, on the other hand, may be incorporated as input variables to enhance riverine load prediction. Finally, riverine load analyses were successfully conducted to reduce the riverine load.

Dynamics optimization of small branch pipes in nuclear power plants based on machine learning algorithms

Vibration fatigue failure of small branch pipes poses a great threat to the safe operation of nuclear power plants. However, the transient and wide-band vibration problems are not adequately considered in ASME and RCC-M code, resulting in repeated fatigue failures. In addition, the current research mainly focuses on vibration test methods and fatigue analysis methods, neglecting the study of pipeline vibration characteristics. Therefore, innovative approaches were essential for effectively managing complex dynamic loads. In this study, an innovative approach combining backpropagation artificial neural networks (BP-ANN) and non-dominated sorting Genetic Algorithm II (NSGA-II) was proposed to optimize the vibration of these pipes. The goal was mitigating vibration-induced failures by enhancing operational stability. The methodology progressed through several key stages. Firstly, BP-ANN was utilized for regression analysis, correlating pipe characteristics to vibration effects. Through regression analysis, the complex interrelationships governing the pipes' dynamic behavior was revealed. Subsequently, based on the regression model, NSGA-II was used to derive an optimal combination of design parameters to minimize the vibration response. The proposed technique was validated on an L-shaped cantilevered pipe via finite element simulations and physical experiments. The analysis case shows that the BP-ANN model demonstrated excellent accuracy in predicting vibration responses. Meanwhile, NSGA-II successfully revealed the trade-offs between conflicting objectives, generating a Pareto-optimal set balancing stability under different excitation directions. This study highlights the potential of machine learning methods for dynamic optimization of small branch pipes in nuclear power plants, and verifies the accuracy and effectiveness of the method through experiments. The research results provide a new idea for small branch pipe design and vibration control of nuclear power plant, which contributes to enhancing the safety and reliability of small branch pipes.

Prediction and visualization of moisture content in Tencha drying processes by computer vision and deep learning.

It is important to monitor and control the moisture content throughout the Tencha drying processing procedure so that its quality is ensured. Workers often rely on their senses to perceive the moisture content, leading to relative subjectivity and low reproducibility. Traditional drying methods, which are used for measuring moisture content, are destructive to samples. This research was conducted using computer vision combined with deep learning to detect moisture content during the Tencha drying process. Different color space components of Tencha drying sample images were first extracted by computer vision. The color components were preprocessed using MinMax and Z score. Subsequently, one-dimensional convolutional neural networks (1D-CNN), partial least squares, and backpropagation artificial neural networks models were built and compared. The 1D-CNN model and Z scorepreprocessing achieved superior predictive accuracy, with correlation coefficient of prediction (Rp ) = 0.9548 for moisture content. The migration of moisture content during the Tencha drying process was eventually visualized by mapping its spatial and temporal distributions. The results indicated that computer vision combined with 1D-CNN was feasible for moisture prediction during the Tencha drying process. This study provides technical support for the industrial and intelligent production of Tencha. © 2024 Society of Chemical Industry.

Classification of Banana Types Based on The Geometrical Attributes using Artificial Neural Network Method

Banana (Musa paradisiaca) is one of the important horticultural commodities. This study aims to measure the physical and geometrical parameters of three different bananas (Muli, Ambon, and Kepok) and to develop prediction equations using an Artificial Neural Network (ANN) model. In this study the backpropagation ANN model with supervised learning method was used. The ANN model had one output node, two hidden layers, and network architecture of 8 inputs, namely fruit weight and volume, projected area and roundness of the fruit, cross section, peel color, and geometric mean fruit cross section diameter. The data for building the model and testing the model were respectively 70% and 30% of the 150 data number in total. The results showed that the best ANN model structure for estimating Muli, Ambon and Kepok bananas was purelin-logsig-logsig with an RMSE value of 0.0077 and an R2 of 0.9999. This shows that the ANN model is highly robust to predict the banana types. Using the built model, the accuracy of the prediction results is 100%. Keywords: Artificial Neural Network, Banana fruits, Geometry attribute.

Application of Northern Goshawk Back-Propagation Artificial Neural Network in the Prediction of Monohydroxycarbazepine Concentration in Patients with Epilepsy.

A northern goshawk back-propagation artificial neural network (NGO-BPANN) model was established to predict monohydroxycarbazepine (MHD) concentration in patients with epilepsy. The data were collected from 108 Han Chinese patients with epilepsy on oxcarbazepine monotherapy. The results of 14 genotype variates were selected as the input layer in the first BPANN model, and the variables that had a more significant impact on the plasma concentration of MHD were retained. With demographic characteristics and clinical laboratory test results, the genotypes of SCN1A rs2298771 and SCN2A rs17183814 were used to construct the BPANN model. The BPANN model was comprehensively validated and used to predict the MHD plasma concentration of five patients with epilepsy in our hospital. The model demonstrated favorable fitness metrics, including a mean squared error of 0.00662, a gradient magnitude of 0.00753, an absence of validation tests amounting to zero, and a correlation coefficient of 0.980. Sex, BMI, and the genotype SCN1A rs2298771 were ranked highest by the absolute mean impact value (MIV), which is primarily associated with the concentration of MHD. The test group exhibited a range of - 20.84% to 31.03% bias between the predicted and measured values, with a correlation coefficient of 0.941 between the two. With BPANN, the MHD nadir concentration could be predicted precisely. The NGO-BPANN model exhibits exceptional predictive capability and can be a practical instrument for forecasting MHD concentration in patients with epilepsy. www.chiCTR-OOC-17012141 .

Formation and prediction of heterocyclic amines and N-nitrosamines in smoked sausages using back propagation artificial neural network.

Heterocyclic amines (HAs) and N-nitrosamines (NAs) are formed easily during the thermal processing of food, and epidemiological studies have demonstrated that consuming HAs and NAs increases the risk of cancer. However, there are few studies on the application of back propagation artificial neural network (BP-ANN) models to simultaneously predict the content of HAs and NAs in sausages. This study aimed to investigate the effects of cooking time and temperature, smoking time and temperature, and fat-to-lean ratio on the formation of HAs and NAs in smoked sausages, and to predict their total content based on the BP-ANN model. With an increase in processing time, processing temperature and fat ratio, the content of HAs and NAs in smoked sausages increased significantly, while the content of HA precursors and nitrite residues decreased significantly. The optimal network topology of the BP-ANN model was 5-11-2, the correlation coefficient values for training, validation, testing and all datasets were 0.99228, 0.99785, 0.99520 and 0.99369, respectively, and the mean squared error value of the best validation performance was 0.11326. The bias factor and the accuracy factor were within acceptable limits, and the predicted values approximated the true values, indicating that the model has good predictive performance. The contents of HAs and NAs in smoked sausages were significantly influenced by the cooking conditions, smoking conditions and fat ratio. The BP-ANN model has high application value in predicting the contents of HAs and NAs in sausages, which provides a theoretical basis for the suppression of carcinogen formation. © 2024 Society of Chemical Industry.

Predictive Quality Analytics of Surface Roughness in Turning Operation Using Polynomial and Artificial Neural Network Models

The variability of the material properties of steel from different suppliers causes problems in achieving the required surface quality after turning. Therefore, the manufacturer needs to estimate the resulting quality before starting production, especially if it is an expensive, small-batch production from stainless steel. Predictive models will make it possible to estimate the surface roughness from the mechanical properties of steel and thus support decision making about supplier selection or acceptance of a material supply. This research presents a step-by-step decision-making procedure, which enables the trained staff to make quick decisions based on commonly available information in the Mill Test Certificate (MTC). A new multivariate second-order polynomial model and feedforward backpropagation artificial neural network (ANN) models have been developed using input variables from the MTC: Tensile Strength, Yield Strength, Elongation, and Hardness. Models were used to enhance the methodological robustness in formulating the decision if the predicted surface roughness is outside the required range, even before accepting the delivery. Both models can accurately predict surface roughness, while the ANN model is more accurate than the polynomial model; however, the predictive model is sensitive to the accuracy of the input data, and the model’s prediction is valid only under precisely defined conditions.

Assessing the impact of occlusal plane rotation on facial aesthetics in orthodontic treatment: a machine learning approach

BackgroundAdequate occlusal plane (OP) rotation through orthodontic therapy enables satisfying profile improvements for patients who are disturbed by their maxillomandibular imbalance but reluctant to surgery. The study aims to quantify profile improvements that OP rotation could produce in orthodontic treatment and whether the efficacy differs among skeletal types via machine learning.Materials and methodsCephalometric radiographs of 903 patients were marked and analyzed by trained orthodontists with assistance of Uceph, a commercial software which use artificial intelligence to perform the cephalometrics analysis. Back-propagation artificial neural network (BP-ANN) models were then trained based on collected samples to fit the relationship among maxillomandibular structural indicators, SN-OP and P-A Face Height ratio (FHR), Facial Angle (FA). After corroborating the precision and reliability of the models by T-test and Bland-Altman analysis, simulation strategy and matrix computation were combined to predict the consequent changes of FHR, FA to OP rotation. Linear regression and statistical approaches were then applied for coefficient calculation and differences comparison.ResultsThe regression scores calculating the similarity between predicted and true values reached 0.916 and 0.908 in FHR, FA models respectively, and almost all pairs were in 95% CI of Bland-Altman analysis, confirming the effectiveness of our models. Matrix simulation was used to ascertain the efficacy of OP control in aesthetic improvements. Intriguingly, though FHR change rate appeared to be constant across groups, in FA models, hypodivergent group displayed more sensitive changes to SN-OP than normodivergent, hypodivergent group, and Class III group significantly showed larger changes than Class I and II.ConclusionsRotation of OP could yield differently to facial aesthetic improvements as more efficient in hypodivergent groups vertically and Class III groups sagittally.

Intelligence detection of oil absorption in French fries by surface profiles

Surface profiles are important evaluation indices for oil absorption behavior of fried foods. This research established two intelligent models of partial least-squares regression (PLSR) and back propagation artificial neural network (BP-ANN) for monitoring the oil absorption behavior of French fries based on the surface characteristics. Surface morphology and texture of French fries by rapeseed oil (RO) and high-oleic peanut oil (HOPO) at different temperatures were investigated. Results showed that oil content of samples increased with frying temperature, accounting for 37.7% and 41.4% of samples fried by RO and HOPO respectively. The increase of crust ratio, roughness and texture parameters (Fm, Nwr, fwr, Wc) and the decrease of uniformity were observed with the frying temperature. Coefficients of prediction set of PLSR and BP-ANN models were more than 0.93, which indicated that surface features combined with chemometrics were rapid and precise methods for determining the oil content of French fries.

Bioenergy potential from agricultural by-product in 2030: An AI-based spatial analysis and climate change scenarios in a Chinese region

Addressing the pressing need for sustainable bioenergy sources amid the growing climate crisis, this study investigates potential crop bioenergy output in the Jianghan Plain, Hubei, China. The research leverages Backpropagation Artificial Neural Network models to predict the accumulated net primary productivity (NPP) of various crops, i.e., single rice, double rice, maize, and wheat, in Jianghan Plain. The models highlight a strong predictive performance, with the SE model achieving an R2 of 0.83 and wheat model an R2 of 0.90, implying these models can be utilized reliably for future NPP forecasting. Our evaluation of past and future NPP values reveals a negative correlation between the increase in NPP values and observed temperature trends. This emphasizes the significant impact of temperature on crop bioenergy potential. For future NPP predictions, scenario (S3a) under Temperature Control strategies at medium-effort mitigation yields a favourable outcome for all crops, particularly SE. Additionally, the results suggest that aggressive decarbonization and global warming mitigation strategies are vital for enhancing NPP values. However, the study also highlights potential trade-offs and stresses the necessity for region-specific climate reconciliation strategies; for instance, midland counties benefit from temperature control, whereas the sideline favours CO2 mitigation. The study further investigates the spatial distribution of potential bioenergy, with the southwestern part of the plain holding more bioenergy potential, providing an essential reference for future energy planning. By 2030, in Jianghan Plain, when fully integrated with reasonable climate responses, there could be substantial bioenergy potential from crop residues reaching (1.2–1.9) × 109 MJ, which is 11% less than the current.

Predicting the medium-temperature thermal stability of impact-strengthened medium-thick plate aluminum alloy using a back propagation artificial neural network

A normalized medium-thick plate of aluminum alloy (4038) was impact-strengthened using a free-fall method at room temperature (approximately 20 °C). Specimens were then aged at 450 °C, 550 °C and 650 °C for 10, 20, 30 and 40 min respectively. Micro-hardness of each sample was tested. Micro-structure of samples annealed at 650 °C for different durations was characterized. A three-layer back propagation artificial neural network (BPANN) was trained using actual state parameters of the prepared samples. Results reveal that medium-temperature thermal stability of the prepared plate can be predicted through the BPANN model. Deviation of predicted values from the experimental ones is within 6 %, with a prediction accuracy exceeding 94 %. Variation trend of the predicted and the experimental thermal stability is consistent, but the predicted values are all higher than the measurements. Prediction accuracy of BPANN can be improved by increasing convergence rate of the error function. By adding relevant parameters of the micro-structure from samples aged at 650 °C to the input layer, BPANN model further improve its output and approach the real state of samples. The findings of this study can help researchers reduce the number and cost of experiments. The aim of this work was to predict the medium-temperature thermal stability of impact-strengthened normalized medium-thick plate of aluminum alloy annealed at different temperatures, and it also can be used as reference for other similar experiments.

Food safety testing by negentropy-sorted kernel independent component analysis based on infrared spectroscopy

In the field of food safety testing, variety, brand, origin, and adulteration are four important factors. In this study, a novel food safety testing method based on infrared spectroscopy is proposed to investigate these factors. Fourier transform infrared spectroscopy data are analyzed using negentropy-sorted kernel independent component analysis (NS-kICA) as the feature optimization method. To rank the components, negentropy is performed to measure the non-Gaussian independent components. In our experiment, the proposed method was run on four datasets to comprehensively investigate the variety, brand, origin, and adulteration of agricultural products. The experimental results show that NS-kICA outperforms conventional feature selection methods. The support vector machine model outperforms the backpropagation artificial neural network and partial least squares models. The combination of NS-kICA and support vector machine (SVM) is the best method for achieving high, stable, and efficient recognition performance. These findings are of great importance for food safety testing.

Comparative QSAR Modeling for Predicting Anticancer Potency of Imidazo[4,5-b]Pyridine Derivatives Using GA-MLR and BP-ANN Techniques

Background: Prediction of toxicity of imidazo[4,5-b]pyridine derivatives is carried out using GA-MLR and BPANN methods. Objective: A quantitative structure-property relationship (QSPR) was determined based on methods, including genetic algorithm-multiple linear regression (GA-MLR) and backpropagation artificial neural network (BP-ANN). These methods were employed for modeling and predicting the anticancer potency of imidazo[4,5-b]pyridine derivatives. Materials and Methods: A dataset of imidazo[4,5-b]pyridine derivatives was randomly divided into two groups, training and test sets consisting of 75% and 25% of data points, respectively. The optimized conformation of compounds was obtained using the DFT-B3LYP method and 6-31G* basis sets level with Gaussian 09 software. A large number of molecular descriptors were calculated using Dragon software. The QSAR models were optimized using multiple linear regressions (MLR). Results: The most relevant molecular descriptors were obtained using the genetic algorithm (GA) and backward stepwise regression. The predictive powers of the GA-MLR models were studied using leaveone- out (LOO) cross-validation and an external test set. Conclusion: The obtained results of statistical parameters showed the BP-ANN model to have better performance compared to the GA-MLR model. To assess the predictive ability of QSAR models, many statistical terms, such as correlation coefficient (R2), leave-one-out cross-validation (LOOCV), root mean squared error (RMSE), and external and internal validation were used. The results of validation methods demonstrate the QSAR model to be robust and with high predictivity.

Back propagation artificial neural network (BP-ANN) for prediction of the quality of gamma-irradiated smoked bacon

This study investigated the effect of gamma irradiation on smoked bacon quality during storage and developed a multi-quality prediction model based on gamma irradiation. Gamma irradiation reduced moisture content and improved the microbial safety of smoked bacon. It also accelerated protein and lipid oxidation and altered free amino acids and fatty acids composition. It was effective in slowing down quality deterioration and sensory quality decline during storage. The backpropagation artificial neural network (BP-ANN) model was constructed by using physical and chemical indicators, irradiation dose, and storage time as input variables, and the total number of colonies and sensory scores as output layers. The transfer functions of the input-hidden layer and hidden-output layer were ReLu and Sigmoid, respectively. There were 13 neurons in the hidden layer. Results showed that BP-ANN based on physical and chemical indicators, irradiation dose, and storage time had great potential in predicting the multiple quality of smoked bacon.

Infilling of missing data in groundwater pollution prediction models using statistical methods

ABSTRACT Missing data is ubiquitous in hydrology. This phenomenon poses difficulty in the development of data-driven models. Events of missing data in groundwater pollution monitoring networks may occur due to failure of recording devices, malfunctioning of sensors, etc. Handling such missing data implies filling the missing portions of the data structure. Though several studies are available for dealing with missing data in the field of hydrology, literature dealing with such scenarios in groundwater pollution prediction is scarce. This paper assesses four imputation techniques – viz. linear, cubic spline, piece-wise cubic Hermite and modified Akima with cubic Hermite interpolation methods – for developing groundwater pollution prediction models using artificial neural network (ANN). The study employs the development of cascade-forward back-propagation ANN models using missing data ranging from 5% to 75% and evaluating their performance. Results show that imputation techniques can be effective in such circumstances.

A Multi-Factor Combination Model for Medium to Long-Term Runoff Prediction Based on Improved BP Neural Network

Taking a certain coastal area of Jiangsu province as the research background, this study scientifically predicts the runoff on the medium and long-term time scale according to the changes of various climate factors such as atmospheric circulation, sea surface temperature, and solar activity in the first half of the year. A lag correlation is established between various related climate factors and the monthly runoff process in the research area for the previous 1–6 months. Selecting advantageous factors and constructing a significant factor set. Using the improved BP (Back-Propagation) artificial neural network model and combining it with the sensitivity analysis method, a specific number of 8-factor combinations are selected from the set of significant factors for medium and long-term runoff prediction. After that, the prediction results are compared with the forecasting effects of two multi-factor combination runoff simulation schemes formed by stepwise regression and Spearman rank correlation methods. The study concluded that the multi-factor combination simulation effect formed through sensitivity analysis was the best. The 20% standard forecast qualification rate of the three schemes is not significantly different. The Mean Absolute Relative Error of the multi-factor combination training and validation periods simulated through sensitivity analysis is the smallest among the three schemes, which are 36.61% and 38.01%, respectively. The Nash Efficiency Coefficient in the validation period is 0.45, which is far better than other schemes and has better generalization ability. The Standard Deviation of Relative Error in the training and validation periods is much smaller than other schemes, and the dispersion of relative errors is the smallest.

Prediction of biological nutrients removal in full-scale wastewater treatment plants using H2O automated machine learning and back propagation artificial neural network model: Optimization and comparison

The effective control of total nitrogen (ETN) and total phosphorus (ETP) in effluent is challenging for wastewater treatment plants (WWTPs). In this work, automated machine learning (AutoML) (mean square error = 0.4200 ∼ 3.8245, R2 = 0.5699 ∼ 0.6219) and back propagation artificial neural network (BPANN) model (mean square error = 0.0012 ∼ 6.9067, R2 = 0.4326 ∼ 0.8908) were used to predict and analyze biological nutrients removal in full-scale WWTPs. Interestingly, BPANN model presented high prediction performance and general applicability for WWTPs with different biological treatment units. However, the AutoML candidate models were more interpretable, and the results showed that electricity carbon emission dominated the prediction. Meanwhile, increasing data volume and types of WWTP hardly affected the interpretable results, demonstrating its wide applicability. This study demonstrated the validity and the specific advantages of predicting ETN and ETP using H2O AutoML and BPANN model, which provided guidance on the prediction and improvement of biological nutrients removal in WWTPs.

Prediction of membrane purification by membrane fouling based on mathematic and machine learning models combined with image processing technology

Apples have been widely cultivated due to their multiple functional nutrients such as polyphenols. In this study, polyphenol of apple skins were extracted and purified by ultrasound treatment and membrane filtration, respectively. The effects of feed liquid concentration, transmembrane pressure, and rotational speed on the purification of polyphenols by membrane filtration were mainly investigated. The efficiency of polyphenol purification was analyzed based on the particle deposition effect, steric rejection effect, and pectin sieving effect. Single and multi-stage Hermia models were applied to corroborate the aforementioned theory. The degree of membrane fouling was quantitatively analyzed using a self-written image processing program. The study compared the quantitative prediction models between the degree of membrane fouling and polyphenol purification using mathematical models and two machine learning (ML) models, namely Support Vector Machine Regression (SVR) and Back Propagation Artificial neural networks (BPNN). The degree of membrane fouling and the degree of polyphenol purification were used as input parameters and output parameters of the ML prediction model, respectively. The ratio of the training set (98 data points) and the test set (42 data points) was 7:3. The obtained results showed that the prediction efficiency of BPNN model (R2 =0.99968) outperformed the SVR model (R2 =0.97697), which was better than the mathematical model in terms of prediction performance due to the high generalization ability of the ML model and the high robustness of BPNN. The study provided an effective and accurate method for predicting membrane fouling and filtration purification. Thus, the use of ML as an artificial intelligence technique has a promising future in membrane separation and purification.

Geo-spatial intelligence for searching and rescuing man overboard incidents using an artificial neural network: An empirical study of the Royal Thai Navy in the Gulf of Thailand

Man overboard (MOB) incidents happened continuously and they are the main type of suffering incident at the sea. From the MOB situation, searching and rescuing (SAR) operations are occurring in global coastal countries. According to the literature reviews on SAR operations, most studies presented the methodology to search the MOB in better performance indicators after the MOB occurred however, no evidence has been found on using Geo-spatial intelligence with Artificial Neural Networks (ANN) to predict MOB incidents under various weather conditions before the MOB occurred. The objective of this research, therefore, is to propose an intelligent methodology to reduce the searching and rescuing operation time of MOB incidents. A model of Artificial Neural Network (ANN) is applied due to its ability to accurately predict location and practicality of the Royal Thai Navy (RTN). The MOB incidents from 2019 to 2022 are collected and generated for the methodology implementation. This research utilizes the advantage of the Back-Propagation Artificial Neural Network (BP-ANN) to apply in prediction model in which the input variables are various weather condition information including the location of MOB from Geo-spatial, and the output variables are MOB location in the form of Latitude and Longitude. The appropriate BP-ANN model is created under the trial and error method, and the results show that the BP-ANN can predict the location of the MOB accurately. The benefit of this research is to reduce the time consumed by SAR operations in the Gulf of Thailand. As a result, when the traditional SAR operation time is compared with the SAR operation time by using BP-ANN predicting model, it was found that the SAR operation time by using BP-ANN predicting model is more accurate and can reduce the operation time by 5 hrs.