A back-propagation artificial neural network approach for three-dimensional coordinate transformation

The study seeks to assess the efficacy of utilizing a Back Propagation Artificial Neural Network (BPANN) as an alternative to conventional methodologies for 2D coordinate transformation. Converting the previously used reference system in Iraq to the global system is one of the main requirements that play a main role in various applications requiring the standardization of geographic referencing for different data, including geospatial applications, remote sensing, image processing, and more. This necessitates researching and finding the most suitable methods to ensure high accuracy in the results. The Karbala 1979 (Clark 1880) of the Iraqi national geodetic network and the Iraqi Geospatial Reference System (IGRS) were currently used as geodetic reference frames in Iraq. In the context of transitioning between the local system (Karbala 1979) and the global system, specifically IGRS (ITRF2000 at epoch 1997.0), a crucial procedure involves the conversion of two-dimensional (2D) coordinate data. This conversion employed the BPANN method and conventional techniques such as affine and polynomial. The outcomes derived from the BPANN methodology are juxtaposed against those from the affine and polynomial methodologies. The findings reveal that the effective implementation of each approach within this study is contingent upon the spatial arrangement of the control points leveraged to develop the transformation model. Additionally, the root mean square error (RMSE) of BPANN ranging from 3 to 5 cm closely aligns with the outcomes achieved through affine and polynomial methods.

Global Navigation Satellite System (GNSS) signals arrive at the Earth in a nonlinear and slightly curved way due to the refraction effect caused by the troposphere. The troposphere delay of the GNSS signal consists of hydrostatic and wet parts. In particular, tropospheric wet delay prediction and interpolation are more difficult than those of the dry component due to the rapid temporal and spatial variation of the water vapor content. Wet delay estimation and interpolation with a sufficient accuracy is an important issue for all parameters obtained by GNSS positioning techniques. In particular, in real-time positioning applications, errors caused by interpolation of the wet troposphere delay are reflected in the height component of about 1 to 2 cm. Furthermore, the amount of water vapor in the troposphere is very important information in weather forecast applications obtained as a function of wet delay. Therefore, real-time monitoring of the troposphere can be achieved with a higher resolution and accuracy by utilizing neural network models for interpolation of the wet tropospheric delay. In addition, in the absence of the GNSS station, wet delays can be interpolated by means of the surrounding stations to the desired location. In this study, a back propagation artificial neural network (BPNN) model based on meteorological parameters obtained from The New Austrian Meteorological Measuring Network (TAWES) was used to interpolate wet troposphere delay. Analysis was carried out at 40 reference stations of the Echtzeit Positionierung Austria (EPOSA) GNSS Network covering the whole of Austria. The interpolation of zenith wet delays based on the artificial neural network was performed by using latitude, longitude, altitude and meteorological parameters (temperature, pressure, weighted mean temperature, and water vapor pressure). These parameters were then subtracted from the artificial neural network model one by one and six different artificial neural networks were designed. In addition, the linear interpolation method (LIN) and inverse distance weighted interpolation method (IDW) were used as conventional interpolation methods. In order to investigate the effect of the network density on interpolation methods, three networks, including 40, 30, and 20 reference stations, were formed and the increased distance effect on interpolation methods was evaluated. In addition, analyses were conducted in winter, spring, and summer to evaluate the seasonal effects on interpolation methods. According to the statistical analysis, the root mean square error (RMSE) values of the IDW, LIN, and BPNN methods were found to be 12.6, 13.4, and 5.9 mm, respectively.

There has been a need for geodetic network densification since the early days of traditional surveying. In order to densify geodetic networks in a way that will produce the most effective reference frame improvements, the crustal velocity field must be modelled. Artificial Neural Networks (ANNs) are widely used as function approximators in diverse fields of geoinformatics including velocity field determination. Deciding the number of hidden neurons required for the implementation of an arbitrary function is one of the major problems of ANN that still deserves further exploration. Generally, the number of hidden neurons is decided on the basis of experience. This paper attempts to quantify the significance of pruning away hidden neurons in ANN architecture for velocity field determination. An initial back propagation artificial neural network (BPANN) with 30 hidden neurons is educated by training data and resultant BPANN is applied on test and validation data. The number of hidden neurons is subsequently decreased, in pairs from 30 to 2, to achieve the best predicting model. These pruned BPANNs are retrained and applied on the test and validation data. Some existing methods for selecting the number of hidden neurons are also used. The results are evaluated in terms of the root mean square error (RMSE) over a study area for optimizing the number of hidden neurons in estimating densification point velocity by BPANN.

Preparation and artificial neural networks analysis of ultrafine β-Sialon powders by microwave-assisted carbothermal reduction nitridation of sol–gel derived powder precursors

In this study, a back propagation artificial neural network model utilizing characteristics of urban signalized intersections for occurrence prediction of intersection - related traffic crashes, along with its application for crash reduction, are proposed. The 1,593 traffic crashes reported at 62 signalized intersections in Taichung city, Taiwan during year 2000 - 2001 were analyzed. These intersections were decomposed into 636 approaching direction combinations (ADCs). Characteristics, as well as the number of crashes, corresponding to each ADC were investigated. A back propagation artificial neural network (ANN) model was then generated. It was found that the ANN model exhibited great prediction ability with 0.992 correlation coefficient, and 3.38E- 06 mean square error. With the ANN model, a proposed decision-making scheme for intersection rehabilitation was suggested. Case study indicated that the suggested scheme could serve well as a referable tool.

Temporary storage for collection of municipal solid waste is designed with various parameters, such as size of bin, collection frequency, average filling rate, number of bins, volume of material needed for bin production, bin density, and catchment area. The environmental impact of temporary storage is evaluated with emissions generated due to the production of net material used for bin production. An artificial neural network-back propagation method is proposed for modeling the temporary storage. In this proposed artificial neural network-back propagation network, the number of bins and global warming potential (GWP) based on the quantity of generated wastes are predicted. Thus, a set of different structures of artificial neural network-back propagation are investigated and then the best model is developed for temporary storage of municipal solid waste. The artificial neural network-back propagation model is trained using various parameters, such as number of neurons in the hidden layers, training tolerance, momentum, and learning rate. This model is helpful for the decision makers in choosing the environmentally sound options in the design of temporary storage for municipal solid waste management. Artificial neural network-back propagation can be successfully used to predict number of bins, GWP from waste generation rate of the city.

Traditional methods of multivariate statistical analyses, used to recognize anomalous hydrocarbon signatures in petroleum exploration, have at least four shortcomings: (1) it is difficult to isolate anomalies where the data are not normally distributed; (2) it is difficult to separate distinct anomaly populations corresponding to distinct formation mechanisms while separating anomalies from background; (3) it is not fitting to present illustrations of multivariate anomalies on contour maps; and (4) it is not suitable for preparation for multivariate pattern recognition. These present serious obstacles to the application of exploration geochemistry in hydrocarbon exploration. This study demonstrates that the Back Propagation Artificial Neural Network (BP-ANN) with logic multiplication cluster analysis (a new cluster analysis proposed in this paper) overcomes the difficulties exhibited by traditional multivariate methods. The logic multiplication cluster analysis was designed to produce a training set for the BP-ANN. This approach was established on the basis of geochemical characteristics and origin of the various populations in geochemical data, such as background, micro-seepage anomalies and seepage anomalies. The topology of the BP-ANN was optimized using the outputs of the BP-ANN and the correct rate. In order to illustrate the multivariate anomalies recognized using BP-ANN on contour maps, we designed the expression functions for BP-ANN application in this field. With traditional methods of anomaly recognition, acid-extractable hydrocarbons in soils have not proven to be efficient indicators for hydrocarbon potential in East Anan Sag, Inner Mongolia. However, the BP-ANN has indicated that these indicators are efficient and that areas of East Anan Sag have potential reserves of petroleum.

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

This paper proposes a novel prediction model for photovoltaic (PV) system output current. The proposed model is based on cascade-forward back propagation artificial neural network (CFNN) with two inputs and one output. The inputs are solar radiation and ambient temperature, while the output is output current. Two years of experimental data for a 1.4 kWp PV system are utilized in this research. The monitored performance is recorded every 2 s in order to consider the uncertainty of the system’s output current. A comparison between the proposed model and other empirical and statistical models is done in this paper as well. Moreover, the ability of the proposed model to predict performance with high uncertainty rate is validated. Three statistical values are used to evaluate the accuracy of the proposed model, namely, mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). These values are used to measure the deviation between the actual and the predicted data in order to judge the accuracy of the proposed model. A simple estimation of the deviation between the measured value and the predicted value with respect to the measured value is first given by MAPE. After that, the average deviation of the predicted values from measured data is estimated by MBE in order to indicate the amount of the overestimation/underestimation in the predicted values. Third, the ability of predicting future records is validated by RMSE, which represents the variation of the predicted data around the measured data. Eventually, the percentage of MBE and RMSE is calculated with respect to the average value of the output current so as to present better understating of model’s accuracy. The results show that the MAPE, MBE, and RMSE of the proposed model are 7.08%, −0.21 A (−4.98%), and 0.315 A (7.5%), respectively. In addition to that, the proposed model exceeds the other models in terms of prediction accuracy.

Impact operational parameters such as initial dyes concentration, adsorbent mass, pH and sonication time on the efficiency of ultrasound-assisted adsorption of sunset yellow (SY) and azur (II) (AZ) onto MWCNT and MWCNT-Pd-NPs was studied by central composite design (CCD) and back propagation artificial neural network (ANN) models and resulted for tow adsorbent were compared to each other. Optimize conditions from CCD in presence of MWCNT as adsorbent was found to be 6.01 and 9.98mgL−1 AZ and SY concentration, 0.02 of adsorbent mass, pH 7.0 and 3.8min sonication time, respectively, while in the presence of MWCNT-Pd-NPs optimize value were obtained at 10.0 and 8.0mgL−1 AZ and SY concentration, 0.018 of adsorbent mass and pH 5.0 and 3.5min sonication time, respectively. In these conditions and at desirability value of 0.99, the maximum adsorption efficiency for AZ onto MWCNT and MWCNT-Pd-NPs are 82.97 and 94.95, respectively. Also, the maximum adsorption efficiency for SY onto MWCNT and MWCNT-Pd-NPs are 73.13 and 85.55, respectively. This values show that MWCNT-Pd-NPs in comparison with MWCNT at the lower contact time and adsorbent mass was able to remove greater amounts of under study dyes. Based on the ANN model the absolute average deviations (AADs) of AZ and SY dyes adsorption by MWCNT from experimental data are 0.27% and 0.43%, and the R2 values are 0.911 and 0.954, respectively. Also, the AADs of AZ and SY dyes adsorption by MWCNTs-Pd-NPs are 0.44% and 0.65%, and the R2 values are 0.925% and 0.927%, respectively. This values show that ANN for MWCNT-Pd-NPs in comparison with MWCNT was a more powerful tool for building up to construct an empirical model to predict under study dyes adsorption behavior. In addition, the obtained results have good agreement with experimental data for MWCNT-Pd-NPs in comparison with MWCNT. Finally, the study of equilibrium isotherm and kinetic models of adsorption process are shown that the Langmuir isotherm model and pseudo second order kinetic model were the best models for both adsorbent.

Operational parameter impact and back propagation artificial neural network modeling for phosphate adsorption onto acid-activated neutralized red mud

The potential of using three different data-driven techniques namely, multilayer perceptron with backpropagation artificial neural network (MLP), M5 decision tree model, and Takagi–Sugeno (TS) inference system for mimic stage–discharge relationship at Gharraf River system, southern Iraq has been investigated and discussed in this study. The study used the available stage and discharge data for predicting discharge using different combinations of stage, antecedent stages, and antecedent discharge values. The models’ results were compared using root mean squared error (RMSE) and coefficient of determination (R 2) error statistics. The results of the comparison in testing stage reveal that M5 and Takagi–Sugeno techniques have certain advantages for setting up stage–discharge than multilayer perceptron artificial neural network. Although the performance of TS inference system was very close to that for M5 model in terms of R 2, the M5 method has the lowest RMSE (8.10 m3/s). The study implies that both M5 and TS inference systems are promising tool for identifying stage–discharge relationship in the study area.

Quantitative Structure–Retention Relationship Modeling of Morphine and Its Derivatives on OV-1 Column in Gas–Liquid Chromatography Using Genetic Algorithm

<p>Predicting river water quality in the Special Region of Yogyakarta (DIY) is crucial. In this research, we modeled a river water quality prediction system using the artificial neural network (ANN) backpropagation method. Backpropagation is one of the developments of the multilayer perceptron (MLP) network, which can reduce the level of prediction error by adjusting the weights based on the difference in output and the desired target. Water quality parameters included biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), dissolved oxygen (DO), total phosphate, fecal coliforms, and total coliforms. The research object was the upstream, downstream, and middle parts of the Oya River. The data source was secondary data from the DIY Environment and Forestry Service. Data were in the form of time series data for 2013–2023. Descriptive data results showed that the water quality of the Oya River in 2020–2023 was better than in previous years. However, increasing community and industrial activities can reduce water quality. This was concluded based on the prediction results of the ANN backpropagation method with a hidden layer number of 4. The prediction results for period 3 in 2023 and period 1 in 2024 are that 1) the concentrations of BOD, fecal coli, and total coli will increase and exceed quality standards, 2) COD and TSS concentrations will increase but will still be below quality standards, 3) DO and total phosphate concentrations will remain constant and still on the threshold of quality standards. The possibility of several water quality parameters increasing above the quality standards remains, so the potential for contamination of the Oya River is still high. Therefore, early prevention of river water pollution is necessary.</p>

The additional method of fire detection is not reliable and accurate as be influenced by random environmental disturbance. In this paper, first, a novel identification method for fire based on the process information of feature and the neural network of fire detection is discussed. CO and CO2 gas concentration ratio, the ratio of increase in rates and characteristics of the acceleration are chosen as characteristic parameters of the process of fire and input of three layers back-propagation (BP) artificial neural network, which is extracted by extrapolation method and least square method. Second, the true and false fire source can be identified effectively by the trained network based on improved BP arithmetic-LM arithmetic. The tests prove that fire detection system based on BP artificial neural network can detected the process signal of fire and identify the true fire effectively. The reliability and accuracy of fire detection makes better improvement compared with traditional method.