RBF ANN Research Articles

Predictive modeling and analysis of key drivers of groundwater nitrate pollution based on machine learning

Nitrate comtamination of shallow groundwater in agricultural intensification regions is a prevalent and global environment issue affecting food security, human health, and the water ecology. Developing a prediction model for groundwater nitrate contamination is crucial for protection of groundwater resources. Machine learning modeling offer potentials to predict contamination, but often fails to adequately screen input features in complex non-linear environments before modeling. Additionally, there is a tendency to overlook the interpretable description of the relationship between major water chemistry parameters and nitrate concentration after modeling, which hinders scientific decision-making by water resource managers. In this study, a dataset consisting of hydrochemical test results from 316 groundwater samples collected between 2011 and 2015 in intensive agricultural areas of Northeast China was collected. Prior to modeling, the dataset was grouped based on land use type, vadose zones types, and thickness. Based on the grouping of data sets, self-organizing map (SOM) and Spearman's coefficients were employed to identity the correlations between water chemical parameters and nitrate concentration (NO3–-N), which provided a logical basis for selecting the key input variables. A radial basis function artificial neural network (RBF ANN) prediction model and principal components regression (PCR) models was constructed using the dataset, and particle swarm optimization algorithm was applied to determine optimal parameter combinations of the RBF ANN. After conducting modeling, the advantages and disadvantages of RBF ANN and principal components regression (PCR) models were thoroughly examined and discussed, and the primary factors that impact nitrate concentrations in groundwater were analyzed using a PCR model. The results revealed that the RBF ANN model showed greater accuracy, while the PCR model offered better interpretability. Therefore, the integration of the two models is advantageous for nitrate prediction research. Human activities, rainfall, land-use types, vadose zones types and thickness are external factors that affecting nitrate concentration, and the difference between nitrification. Denitrification, and dissimilatory nitrate reduction to ammonium (DNRA) is internal driving factors for the change of nitrate concentration in the aquifer, and partially determine differences in the spatial distribution of aquifer nitrate concentrations under various land use types. This study provides a comprehensive approach for predicting nitrate pollution in groundwater under non-linear environmental conditions, supporting decision making in sustainable groundwater development, management, and conservation.

Research of cooling tower filler based on radial basis function artificial neural network (RBF ANN)

AbstractThe filler is the core component of the cooling tower, filler performance refers to both its thermal and flow resistance characteristics, which use empirical formulas of tower characteristic N, volumetric mass transfer coefficient βxv, and pressure drop ΔP obtained through experimentation under specific conditions. However, the performance equations for identical countercurrent fillers can vary at different heights or seawater concentrations. Linear interpolation is the conventional method for obtaining the filler performance under different conditions, but its uncertainty limits the application. This paper explores the use of the radial basis function artificial neural network (RBF ANN) to analyze filler performance based on existing performance equations. The data set is generated by the filler performance equations. The results demonstrate that RBF ANN has a preferable prediction effect with high correlation (the determination coefficient R2 > 0.99) and prediction accuracy (the proportion of relative error within 10% N10 > 90%). Furthermore, the predicted results are consistent with the experimental results of the filler performance. Therefore, RBF ANN can accurately predict filler performance at varying heights and seawater concentrations, making it universal and providing a basis for cooling tower design.

Design of Customized Garments Towards Sustainable Fashion Using 3D Digital Simulation and Machine Learning-Supported Human–Product Interactions

This paper put forward a new interactive design approach for customized garments towards sustainable fashion using machine learning techniques, including radial basis function artificial neural network (RBF ANN), genetic algorithms (GA), probabilistic neural network (PNN), and support vector regression (SVR). First, RBF ANNs were employed to estimate the detailed human body dimensions to fulfill consumers’ ergonomics requirements. Next, the GA-based models were developed to generate the formalized design solutions following the consumer profiles (demands). Afterwards, the evaluation model was established to quantitatively characterize the relations between consumer profiles and garment profiles from the generated design solutions. The design solutions would be digitally demonstrated and recommended to the consumer following the evaluation results in descending order. Meanwhile, the PNN-based models were created to predict garment fitness based on virtual try-on. Moreover, the SVR-based self-adjustment mechanism was built to estimate and control garment design parameters according to the consumer’s feedback. Based on these mathematical models, the approach enhances the interactions among digital garment demonstration, the designer’s professional knowledge and the user’s perception to find out the most relevant design solution. The effectiveness of the new approach was verified by a real application case of leisure pants customization. The results show that the proposed method can powerfully support the designers’ quality personalized design solutions for consumers more accurately, fast, intelligently, and sustainably, compared with the existing approaches. More importantly, it also establishes an effective and reliable communication channel and mechanism among consumers, fashion designers, pattern designer, and garment producer.

Neural networks for predicting air gap membrane distillation performance

The hollow fiber air gap membrane distillation (AGMD) has recently attracted tremendous attention for desalination and wastewater treatment due to its high packing density, low conductive heat loss, and latent heat recovery capability. Utilizing fast and accurate modeling tools to predict MD performance can result in the further development of desalination technologies. However, simple and time-saving prediction models to assess the AGMD performance were not abundant. Herein, AGMD performance, including permeate flux (J) and gained output ratio (GOR) was predicted through multiple linear regression (MLR) model, back propagation neural network (BP ANN) and radial basis function neural network (RBF ANN) under different hot temperatures (Th), coolant temperatures (Tc), feed flow rates (F), and feed concentration (c). A total of 30 sets of data were used to train the proposed models, the other 10 external validation datasets not used for training the models were applied to validate the prediction accuracy. The results depicted that RBF ANN (SPREAD = 30, N = 30) showed greatest prediction performance (R2 = 0.99–1) compared with BP ANN and MLR models (R2 = 0.98–0.99; R2 = 0.89–0.97). The computing time consumption of RBF ANN was higher than BP ANN. According to the Mean impact value (MIV) analysis, Th had the strongest effect on J and GOR. Increasing Th and decreasing c both had positive impacts on J and GOR, but increasing Tc or F resulted in a trade-off influence. A genetic algorithm (GA) was employed to optimize J and GOR simultaneously, the optimum J and GOR could reach 6.00 kg/m2·h and 7.70 respectively. In this study, the three prediction models proved their abilities to predict AGMD performance and further provide guidance in the actual membrane distillation water treatment process.

Radial base neural network for the detection of banana maturation stages: perceptron multilayer network comparison

Agriculture is one of the pillars of human existence since it allows for the obtention of food as well as other products for food production processes. In this regard, there are some crops, such as climactic fruits, that face difficulties especially regarding classification of their maturation stages at the time of harvest, which is the case of bananas, the focus of this work. Therefore, there are some techniques that use artificial neural networks to classify them, such as multilayer networks. Examples of such networks are Perceptron, widely used in several areas, and Radial Base Functional networks (RBF), whose studies are incipient and have little use in agricultural areas. Hence, the objective of the present work was to carry out a comparison between these two neural networks to verify which provides the highest accuracy. In this work it was possible to verify that radial base functional neural networks provide a faster and more efficient categorization for the stages of bananas maturation, because they do not require training and, therefore, have low computational cost, saving more energy, when compared to a Multilayer Perceptron. Therefore, it can be inferred that Radial Base Functional Artificial Neural Networks (RBF ANN) can be widely used in agriculture, enabling the improvement of different cultures and different processes, such as harvesting.

Efficient photodegradation of disulfine blue dye and Tetracycline over Robust and Green g-CN/Ag3VO4/PAN nanofibers: Experimental design, RSM, RBF-NN and ANFIS modeling

The g-CN/Ag3VO4/PAN nanofibers (NFs) is prominent from various nanophotocatalysts (NPCs) due to their higher surface to volume ratio, mechanical strength and recyclable characteristics. In this study, g-CN/Ag3VO4/PAN NFs were obtained through in situ method by immobilizing Ag3VO4 on g-CN/PAN NFs. Compared to g-CN/PAN and Ag3VO4/PAN, the g-CN/Ag3VO4/PAN NFs revealed excellent photocatalytic performance toward disulfine blue dye (DB). Also, the g-CN/Ag3VO4/PAN NFs showed highly activity over some interference of inorganic cations/anions and excellent cycling stability. Furthermore, the batch experiments designed by central composite design (CCD) were employed for remove of tetracycline (TC). Afterward, response surface methodology (RSM), Radial basis function artificial neural network (RBF ANN) and an adaptive neuro-fuzzy inference system (ANFIS) have been used for modeling of photocatalyst dose, concentration of TC and irradiation time. Due to strong influence of pH on the photocatalytic degradation, the pH was optimized as one at a time variable. For all three models, the values of the statistical parameters were calculated. The obtained results reveal that the ANFIS models is more accurate to predict the degradation of TC. Furthermore, the effective factors were optimized by employing the Desirability function (DF) and genetic algorithm (GA) approach and then used for real water samples. At this optimum condition, the removal percentage was obtained ∼97% by DF and GA approach, respectively. At the end, the LC-Mass method was employed to detect the intermediates of photodegradation of TC molecules.

Small‐signal modeling of microwave transistors using radial basis function artificial neural network‐comparison of different methods for spread constant determined

This article presents a high-precision modeling method to build a small signal model of GaAs pseudomorphic high electron mobility transistor (pHEMT) by using a radial basis function artificial neural network (RBF ANN). Both the RBF ANN Method 1 that uniformly distributed spread constant (SC) in the given range and Method 2 that increasingly changed SC with a chosen step have been modeled in this work. Compared with the l RBF ANN Method 1, the RBF ANN method 2 can automatically obtain the optimal ANN corresponding to the SC. The RBF ANN method 2 is developed for S-parameters model and equivalent circuit parameters (ECPs) model of HEMTs. S-parameters model establishes S-parameters versus bias, temperature and frequency, while the ECPs model establishes ECPs versus bias and temperature. To validate the capability of the RBF ANN in small signal modeling of GaAs pHEMTs, measured and modeled data of S-parameters model and ECPs model of a 4 × 75 μm gate width, 0.15 μm gate length GaAs pHEMT are compared, and very good agreement is achieved up to 50 GHz. For the S-parameters model, the average error of the method 2 is improved by about 20% at the temperature of −20°C, 25°C, and 85°C. For the ECPs model, the average error of the method 2 is 80% higher than that of the method 1.

Physical Exercise Effects on University Students’ Attention: An EEG Analysis Approach

Physically active breaks (AB) are currently being proposed as an interesting tool to improve students’ attention. Reviews and meta-analyses confirm their effect on attention, but also warned about the sparse evidence based on vigilance and university students. Therefore, this pilot study aimed to (a) determine the effects of AB in comparison with passive breaks on university students’ vigilance and (b) to validate an analysis model based on machine learning algorithms in conjunction with a multiparametric model based on electroencephalography (EEG) signal features. Through a counterbalanced within-subject experimental study, six university students (two female; mean age = 25.67, STD = 3.61) had their vigilance performances (i.e., response time in Psycho-Motor Vigilance Task) and EEG measured, before and after a lecture with an AB and another lecture with a passive break. A multiparametric model based on the spectral power, signal entropy and response time has been developed. Furthermore, this model, together with different machine learning algorithms, shows that for the taken signals there are significant differences after the AB lesson, implying an improvement in attention. These differences are most noticeable with the SVM with RBF kernel and ANNs with F1-score of 85% and 88%, respectively. In conclusion, results showed that students performed better on vigilance after the lecture with AB. Although limited, the evidence found could help researchers to be more accurate in their EEG analyses and lecturers and teachers to improve their students’ attentions in a proper way.

Using simple and easy water quality parameters to predict trihalomethane occurrence in tap water

Monitoring of disinfection by-products (DBPs) in water supply system is important to ensure safety of drinking water. Yet it is a laborious job. Developing predictive DBPs models using simple and easy parameters is a promising way. Yet current models could not be well applied into practice because of the improper dataset (e.g. not from real tap water) they used or involving the parameters that are difficult to measure or require expensive instruments. In this study, four simple and easy water quality parameters (temperature, pH, UVA254 and Cl2) were used to predict trihalomethane (THMs) occurrence in tap water. Linear/log linear regression models (LRM) and radial basis function artificial neural network (RBF ANN) were adopted to develop the THMs models. 64 observations from tap water samples were used to develop and test models. Results showed that only one or two parameters entered LRMs, and their prediction ability was very limited (testing datasets: N25 = 46–69%, rp = 0.334–0.459). Different from LRM, the prediction accuracy of RBF ANNs developed with pH, temperature, UVA254 and Cl2 can be improved continuously by tweaking the maximum number of neuron (MN) and Gaussian function spread (S) until it reached best. The optimum RBF ANNs of T-THMs, TCM and BDCM were obtained when setting MN = 20, S = 100, 100.1 and 60, respectively, where the N25 and rp values for testing datasets reached 85–92% and 0.813–0.886, respectively. Accurate predictions of THMs by RBF ANNs with these four simple and easy parameters paved an economic and convenient way for THMs monitoring in real water supply system.

COMPUTER-AIDED MODEL FOR THE CLASSIFICATION OF ACUTE INFLAMMATIONS VIA RADIAL-BASED FUNCTION ARTIFICIAL NEURAL NETWORK

Abstract
 Objective: This study aimed to compare the classification performance of acute inflammation by applying the RBF ANN model on an open-access acute inflammation data set and determining the risk factors that may be associated with acute inflammation markers.
 Material and Methods: In the study, Nephritis of renal pelvis origin was classified using the open access “Acute Inflammation” data set RBF ANN model, and risk factors that could be associated were revealed. The success of RBF ANN is presented by different performance metrics.
 Results: The success of classifying Nephritis of renal pelvis origin with the RBF ANN model has been demonstrated to be excellent (AUC = 1, Accuracy = 100%). In addition, the RBF ANN model revealed that the most important variable among the risk factors that may be associated with Nephritis of renal pelvis origin is “temperature of patient”.
 Conclusion: As a result, the obtained findings show that the RBF ANN model provides very successful predictions in the classification of Nephritis of renal pelvis origin. Also, it has been shown that the importance values of factors associated with Nephritis of renal pelvis origin are estimated with the RBF classification model and can be used safely in preventive medicine applications.

Application of Artificial Intelligence to Predict Enhanced Oil Recovery Using Silica Nanofluids

The feasibility of approaches to enhanced oil recovery (EOR) in harsh conditions of reservoirs should be evaluated primarily in the laboratory environment to capture possible failures that threaten the performance of an operation, although such experiments are commonly expensive and time-consuming. This work investigated the application of artificial intelligence in allaying such concerns regarding the initial screening of EOR methods. Accordingly, three machine learning algorithms, namely adaptive neuro-fuzzy inference system (ANFIS), multilayer perceptron-artificial neural network (MLP–ANN), and radial basis function-artificial neural network (RBF–ANN), were employed to predict the efficiency of a set of silica nanofluid flooding experiments in carbonate and sandstone core samples. Initially, the optimum structures of the employed models were determined. Then, their performances were compared. The strongest performance was achieved by the ANFIS model, where the results in terms of coefficient of determination and root-mean-square error for training, testing, and entire data points were 0.9954 and 0.3395, 0.9877 and 0.4793, and 0.9939 and 0.3793, respectively. The ANFIS model also has the shortest execution time and the least over-fitting problems, and thus it can be utilized for screening the efficiency of silica-EOR projects.

Location of the Leakage From a Simulated Water-Cooling Wall Tube Based on Acoustic Method and an Artificial Neural Network

Water-cooling wall tube leakage accident is an important factor affecting the safe and economic operation of power station boilers. In this article, the acoustic method is applied to locate the leakage from a simulated water-cooling wall tube, and several key technologies are studied in order to identify the precise location of leakage source. Sound wave propagation path in ‘nonuniform temperature field is obtained to overcome the shortcoming that the traditional location technology always assumed that sound waves propagate along the straight line. Considering the refraction effect of sound waves, the reconstruction method based on radial basis function approximation with polynomial reproduction (RBF-PR) is used to reconstruct the temperature field. A generalized cross correlation with second correlation is applied to estimate the time delay of arrival (TDOA). According to the temperature field measured by acoustic method and the propagate time of sound waves, the location fingerprint method including the statistical correlation characteristics between the coordinate of fingerprint points and TDOA are constructed. Radial basis function artificial neural network (RBF ANN) is used to estimate the coordinate of leakage point. Numerical simulations and experimental study are implemented to evaluate the effectiveness of the proposed location system. An acoustic location test platform is constructed to study the location accuracy in different working conditions. The results indicate that considering the refraction effect, the reconstruction performance of temperature field is superior than that without the refraction effect, and acoustic method with RBF ANN algorithm can gain the leakage location with high accuracy and good antinoise ability in the above high-quality reconstructed nonuniform temperature distribution.

Radial basis function artificial neural network (RBF ANN) as well as the hybrid method of RBF ANN and grey relational analysis able to well predict trihalomethanes levels in tap water

Many models have been developed in previous studies for predicting the formation of disinfection by-products (DBPs) in drinking water. However, most of them were linear or log-linear regression models, and generated based on simulated disinfection of source water or treated water in a laboratory other than real tap water, which shows low application potential in practice. In this study, a radial basis function artificial neural network (RBF ANN) as well as the hybrid method of RBF ANN and grey relational analysis (GRA) was proposed to predict trihalomethanes (THMs) levels in real distribution systems. A total of 64 sets of data including THMs levels (trichloromethane (TCM), bromodichloromethane (BDCM) and total-THMs (T-THMs)) and 8 water quality parameters (temperature, pH, UV absorbance at 254 (UVA254), dissolved organic carbon, bromide, residual free chlorine, nitrite and ammonia) were used to train and verify the proposed model. As compared to linear and log-linear regression models (rp = 0.254–0.659; N25 = 46–78%), RBF ANNs for THMs (TCM, BDCM and T-THMs) prediction consistently show higher regression coefficients (rp = 0.760–0.925) and prediction accuracy (N25 = 92–98%), which indicates the high capability of RBF ANN to learn the complex non-linear relationships involved THMs formation. Further analysis shows that RBF ANNs using fewer water quality parameters based on GRA still make excellent performance in THMs prediction (rp = 0.760–0.946; N25 = 92–98%). This result demonstrates that GRA can be an effective technique to facilitate the generation of sound RBF ANN models with fewer factors.

Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms

This work investigated the capability of multilayer perceptron artificial neural network (MLP–ANN), stochastic gradient boosting (SGB) tree, radial basis function artificial neural network (RBF–ANN), and adaptive neuro-fuzzy inference system (ANFIS) models to determine the heat capacity (Cp) of ionanofluids in terms of the nanoparticle concentration (x) and the critical temperature (Tc), operational temperature (T), acentric factor (ω), and molecular weight (Mw) of pure ionic liquids (ILs). To this end, a comprehensive database of literature reviews was searched. The results of the SGB model were more satisfactory than the other models. Furthermore, an analysis was done to determine the outlying bad data points. It showed that most of the experimental data points were located in a reliable zone for the development of the model. The mean squared error and R2 were 0.00249 and 0.987, 0.0132 and 0.9434, 0.0320 and 0.8754, and 0.0201 and 0.9204 for the SGB, MLP–ANN, ANFIS, and RBF–ANN, respectively. According to this study, the ability of SGB for estimating the Cp of ionanofluids was shown to be greater than other models. By eliminating the need for conducting costly and time-consuming experiments, the SGB strategy showed its superiority compared with experimental measurements. Furthermore, the SGB displayed great generalizability because of the stochastic element. Therefore, it can be highly applicable to unseen conditions. Furthermore, it can help chemical engineers and chemists by providing a model with low parameters that yields satisfactory results for estimating the Cp of ionanofluids. Additionally, the sensitivity analysis showed that Cp is directly related to T, Mw, and Tc, and has an inverse relation with ω and x. Mw and Tc had the highest impact and ω had the lowest impact on Cp.

Modification of MTEA-Based Temperature Drift Error Compensation Model for MEMS-Gyros.

The conventional temperature drift error (TDE) compensation model cannot decouple temperature dependence of Si-based materials because temperature correlated quantities (TCQ) have not been obtained comprehensively, and Micro-Electro-Mechanical System gyros’ (MEMS-gyros’) environmental adaptability is reduced in diverse, complicated conditions. The study presents modification of TDE compensation model of MEMS-gyros based on microstructure thermal effect analysis (MTEA). First, Si-based materials’ temperature dependence was studied in microstructure with thermal expansion effect and TCQ that determines the structural deformation were extracted to modify the conventional model, including temperature variation and its square. Second, a precise TDE test method was formed by analyzing heat conduction process between MEMS-gyros and thermal chamber, and temperature experiments were designed and conducted. Third, the modified model’s parameters were identified based on radical basis function artificial neural network (RBF ANN) and its performance was evaluated. Last, the conventional and modified models were compared in performance. The experimental results show MEMS-gyros’ bias stability was up to 10% of the conventional model, the temperature dependence of Si-based materials was decoupled better by the modified one and the environmental adaptability of MEMS-gyros was improved to expand their application in diverse complicated conditions.

A radial basis function artificial neural network (RBF ANN) based method for uncertain distributed force reconstruction considering signal noises and material dispersion

A radial basis function artificial neural network (RBF ANN) based method for distributed dynamic force reconstruction is presented in this study considering multi-source uncertainties. Foremost, the distributed forces are approximated by truncated Legendre orthogonal polynomial in the time history. The RBF ANN implementation is introduced, whereby the normalized structural responses are deemed as input variables, and the normalized polynomial coefficients are regarded as output variables. To improve the generalization of the trained RBF ANN, the structural responses and polynomial coefficients with respect to different types of distributed dynamic forces at every instant are defined as the samples. Thus, the coefficients of the distributed force to be identified may be ascertained after inputting the corresponding responses. In terms of the multi-source uncertainties, the random white Gaussian noise is brought in to simulate the noise of measured signals polluted, and the interval vector is involved to characterize the uncertainties of material dispersion subsequently. In view of the issue of large interval parameters, the subinterval theory is established by a combination of the Taylor series expansion in each subinterval. Eventually, the validity and feasibility of the developed methodology are demonstrated by two numerical examples.

Prediction method of thermal conductivity of nanofluids based on radial basis function

Accurately predicting the thermal conductivity of nanofluids under various thermodynamic conditions is of great importance to promote the industrial application of nanofluids. Unfortunately, the accuracy and applicability of the current theoretical or empirical models cannot meet the demand, due to the inherent complex of nanofluids. In this study, an intelligent model, named radial basis function artificial neural network (RBF–ANNs), is developed to predict the thermal conductivity of nanofluids under various conditions. Five parameters including nanoparticle volume concentration, temperature, nanoparticle diameter, thermal conductivity of nanoparticle and thermal conductivity of base fluid are selected as the input variables. A total of 1444 experimental data samples are collected to optimize the structure of model. The RBF model is compared with six theoretical models and three intelligent models through statistical and graphical analyses. Also, trend analysis and sensitivity analysis are conducted to evaluate the influencing mechanism of nanoparticle concentration, temperature, nanoparticle size, thermal conductivities of base fluids and nanoparticle on the thermal conductivity of nanofluids. Meanwhile, the quality of the experimental data is evaluated by means of leverage algorithm. Results indicate the superiority of the RBF, especially when the data size is large. The overall correlation coefficient (R2), average absolute relative deviation (AARD%) and root-mean-squared error of the developed model are 0.9931, 2.715 and 0.0316, respectively. Among the five input parameters, the volume fraction of nanoparticles has the greatest impact on the thermal conductivity of nanofluid. The results of outlier detection demonstrate that the proposed RBF model and data samples are statistically valid.

Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods

The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.

Forest Above Ground Biomass Estimation from Remotely Sensed Imagery in the Mount Tai Area Using the RBF ANN Algorithm

AbstractForest biomass is a significant indicator for substance accumulation and forest succession, and can provide valuable information for forest management and scientific planning. Accurate estimations of forest biomass at a fine resolution are important for a better understanding of the forest productivity and carbon cycling dynamics. In this study, considering the low efficiency and accuracy of the existing biomass estimation models for remote sensing data, Landsat 8 OLI imagery and field data cooperated with the radial basis function artificial neural network (RBF ANN) approach is used to estimate the forest Above Ground Biomass (AGB) in the Mount Tai area, Shandong Province of East China. The experimental results show that the RBF model produces a relatively accurate biomass estimation compared with multivariate linear regression (MLR), k-Nearest Neighbor (KNN), and backpropagation artificial neural network (BP ANN) models.

Pengembangan Model Jaringan Syaraf Tiruan untuk Memprediksi Jumlah Mahasiswa Baru di PTS Surabaya (Studi Kasus Universitas Wijaya Putra)

Jaringan Syaraf Tiruan (JST) dan data time series dapat digunakan untuk metode peramalan dengan baik. Jaringan Syaraf Tiruan adalah suatu metode yang prinsip kerjanya diadaptasi dari model matematika pada manusia atau syaraf biologi. Jaringan syaraf dikarakteristikkan oleh; (1) pola koneksi diantara neuron (disebut arsitektur), (2) menentukan bobot dari koneksi (disebut training atau learning), dan (3) fungsi aktifasi. Tujuan penelitian adalah mendapatkan arsitektur jaringan syaraf tiruan yang terbaik, membandingkan dua metode Jaringan Syaraf Tiruan Backpropogation dengan metode Jaringan Syaraf Tiruan Fungsi Basis Radial (RBF). Penelitian ini merupakan penelitian dengan menggunakan data yang sebenarnya (true experimental). Penelitian ini dilaksanakan di Universitas Wijaya Putra Surabaya, dengan memakai data kedua yang diperoleh dari tahun 2012 sampai dengan 2016. Hasil penelitian menunjukkan adanya perbedaan antara metode JST RBF dengan metode JST Backpropagation, diperoleh indeks statistik JST RBF, MAE= 0,0074, RMSE=0, 0096, error=12,6532 %. Indeks statistik JST Backpropagation, MAE= 0,2129, RMSE=0, 2752, error=13,3217 %.