Root Mean Square Error Index Research Articles

Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)

This paper sets pioneering research which investigates the parametric identification of thermoelectric modules (TEMs) through the employment of enhanced slime mould algorithm (ESMA). The proposed method incorporates a pair of modifications to the standard slime mould algorithm (SMA). Primary modification encloses computation of random average position between the slimes' current individual position and best individual position towards resolution of local optima issue. Subsequent modification then involves substitution of an exponential function to the existing tangent hyperbolic function within formula p of the standard SMA in enabling improved probability variants via the selection of updated equations. Competency of the proposed algorithm in generating the optimal parameters for TEMs was appraised based on 21 benchmarked design parameters, following the objective of root mean square error (RMSE) minimization between the temperature of both actual and estimated models. Acquired results which demonstrate lower values of RMSE and parameter deviation index against the standard SMA and other preceding algorithms such as particle swarm optimization, sine cosine algorithm, moth flame optimizer and ant lion optimizer ultimately verified ESMA’s efficacy as an effective approach for accurate model identification.

Turbofan Engine Health Assessment Based on Spatial-Temporal Similarity Calculation.

Aiming at the problem of the remaining useful life prediction accuracy being too low due to the complex operating conditions of the aviation turbofan engine data set and the original noise of the sensor, a residual useful life prediction method based on spatial-temporal similarity calculation is proposed. The first stage is adaptive sequence matching, which uses the constructed spatial-temporal trajectory sequence to match the sequence to find the optimal matching sample and calculate the similarity between the two spatial-temporal trajectory sequences. In the second stage, the weights of each part are assigned by the two weight allocation algorithms of the weight training module, and then the final similarity is calculated by the similarity calculation formula of the life prediction module, and the final predicted remaining useful life is determined according to the size of the similarity and the corresponding remaining life. Compared with a single model, the proposed method emphasizes the consistency of the test set and the training set, increases the similarity between samples by sequence matching with other spatial-temporal trajectories, and further calculates the final similarity and predicts the remaining use through the weight allocation module and the life prediction module. The experimental results show that compared with other methods, the root mean square error (RMSE) index and the remaining useful life health score (Score) index are reduced by 12.6% and 14.8%, respectively, on the FD004 dataset, and the RMSE index is similar to that in other datasets; the Score index is reduced by about 10%, which improves the prediction accuracy of the remaining useful life and can provide favorable support for the operation and maintenance decision of turbofan engines.

Comparative Analysis of Data-Driven Techniques to Predict Heating and Cooling Energy Requirements of Poultry Buildings

Many models have been developed to predict the energy consumption of various building types, including residential, office, institutional, educational, and commercial buildings. However, to date, no models have been designed specifically to predict poultry buildings’ energy consumption. To address this information gap, this study integrated data-driven techniques, including artificial neural networks (ANN), support vector regressions (SVR), and random forest (RF), into a physical model to predict the energy consumption of poultry buildings in different climatic zones in Turkey. The following statistical indices were employed to evaluate the model’s effectiveness: Root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The calculated and predicted values of the heating and cooling loads were also compared using visualization techniques. The results indicated that the RF model was the most accurate during the testing period according to the RMSE (0.695 and 6.514 kWh), MAPE (3.328 and 2.624%), and R2 (0.990 and 0.996) indices for heating and cooling loads, respectively. Overall, this model offers a simple decision-support tool to estimate the energy requirements of different buildings and weather conditions.

IT-PMF: A Novel Community E-Commerce Recommendation Method Based on Implicit Trust

It is well-known that data sparsity and cold start are two of the open problems in recommendation system research. Numerous studies have been dedicated to dealing with those two problems. Among these, a method of introducing user context information could effectively solve the problem of data sparsity and improve the accuracy of recommendation algorithms. This study proposed a novel approach called IT-PMF (Implicit Trust-Probabilistic Matrix Factorization) based on implicit trust, which consists of local implicit trust relationships and in-group membership. The study started from generating the user commodity rating matrix based on the cumulative purchases for items according to their historical purchase records to find the similarity of purchase behaviors and the number of successful interactions between users, which represent the local implicit trust relationship between users. The user group attribute value was calculated through a fuzzy c-means clustering algorithm to obtain the user’s in-group membership. The local implicit trust relationship and the user’s in-group membership were adjusted by the adaptive weight to determine the degree of each part’s influence. Then, the author integrated the user’s score of items and the user’s implicit trust relationship into the probabilistic matrix factorization algorithm to form a trusted recommendation model based on implicit trust relationships and in-group membership. The extensive experiments were conducted using a real dataset collected from a community E-commerce platform, and the IT-PMF method had a better performance in both MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) indices compared with well-known existing algorithms, such as PMF (Probabilistic Matrix Factorization) and SVD (Single Value Decomposition). The results of the experiments indicated that the introduction of implicit trust into PMF could improve the quality of recommendations.

Hyperspectral reconstruction method for optically complex inland waters based on bio-optical model and sparse representing

For better use of well-performed water quality parameter estimation models and the comprehensive use of multi-source remote sensing data, hyperspectral reconstruction is urgently needed in the remote sensing of optically complex inland waters. In this study, we proposed a bio-optical–based hyperspectral reconstruction (BBHR) algorithm to generate hyperspectral above-surface remote-sensing reflectance (Rrs) data ranging in wavelength from 400 to 800 nm. One core advantage of the BBHR method is its in situ data independency, which theoretically renders the algorithm universal. The other advantage is its ability to reconstruct hyperspectral Rrs for the 400–800 nm spectral range, which facilitates the construction of more high accuracy chlorophyll-a concentration (Cchla) estimation models for optically complex waters. The reconstruction was tested by employing six widely used multispectral sensors: the Medium Resolution Imaging Spectrometer, (MERIS), Sentinel-3 Ocean and Land Color Instrument (S3 OLCI), Sentinel-2 Multispectral Instrument (S2 MSI), Geostationary Ocean Color Imager (GOCI), Visible Infrared Imaging Radiometer Suite (VIIRS), and Moderate Resolution Imaging Spectroradiometer (MODIS). The model performance was validated by using a ASD FieldSpec spectroradiometer-measured hyperspectral dataset containing 1396 samples and a satellite-in-situ match-up dataset with 66 samples. The results show that the proposed BBHR method exhibits satisfactory performance. The average mean absolute percentage error (MAPE), root mean square error (RMSE), R2 and bias indices of the BBHR-reconstructed Rrs over all spectral bands of the six multispectral sensors were 3.27%, 8.86 × 10−4 sr−1, 0.98, and − 6.53 × 10−5 sr−1, respectively. In the field Cchla estimation experiment that contained 391 samples (mean Cchla is 25.42 ± 16.37 μg/L), the BBHR algorithm improved the MAPE and RMSE indices of multispectral data from 0.47 and 12.80 μg/L to 0.42 and 10.16 μg/L, respectively. For the satellite image match-up dataset (66 samples), the BBHR method decreased the MAPE and RMSE indices of multispectral images from 0.51 and 12.94 μg/L to 0.32 and 8.01 μg/L, respectively. The proposed algorithm outperformed the other two high-accuracy models in terms of spectral fidelity and Cchla estimation. In addition, the BBHR method shows great potential for the multi-source monitoring of inland water bodies. This could improve the accuracy and robustness of the reconstruction when semi-synchronized multi-source data are input and increase the consistency of multi-source data when non-synchronized multi-source data are provided. Our results revealed that BBHR is a trustworthy algorithm that offers hyperspectral Rrs data and facilitates the remote monitoring of turbid inland waterbodies.

Machine learning-based model development for battery state of charge–open circuit voltage relationship using regression techniques

The development of the Battery Management System mainly depends on an accurate equivalent circuit of the battery model which can be used for the estimation of State of Charge (SoC) and State of Health (SoH). The accuracy of the predicted battery model mainly depends on the exactness of the SoC–Open Circuit Voltage (OCV) relationship which is obtained through the Static Test. Numerous research works reported on various higher-order SoC–OCV battery mathematical models which are unsuitable for real-time applications. Also, there is no unique model for the estimation of battery parameters. This work proposes a Machine Learning (ML) based battery static SoC–OCV prediction method using the concept of supervised learning. The performance of the proposed ML based techniques is compared with the conventional curve fitting techniques. The real-time data of Lithium Ion battery at different temperature profiles are used to analyze the performance of the proposed techniques. The Root Mean Square Error (RMSE) and R-Square (R2) indices are considered in the study to validate the performance of the proposed algorithm. From the study, it is revealed that the Neural network based prediction method is found to be the best method with high R2 and low RMSE indices. The proposed method has the advantages such as simplicity and high accuracy. This predicted battery model can be used in electric vehicle applications.

Modelling above-ground biomass ofPinus radiatatrees with explicit multivariate uncertainty

AbstractThe biomass content and carbon captured by forest plantations is of interest, for example in the context of climate change and carbon budgets.The main objective of our study was to develop functions to estimate the total biomass and its components (stem, branches, bark and leaves) of Pinus radiata D. Don trees in Chile. The methodology proposed for the model fitting uses the maximum likelihood method in a multivariate equation system fitting simultaneously. The fit strategy incorporates additivity restrictions in the estimation functions and in the variance functions to incorporate the heteroskedasticity of biomass, and three structures of the variance–covariance matrix were evaluated to assess the dependence of the different components of tree biomass. Non-linear biomass functions that used the variable $D^2H$ performed best according to several indicators of goodness-of-fit (log-likelihood, Akaike Information Criterion and Bayesian Information Criterion) and estimation precision (root mean square error (RMSE), Bias and EI). The simple structure of both biomass and variance estimation functions was $\beta _1 (D^2H)^{\beta _2}$, and in the modelling system for total tree biomass RMSE between 54.1-54.4 kg (28-36%) were obtained. The three variance–covariance matrix structures evaluated did not generate clear differences in relation to the RMSE, bias and Error Index indicators. The structure of the variance–covariance matrix that incorporated explicitly in the system equations allowed modelling of the relationship between biomass components.

Data-driven risk stratification for preterm birth in Brazil: a population-based study to develop of a machine learning risk assessment approach

BackgroundPreterm birth (PTB) is a growing health issue worldwide, currently considered the leading cause of newborn deaths. To address this challenge, the present work aims to develop an algorithm capable of accurately predicting the week of delivery supporting the identification of a PTB in Brazil. MethodsThis a population-based study analyzing data from 3,876,666 mothers with live births distributed across the 3,929 Brazilian municipalities. Using indicators comprising delivery characteristics, primary care work processes, and physical infrastructure, and sociodemographic data we applied a machine learning-based approach to estimate the week of delivery at the point of care level. We tested six algorithms: eXtreme Gradient Boosting, Elastic Net, Quantile Ordinal Regression - LASSO, Linear Regression, Ridge Regression and Decision Tree. We used the root-mean-square error (RMSE) as a precision. FindingsAll models obtained RMSE indexes close to each other. The lower levels of RMSE were obtained using the eXtreme Gradient Boosting approach which was able to estimate the week of delivery within a 2.09 window 95%IC (2.090–2.097). The five most important variables to predict the week of delivery were: number of previous deliveries through Cesarean-Section, number of prenatal consultations, age of the mother, existence of ultrasound exam available in the care network, and proportion of primary care teams in the municipality registering the oral care consultation. InterpretationUsing simple data describing the prenatal care offered, as well as minimal characteristics of the pregnant, our approach was capable of achieving a relevant predictive performance regarding the week of delivery. FundingBill and Melinda Gates Foundation, and National Council for Scientific and Technological Development – Brazil, (Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPQ acronym in portuguese) Support of the research project named: Data-Driven Risk Stratification for Preterm Birth in Brazil: Development of a Machine Learning-Based Innovation for Health Care- Grant: OPP1202186

Real-time CT image generation based on voxel-by-voxel modeling of internal deformation by utilizing the displacement of fiducial markers.

To show the feasibility of real-time CT image generation technique utilizing internal fiducial markers that facilitate the evaluation of internal deformation. In the proposed method, a linear regression model that can derive internal deformation from the displacement of fiducial markers is built for each voxel in the training process before the treatment session. Marker displacement and internal deformation are derived from the four-dimensional computed tomography (4DCT) dataset. In the treatment session, the three-dimensional deformation vector field is derived according to the marker displacement, which is monitored by the real-time imaging system. The whole CT image can be synthesized by deforming the reference CT image with a deformation vector field in real-time. To show the feasibility of the technique, image synthesis accuracy and tumor localization accuracy were evaluated using the dataset generated by extended NURBS-Based Cardiac-Torso (XCAT) phantom and clinical 4DCT datasets from six patients, containing 10 CT datasets each. In the validation with XCAT phantom, motion range of the tumor in training data and validation data were about 10 and 15mm, respectively, so as to simulate motion variation between 4DCT acquisition and treatment session. In the validation with patient 4DCT dataset, eight CT datasets from the 4DCT dataset were used in the training process. Two excluded inhale CT datasets can be regarded as the datasets with large deformations more than training dataset. CT images were generated for each respiratory phase using the corresponding marker displacement. Root mean squared error (RMSE), normalized RMSE (NRMSE), and structural similarity index measure (SSIM) between the original CT images and the synthesized CT images were evaluated as the quantitative indices of the accuracy of image synthesis. The accuracy of tumor localization was also evaluated. In the validation with XCAT phantom, the mean NRMSE, SSIM, and three-dimensional tumor localization error were 7.5±1.1%, 0.95±0.02, and 0.4±0.3mm, respectively. In the validation with patient 4DCT dataset, the mean RMSE, NRMSE, SSIM, and three-dimensional tumor localization error in six patients were 73.7±19.6 HU, 9.2±2.6%, 0.88±0.04, and 0.8±0.6mm, respectively. These results suggest that the accuracy of the proposed technique is adequate when the respiratory motion is within the range of the training dataset. In the evaluation with a marker displacement larger than that of the training dataset, the mean RMSE, NRMSE, and tumor localization error were about 100 HU, 13%, and <2.0mm, respectively, except for one case having large motion variation. The performance of the proposed method was similar to those of previous studies. Processing time to generate the volumetric image was <100ms. We have shown the feasibility of the real-time CT image generation technique for volumetric imaging.

A Simple Way to Increase the Prediction Accuracy of Hydrological Processes Using an Artificial Intelligence Model

Rainfall and evaporation, which are known as two complex and unclear processes in hydrology, are among the key processes in the design and management of water resource projects. The application of artificial intelligence, in comparison with physical and empirical models, can be effective in the face of the complexity of hydrological processes. The present study was prepared with the aim of increasing the accuracy in monthly prediction of rainfall (R) and pan evaporation (EP) by providing a simple solution to determining new inputs for forecasting scenarios. Initially, the prediction of two parameters, R and EP, for the current and one–three lead times, by determining the different input modes, was developed with the SVM model. Then, in order to increase the accuracy of the predictions, the month number (τ) was added to all scenarios in predicting both the R and EP parameters. The results of the intelligent model using several statistical indices (i.e., root mean square error (RMSE), Kling–Gupta (KGE) and correlation coefficient (CC)), with the help of case visual indicators, were compared. The month number (τ) was able to greatly improve the prediction accuracy of both the R and EP parameters under the SVM model and overcome the complexities within these two hydrological processes that the scenarios were not initially able to solve with high accuracy. This is proven in all time steps. According to the RMSE, KGE and CC indices, the highest increase in the forecast accuracy for the upcoming two months of rainfall (Rt+2) for Ardabil station in scenario 2 (SVM-2) was 19.1, 858 and 125%, and for the current month of pan evaporation (EPt) for Urmia station in scenario 6 (SVM-6), this occurred at the rates of 40.2, 11.1 and 7.6%, respectively. Finally, in order to investigate the characteristic of the month number in the SVM model under special conditions such as considering the highest values of the R and EP time series, it was proved that by using the month number of the SVM model, again, the accuracy could be improved (on average, 17% improvement for rainfall, and 13% for pan evaporation) in almost all time steps. Due to the wide range of effects of the two variables studied in the hydrological discussion, the results of the present study can be useful in agricultural sciences and in water management in general and will help owners.

Predicting Incident Duration Based on Machine Learning Methods

Traffic incidents dont only cause various levels of traffic congestion but often contribute to traffic accidents and secondary accidents, resulting in substantial loss of life, economy, and productivity loss in terms of injuries and deaths, increased travel times and delays, and excessive consumption of energy and air pollution. Therefore, it is essential to accurately estimate the duration of the incident to mitigate these effects. Traffic management center incident logs and traffic sensors data from Eastbound Interstate 70 (I-70) in Missouri, United States collected during the period from January 2015 to January 2017, with a total of 352 incident records were used to develop incident duration estimation models. This paper investigated different machine learning (ML) methods for traffic incidents duration prediction. The attempted ML techniques include Support Vector Machine (SVM), Random Forest (RF), and Neural Network Multi-Layer Perceptron (MLP). Root mean squared error (RMSE) and Mean absolute error (MAE) were used to evaluate the performance of these models. The results showed that the performance of the models was comparable with SVM models slightly outperforms the RF, and MLP models in terms of MAE index, where MAE was 14.23 min for the best-performing SVM models. Whereas, in terms of the RMSE index, RF models slightly outperformed the other two models given RMSE of 18.91 min for the best-performing RF model. Index Terms— Incident Duration, Neural Network Multi-Layer Perceptron, Random Forest, Support Vector Machine.

Interpolation of soil infiltration in furrow irrigation: Comparison of kriging, inverse distance weighting, multilayer perceptron and principal component analysis methods

&lt;p&gt;Study on soil infiltration rate as part of water cycle is essential for managing water resources and designing irrigation systems. The present study was conducted with the aim to compare Kriging, inverse distance weighting (IDW), multilayer perceptron (MLP) and principal component analysis (PCA) methods in the interpolation of soil infiltration in furrow irrigation, and determine the best interpolation method. To conduct infiltration tests, furrows were made on the farm in four triad groups. Infiltration through the blocked furrows method was measured 10, 20, 30, 40, 50, 60, 90, 120, 150, 160, 180 and 210 min after irrigation at a 10-meter distance in each furrow. Data were analyzed by GS+ and Neuro Solutions (NS) software packages. In this study, the maximum error (ME), mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative error (RE) and correlation coefficient (r) were used to compare the interpolation methods. The results of analysis of variance (ANOVA) indicated that differences in methods based on RMSE, MBE, MAE and ME indices were not significant; however, this difference was significant based on r and RE indices. According to the ANOVA results, it can be said that the PCA method with a r of 0.69 and RE of 31%, was predicted with a higher accuracy as compared to other methods.&lt;/p&gt;

Evaluation of the Dual Crop Coefficient Approach in Estimating Evapotranspiration of Drip-Irrigated Summer Maize in Xinjiang, China

A dual crop coefficient approach was validated experimentally to estimate evapotranspiration of drip-irrigated summer maize with partial mulch and no mulch in an arid region in Aksu, Xinjiang, China, during 2016–2017. In this study, five treatments were established based on fixed or variable irrigation cycles. Summer maize transpiration and evapotranspiration were estimated by the dual crop coefficient approach. Evapotranspiration was validated, and a positive regression with those values was obtained using the water balance method, with a root mean square error (RMSE) of 10 mm. The estimated transpiration also had a positive regression with measurements obtained by the stable carbon isotope technique, with a RMSE of 20 mm. By analyzing the RMSE, regression coefficients, and concordance index, we suggest that the dual crop coefficient approach is an effective method to estimate and partition evapotranspiration. Across the entire growing season for partially mulched summer maize, the estimated crop transpiration accounted for 78.7% and 76% of the total evapotranspiration in 2016 and 2017, respectively. For non-mulched summer maize, the estimated crop transpiration accounted for 64.9% of the total evapotranspiration over the entire growing season, which implied that the soil evaporation was about 12% higher than that of the partially mulched treatments. Water consumption with partial mulching was reduced by about 10%, compared with non-mulching, which indicated that mulching improved the use of water during irrigation.

Predicting saturated hydraulic conductivity using particle swarm optimization and genetic algorithm

Soil saturated hydraulic conductivity (Ks) is considered as soil basic hydraulic property, and its precision estimation is a key element in modeling water flow and solute transport processes both in the saturated and vadose zones. Although some predictive methods (e.g., pedotransfer functions, PTFs) have been proposed to indirectly predict Ks, the accuracy of these methods still needs to be improved. In this study, some easily available soil properties (e.g., particle size distribution, organic carbon, calcium carbonate content, electrical conductivity, and soil bulk density) are employed as input variables to predict Ks using a fuzzy inference system (FIS) trained by two different optimization techniques: particle swarm optimization (PSO) and genetic algorithm (GA). To verify the derived FIS, 113 soil samples were taken, and their required physical properties were measured (113 sample points × 7 factors = 791 input data). The initial FIS is compared with two methods: FIS trained by PSO (PSO-FIS) and FIS trained by GA (GA-FIS). Based on experimental results, all three methods are compared according to some evaluation criteria including correlation coefficient (r), modeling efficiency (EF), coefficient of determination (CD), root mean square error (RMSE), and maximum error (ME) statistics. The results showed that the PSO-FIS model achieved a higher level of modeling efficiency and coefficient of determination (R2) in comparison with the initial FIS and the GA-FIS model. EF and R2 values obtained by the developed PSO-FIS model were 0.69 and 0.72, whereas they were 0.63 and 0.54 for the GA-FIS model. Moreover, the results of ME and RMSE indices showed that the PSO-FIS model can estimate soil saturated hydraulic conductivity more accurate than the GA-FIS model with ME = 10.4 versus 11.5 and RMSE = 5.2 versus 5.5 for PSO-FIS and GA-FIS, respectively.

Accelerating knee MR imaging: Compressed sensing in isotropic three-dimensional fast spin-echo sequence

PurposeTo compare image quality between compressed sensing (CS)-3D-fast spin-echo (FSE) and conventional 3D-FSE sequences for knee magnetic resonance imaging (MRI). MethodsKnee MRI of 43 patients (male:female, 14:29; mean age, 53years) were acquired using conventional and CS-3D-FSE with an acceleration factor of 1.5. Overall image quality was assessed by correlation coefficient, root-mean-square error (RMSE), and structural similarity (SSIM) index. Regional image quality was evaluated using signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs). Subjective image quality was evaluated using a four-point scale. Diagnostic agreement for meniscal lesions between the two sequences was evaluated. ResultsThe scan time was reduced from 7:14–8:08 to 4:53–5:08 with CS. A strong positive correlation was observed between data of the two sequences (mean r=0.880). The RMSE (mean, 126.861) and SSIM index (mean, 0.987) were acceptable. The SNRs and CNRs were not significantly different between the two sequences (P>0.05, each). There were no significant differences in the evaluation of the menisci and cruciate ligaments, while the CS images demonstrated inferior quality of cartilage–subchondral bone delineation. Diagnostic agreement for meniscal lesions between the two sequences was very good (κ=0.943–1). ConclusionCompressed sensing-3D-FSE knee MRI produces images of acceptable quality while reducing scan time.

Role of environmental variables in the spatial distribution of soil carbon (C), nitrogen (N), and C:N ratio from the northeastern coastal agroecosystems in China

Soil organic carbon (SOC) and total nitrogen (TN) influence physical, chemical and biological properties and process in soils that determine soil fertility and enhance or maintain agricultural productivity and food security. Knowledge on the spatial distribution of SOC and TN is necessary for sustainable soil resource management. In this study, we applied boosted regression trees (BRT) model to predict and map the spatial distribution of SOC and TN in the northeastern coastal areas of China, and C:N ratio map was generated from the predicted maps of SOC and TN. A total of 149 topsoil (0–20cm) samples and 12 environmental variables that included topography, landuse and remote sensing indices, and climatic variables were selected. The performance of the models was evaluated based on cross-validation using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The BRT model was run for 100 iterations where the average of 100 SOC and TN maps was considered as final predicted maps and its standard deviation as prediction uncertainty. The BRT model showed a good predictive performance due to its higher R2 and lower MAE, and RMSE indices. The model explained approximately 64% and 56% of the total SOC and TN variability. Topographic variables showed a maximum influence in the prediction of SOC and TN, followed by vegetation and climate. The SOC and TN contents were reduced from the northwest towards the southeast of the study area; average predicted SOC and TN contents were 15.4gkg−1 and 1.01gkg−1, respectively. The spatial distribution of C:N ratio was closely related to landuse types in a decreasing order: woodland > orchard > cultivated land > grassland. We found topographic variables as main environmental indicators of SOC and TN distribution, and recommend to include them in future SOC and TN mapping studies in the coastal agroecosystems in China.

Prediction of Surface Currents Using High Frequency CODAR Data and Decision Tree at a Marine Renewable Energy Test Site

Abstract In this study, Decision Tree (DT) was employed to predict surface currents in a ¼-scaled marine renewable energy test site—Galway Bay. In training and testing models, wind speed, wind direction and tidal water elevation from a forecasting model, and observations of surface velocity components during previous hours were taken as input variables; surface velocity components were taken as the output variable. Appropriate value of Complexity Parameter (CP) in decision tree models was determined based on experiments producing the minimum Root-Mean-Square-Error (RMSE) values compared with the radar data. Statistics including RMSE, bias, correlation (R) and Scatter Index (SI) were computed between predictions and radar data to assess predictions. Results indicated that the DT model can produce satisfactory predictions of surface currents. Good performance of DT model indicated that it can be regarded as a promising approach for future applications.

Can augmented feedback facilitate learning a reactive balance task among older adults?

While concurrent augmented visual feedback of the center of pressure (COP) or center of gravity (COG) can improve quiet standing balance control, it is not known whether such feedback improves reactive balance control. Additionally, it is not known whether feedback of the COP or COG is superior. This study aimed to determine whether (1) concurrent augmented feedback can improve reactive balance control, and (2) feedback of the COP or COG is more effective. Forty-eight healthy older adults (60-75years old) were randomly allocated to one of three groups: feedback of the COP, feedback of the COG, or no feedback. The task was to maintain standing while experiencing 30s of continuous pseudo-random perturbations delivered by a moving platform. Participants completed 25 trials with or without feedback (acquisition), immediately followed by 5 trials without feedback (immediate transfer); 5 trials without feedback were completed after a 24-h delay (delayed transfer). The root mean square error (RMSE) of COP-COG, electrodermal level, and co-contraction index were compared between the groups and over time. All three groups reduced RMSE and co-contraction index from the start of the acquisition to the transfer tests, and there were no significant between-group differences in RMSE or co-contraction on the transfer tests. Therefore, all three groups learned the task equally well, and improved balance was achieved with practice via a more efficient control strategy. The two feedback groups reduced electrodermal level with practice, but the no-feedback group did not, suggesting that feedback may help to reduce anxiety.

Comparison of artificial intelligence methods and empirical equations to estimate daily solar radiation

In the present research, three artificial intelligence methods including Gene Expression Programming (GEP), Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) as well as, 48 empirical equations (10, 12 and 26 equations were temperature-based, sunshine-based and meteorological parameters-based, respectively) were used to estimate daily solar radiation in Kerman, Iran in the period of 1992–2009. To develop the GEP, ANN and ANFIS models, depending on the used empirical equations, various combinations of minimum air temperature, maximum air temperature, mean air temperature, extraterrestrial radiation, actual sunshine duration, maximum possible sunshine duration, sunshine duration ratio, relative humidity and precipitation were considered as inputs in the mentioned intelligent methods. To compare the accuracy of empirical equations and intelligent models, root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE) and determination coefficient (R2) indices were used. The results showed that in general, sunshine-based and meteorological parameters-based scenarios in ANN and ANFIS models presented high accuracy than mentioned empirical equations. Moreover, the most accurate method in the studied region was ANN11 scenario with five inputs. The values of RMSE, MAE, MARE and R2 indices for the mentioned model were 1.850MJm−2 day−1, 1.184MJm−2 day-1, 9.58% and 0.935, respectively.

A new method for estimating chlorophyll-a concentration in the Pearl River Estuary

Accurate and reliable assessment of chlorophyll-a (Chl-a) concentration in turbid waters by remote sensing is challenging due to optical complexity of case II waters. Recently, an optimization procedure based on minimizing root mean square error (RMSE) with three-band model was suggested to retrieve the Chl-a concentration in the Pearl River Estuary (PRE) in China. However, it is sensitive to initial values of model parameters and it doesn’t consider other two bands information to determine each optimal band. To estimate the Chl-a concentration according to information of all bands without initial values, we proposed an optimization procedure based on maximizing correlation of the Chl-a concentration in situ and the Chl-a concentration estimated. Firstly, correlations are computed with the three-band model, in which each band varies from the minimal value to the maximal value. Secondly, the sum of correlations for all values of other two bands is computed to find the optimal value for the first band. Lastly, the sum of correlations for all values of other one band with the first optimal band is computed to find the second and third optimal bands. The calibrated three-band model is applied to retrieve the Chl-a concentration from the reflectance data in the PRE on May 16, 2008. RMSE and correlation indices of our method and two state-of-the-art methods in this dataset are shown the effectiveness of our method. These findings imply that the extensive database of remote sensing could be used to quantitatively monitor the Chl-a concentration in the PRE.