Accuracy Root Mean Square Error Research Articles

Rapid patient-specific FEM meshes from 3D smart-phone based scans

Objective. The objective of this study was to describe and evaluate a smart-phone based method to rapidly generate subject-specific finite element method (FEM) meshes. More accurate FEM meshes should lead to more accurate thoracic electrical impedance tomography (EIT) images. Approach. The method was evaluated on an iPhone® that utilized an app called Heges, to obtain 3D scans (colored, surface triangulations), a custom belt, and custom open-source software developed to produce the subject-specific meshes. The approach was quantitatively validated via mannequin and volunteer tests using an infrared tracker as the gold standard, and qualitatively assessed in a series of tidal-breathing EIT images recorded from 9 subjects. Main results. The subject-specific meshes can be generated in as little as 6.3 min, which requires on average 3.4 min of user interaction. The mannequin tests yielded high levels of precision and accuracy at 3.2 ± 0.4 mm and 4.0 ± 0.3 mm root mean square error (RMSE), respectively. Errors on volunteers were only slightly larger (5.2 ± 2.1 mm RMSE precision and 7.7 ± 2.9 mm RMSE accuracy), illustrating the practical RMSE of the method. Significance. Easy-to-generate, subject-specific meshes could be utilized in the thoracic EIT community, potentially reducing geometric-based artifacts and improving the clinical utility of EIT.

A Spatiotemporal Fusion Model of Land Surface Temperature Based on Pixel Long Time-Series Regression: Expanding Inputs for Efficient Generation of Robust Fused Results

Spatiotemporal fusion technology effectively improves the spatial and temporal resolution of remote sensing data by fusing data from different sources. Based on the strong time-series correlation of pixels at different scales (average Pearson correlation coefficients > 0.95), a new long time-series spatiotemporal fusion model (LOTSFM) is proposed for land surface temperature data. The model is distinguished by the following attributes: it employs an extended input framework to sidestep selection biases and enhance result stability while also integrating Julian Day for estimating sensor difference term variations at each pixel location. From 2013 to 2022, 79 pairs of Landsat8/9 and MODIS images were collected as extended inputs. Multiple rounds of cross-validation were conducted in Beijing, Shanghai, and Guangzhou with an all-round performance assessment (APA), and the average root-mean-square error (RMSE) was 1.60 °C, 2.16 °C and 1.71 °C, respectively, which proved the regional versatility of LOTSFM. The validity of the sensor difference estimation based on Julian days was verified, and the RMSE accuracy significantly improved (p < 0.05). The accuracy and time consumption of five different fusion models were compared, which proved that LOTSFM has stable accuracy performance and a fast fusion process. Therefore, LOTSFM can provide higher spatiotemporal resolution (30 m) land surface temperature research data for the evolution of urban thermal environments and has great application potential in monitoring anthropogenic heat pollution and extreme thermal phenomena.

Estimation of Mean Velocity Upstream and Downstream of a Bridge Model Using Metaheuristic Regression Methods

This study compares four data-driven methods, Gaussian process regression (GPR), multivariate adaptive regression spline (MARS), M5 model tree (M5Tree), and multilinear regression (MLR), in estimating mean velocity upstream and downstream of bridges. Data were obtained through multiple experiments in a rectangular laboratory flume with glass walls 9.5 m long, 0.6 m wide, and 0.6 m deep. Four different bridge models were placed at the 6th meter of the channel to determine the average velocities upstream and downstream. Different data-driven models were implemented with different combinations of effective parameters as input. They were evaluated and compared using root mean square error (RMSE), mean absolute relative error (MARE), and Nash–Sutcliffe efficiency (NSE). The results showed that the MARS had the best efficiency in estimating the mean velocity upstream of the bridge model. At the same time, the M5Tree provided the highest performance in estimating the mean velocity downstream. The MARS method improved the estimation accuracy of GPR, M5Tree, and MLR in the test phase by 23.8%, 45.1%, and 47.4% concerning the RMSE at the upstream. The M5Tree provided better RMSE accuracy of 31.8%, 70.4%, and 75.5% at the downstream compared to MARS, GPR, and MLR, respectively. The study recommends the MARS and M5Tree for estimating mean velocities upstream and downstream of the bridge.

A Novel Physics-Statistical Coupled Paradigm for Retrieving Integrated Water Vapor Content Based on Artificial Intelligence

Retrieval of integrated water vapor content (WVC) from remote sensing data is often ill-posed because of insufficient observational information. There are many factors that cause WVC changes, which yield instability in the accuracy of many traditional algorithms. To overcome this problem, we developed a novel fully-coupled paradigm for the robust retrieval of WVC from thermal infrared remote sensing data. Through the derivation of the physical radiative transfer equation, we determined two conditions that need to be satisfied for the deep learning retrieval paradigm of WVC. The first condition is that the input parameters and output parameters of the deep learning need to be able to build a complete set of solvable equations in theory. The second condition is that, if there is a strong relationship between input parameters and output parameters, it can be directly retrieved. If it is a weak relationship, we need to use prior knowledge to improve the portability and accuracy of the algorithm. The training and test data of deep learning is composed of representative solutions of physical methods and solutions of statistical methods. The representative solutions of the physical methods were obtained from the physical forward model, and the statistical solutions were obtained from multi-source data which can compensate for the defect that the physical model cannot simulate mixed pixels. MODIS L1B data was used for case analysis of paradigm retrieval, and the analysis indicated that four thermal infrared bands were usually needed as the input parameters of deep learning and the integrated water vapor content as the output parameter. When land surface temperature and emissivity were taken as prior knowledge, the root-mean-square error (RMSE) of the retrieved WVC was 0.07 g/cm2. The optimal accuracy RMSE was 0.27 g/cm2. When there was a strong correlation between input parameters and output parameters, i.e., if there were two bands that were very sensitive to WVC in the band combination, high-precision retrieval could also be achieved without prior knowledge. All the analyses show that the paradigm of deep learning coupling physics and statistics can accurately retrieve WVC, which is a significant improvement on the traditional method and solves the problem of lack of physical interpretation of deep learning.

Short‐term wind power prediction based on the combination of firefly optimization and LSTM

AbstractWith the development of social resources, people's consumption of energy is huge, so renewable energy, such as wind energy, has been widely concerned and developed. Although there has been sufficient development of wind power generation, its output has some problems such as uncertainty, which leads to insufficient utilization of wind energy resources and uneven power output quality level, which brings great challenges to the grid connection. To solve this problem, a short‐term wind power prediction model combining firefly algorithm and long term memory network is proposed. The main motivation of the research is to improve the accuracy of wind power prediction and thus improve the utilization of wind energy resources. Compared with the existing methods, the innovation of FA‐LSTM model lies in the integration of the two algorithms, making full use of the advantages of FA in global search optimization and LSTM in time series data processing, and improving the accuracy and stability of prediction. During the experiment, we used different wind farm data to train and test the model. The results show that the FA‐LSTM model can improve the optimal fitness by more than 50% compared with other algorithms, and the iterative prediction error is smaller. Standard root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate the model. The accuracy of RMSE and MAE reached over 97% and 98% respectively. When the test data is highly volatile, the data accuracy of FA‐LSTM model reaches 92% and 94%, and the FA‐LSTM model drops to the stable value faster. FA‐LSTM model has the best fitting degree with the true value curve, and the fitting degree reaches more than 90%. Comparing the actual power and predicted power of different units, the actual power of Unit 1 is 34.875, and the predicted power obtained by FA‐LSTM model is 34.935, with an error of only 0.06. The key finding of this study is that the prediction model combining FA and LSTM has high accuracy and stability in wind power prediction, and can effectively deal with the uncertainty and volatility of wind energy resource utilization. FA‐LSTM model provides an effective solution for wind power prediction, which is helpful to improve the utilization rate of wind energy resources.

Near-Surface Soil Moisture Characterization in Mississippi’s Highway Slopes Using Machine Learning Methods and UAV-Captured Infrared and Optical Images

Near-surface soil moisture content variation is a major factor in the frequent shallow slope failures observed on Mississippi’s highway slopes built on expansive clay. Soil moisture content variation is monitored generally through borehole sensors in highway embankments and slopes. This point monitoring method lacks spatial resolution, and the sensors are susceptible to premature failure due to wear and tear. In contrast, Unmanned/Uncrewed Aerial Vehicles (UAVs) have higher spatial and temporal resolutions that enable more efficient monitoring of site conditions, including soil moisture variation. The current study focused on developing two methods to predict soil moisture content (θ) using UAV-captured optical and thermal combined with machine learning and statistical modeling. The first method used Red, Green, and Blue (RGB) color values from UAV-captured optical images to predict θ. Support Vector Machine for Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR) models were trained and evaluated for predicting θ from RGB values. The XGB model and MLR model outperformed the SVR model in predicting soil moisture content from RGB values. The R2 values for the XGB and MLR models were >0.9 for predicting soil moisture when compared to SVR (R2 = 0.25). The Root Mean Square Error (RMSE) for XGB, SVR, and MLR were 0.009, 0.025, and 0.01, respectively, for the test dataset, affirming that XGB was the best-performing model among the three models evaluated, followed by MLR and SVR. The better-performing XGB and MLR models were further validated by predicting soil moisture using unseen input data, and they provided good prediction results. The second method used Diurnal Land Surface Temperature variation (ΔLST) from UAV-captured Thermal Infrared (TIR) images to predict θ. TIR images of vegetation-covered areas and bare ground areas of the highway embankment side slopes were processed to extract ΔLST amplitudes. The underlying relationship between soil surface thermal inertia and moisture content variation was utilized to develop a predictive model. The resulting single-parameter power curve fit model accurately predicted soil moisture from ΔLST, especially in vegetation-covered areas. The power curve fit model was further validated on previously unseen TIR, and it predicted θ with an accuracy of RMSE = 0.0273, indicating good prediction performance. The study was conducted on a field scale and not in a controlled environment, which aids in the generalizability of the developed predictive models.

A Study on the Retrieval of Ozone Profiles Using FY-3D/HIRAS Infrared Hyperspectral Data

Atmospheric ozone is a pollutant gas that has an important influence on the process of atmospheric radiation transmission and climate change. The Fengyun-3D (FY-3D) satellite Hyperspectral Infrared Atmospheric Sounder (HIRAS) has better spectral performance than other remote sensing payloads. Its observation radiation data contains abundant atmospheric vertical information, which can be used for ozone retrieval, but there are no ozone profile business products being generated at present. Therefore, for the mainland of Hong Kong, based on HIRAS infrared hyperspectral observation data, we used the traditional one-dimensional variational (1D-VAR) physical retrieval algorithm, combined with the radiative transfer model for TOVS (RTTOV), and selected the spectrum channel according to the optimal sensitive profile algorithm. The artificial neural network (ANN) algorithm was used to optimize the prior profiles, and the atmospheric ozone profile retrieval system was established. Finally, a set of ozone profile retrieval schemes suitable for FY-3D/HIRAS were summarized. We used ERA5 reanalysis data and World Ozone and Ultraviolet Radiation Data Centre (WOUDC) data to determine true values. The retrieval results were compared with Global Forecast System (GFS) forecast data, Ozone Mapping and Profile Suite (OMPS) ozone products, and Atmospheric Infrared Sounder (AIRS) ozone products. The results show that our ozone profile retrieval scheme makes up for the shortcomings of the conventional physical methods in some atmospheric pressure levels. The overall root-mean-square error (RMSE) of the ozone from the ground to the top of the stratosphere is within 30% on average, which was better than that for the GFS forecast data; the retrieval accuracy RMSE (%) was less than 20% in the pressure layer with the highest ozone concentration (15–25 hPa), which is better than that of OMPS ozone products and AIRS ozone products. The retrieval results prove that FY3D/HIRAS observation data allow ozone profile retrieval. This paper provides a reference for generating independent HIRAS ozone profile product data sets in business, and provides support for the subsequent application of Fengyun-3 series meteorological satellites in atmospheric parameter remote sensing.

Nonlinear System Identification Based on an Online SCFNN With Applications in IoTs

In this paper, an online self-constructing fuzzy neural network (SCFNN) is proposed to solve four kinds of nonlinear dynamic system identification (NDSI) problems in the internet of things (IoTs). The SCFNN is capable of constructing a simple network without the need for knowledge of the NDSI. Thus, carefully setting conditions for the increased demands for fuzzy rules will make the architecture of the constructed SCFNN fairly simple. The applications of neural networks in IoTs are discussed. The authors also propose a new identification model for NDSI. Through an experimental example, it is proved that online learning can arrange membership functions in a more appropriate vector space. The performance of the online SCFNN is compared with both MLP and RBF through four extensive simulations. The comparison terms are convergence rate, training root mean square error (RMSE), test RMSE, and prediction accuracy (PA). The simulation results show that SCFNN is superior to MLP and RBF in NDSI problems.

Forest Height Inversion Based on Time–Frequency RVoG Model Using Single-Baseline L-Band Sublook-InSAR Data

The interferometric synthetic aperture radar (InSAR) technique based on time–frequency (TF) analysis has great potential for mapping the forest canopy height model (CHM) at regional and global scales, as it benefits from the additional InSAR observations provided by the sublook decomposition. Meanwhile, due to the wider swath and higher spatial resolution of single-polarization data, InSAR has a higher observation efficiency in comparison with PolInSAR. However, the accuracy of the CHM inversion obtained by the TF-InSAR method is attenuated by its inaccurate coherent scattering modeling and uncertain parameter calculation. Hence, a new approach for CHM estimation based on single-baseline InSAR data and sublook decomposition is proposed in this study. With its derivation of the coherent scattering modeling based on the scattering matrix of sublook observations, a time–frequency based random volume over ground (TF-RVoG) model is proposed to describe the relationship between the sublook coherence and the forest biophysical parameters. Then, a modified three-stage method based on the TF-RVoG model is used for CHM retrieval. Finally, the two-dimensional (2-D) ambiguous error of pure volume coherence caused by residual ground scattering and temporal decorrelation is alleviated in the complex unit circle. The performance of the proposed method was tested with airborne L-band E-SAR data at the Krycklan test site in Northern Sweden. Results show that the modified three-stage method provides a root-mean-square error (RMSE) of 5.61 m using InSAR and 14.3% improvement over the PolInSAR technique with respect to the classical three-stage inversion result. An inversion accuracy of RMSE = 2.54 m is obtained when the spatial heterogeneity of CHM is considered using the proposed method, demonstrating a noticeable improvement of 32.8% compared with results from the existing method which introduces the fixed temporal decorrelation factor.

Pilot Validation Study of Inertial Measurement Units and Markerless Methods for 3D Neck and Trunk Kinematics during a Simulated Surgery Task.

Surgeons are at high risk for developing musculoskeletal symptoms (MSS), like neck and back pain. Quantitative analysis of 3D neck and trunk movements during surgery can help to develop preventive devices such as exoskeletons. Inertial Measurement Units (IMU) and markerless motion capture methods are allowed in the operating room (OR) and are a good alternative for bulky optoelectronic systems. We aim to validate IMU and markerless methods against an optoelectronic system during a simulated surgery task. Intraclass correlation coefficient (ICC (2,1)), root mean square error (RMSE), range of motion (ROM) difference and Bland-Altman plots were used for evaluating both methods. The IMU-based motion analysis showed good-to-excellent (ICC 0.80-0.97) agreement with the gold standard within 2.3 to 3.9 degrees RMSE accuracy during simulated surgery tasks. The markerless method shows 5.5 to 8.7 degrees RMSE accuracy (ICC 0.31-0.70). Therefore, the IMU method is recommended over the markerless motion capture.

Characteristics Analysis of the Multi-Channel Ground-Based Microwave Radiometer Observations during Various Weather Conditions

Ground-based multi-channel microwave radiometers (MWRs) can continuously detect atmospheric profiles in the tropospheric atmosphere. This makes MWR an ideal tool to supplement radiosonde and satellite observations in monitoring the thermodynamic evolution of the atmosphere and improving numerical weather prediction (NWP) through data assimilation. The analysis of product characteristics of MWR is the basis for applying its data to real-time monitoring and assimilation. In this paper, observations from the latest generation of ground-based multi-channel MWR RPG-HATPRO-G5 installed in Shanghai, China, are compared with the radiosonde observations (RAOB) observed in the same location. The detection performance, characteristics of various channels, and the accuracy of the retrieval profile products of the MWR RPG are comprehensively evaluated during various weather conditions. The results show that the brightness temperatures (BTs) observed by the ground-based MWR RPG during precipitation conditions were high, which affected its detection performance. The bias and the standard deviation (SD) between the BT observed by MWR RPG and the simulated BT during clear and cloudy sky conditions were slight and large, respectively, and the coefficient of determination (R2) was high and low, respectively. However, when the cloud liquid water (CLW) information was added when simulating BT, the bias and the SD of the observed BT and the simulated BT during cloudy days were reduced and the R2 value improved, which indicated that CLW information should be taken into account when simulating BT during cloudy conditions. The temperature profiles of the MWR retrieval had the same accuracy of RMSEs (root-mean-square error) with heights during both clear-sky and cloudy sky conditions, where the RMSEs were below 2 K when the heights were below 4 km. In addition, the MWR RPG has the potential ability to retrieve the temperature inversion in the boundary layer, which has important application value for fog and air pollution monitoring.

The Impact of Sample Size and Various Other Factors on Estimation of Dichotomous Mixture IRT Models.

The purpose of this study was to examine the effects of different data conditions on item parameter recovery and classification accuracy of three dichotomous mixture item response theory (IRT) models: the Mix1PL, Mix2PL, and Mix3PL. Manipulated factors in the simulation included the sample size (11 different sample sizes from 100 to 5000), test length (10, 30, and 50), number of classes (2 and 3), the degree of latent class separation (normal/no separation, small, medium, and large), and class sizes (equal vs. nonequal). Effects were assessed using root mean square error (RMSE) and classification accuracy percentage computed between true parameters and estimated parameters. The results of this simulation study showed that more precise estimates of item parameters were obtained with larger sample sizes and longer test lengths. Recovery of item parameters decreased as the number of classes increased with the decrease in sample size. Recovery of classification accuracy for the conditions with two-class solutions was also better than that of three-class solutions. Results of both item parameter estimates and classification accuracy differed by model type. More complex models and models with larger class separations produced less accurate results. The effect of the mixture proportions also differentially affected RMSE and classification accuracy results. Groups of equal size produced more precise item parameter estimates, but the reverse was the case for classification accuracy results. Results suggested that dichotomous mixture IRT models required more than 2,000 examinees to be able to obtain stable results as even shorter tests required such large sample sizes for more precise estimates. This number increased as the number of latent classes, the degree of separation, and model complexity increased.

Methods of mapping the lands disturbed by mining operations and accuracy of cartographic images obtained from Unmanned Aerial Vehicles: A review

Purpose. Analyzing the land disturbance consequences caused by surface mining operations and methods for mapping these lands, as well as studying the accuracy of point coordinates of digital images obtained from materials of aerial photographic surveys using Unmanned Aerial Vehicles (UAVs). Performing a quantitative assessment of the Root Mean Square Error (RMSE) of point coordinates on cartographic images and determining the dependences of the RMSE of point coordinates on the photogrammetric parameters. Methods. The review of previous research publications within the framework of the presented subject is performed in the following sequence: analysis of ecosystem disbalance as a result of surface mining operations; based on previous studies, collecting the data for quantitative assessment of accuracy in the form of RMSE of point coordinates on cartographic images obtained from the materials of aerial photographic survey using UAVs; statistical study of the relationship between the RMSE and photographic survey parameters. Findings. The methods for mapping the disturbed lands to return them to their natural state after the consequences of surface mining operations are presented, based on a review of previous research publications on the subject of the work. According to the previous studies, the RMSE of point coordinates of cartographic images has been systematized, and, based on this, the accuracy of topographic plans has been determined for them. Statistical studies of the relationship between the quantitative assessment of the RMSE (xy) and RMSE (z) accuracy in relation to the photographic survey parameters have been performed. In addition, the scattering diagrams of the correlation dependence and the range of RMSE relative frequency have been presented. Originality. Based on a critical analysis of previous studies on the lack of quantitative accuracy regulation of cartographic images obtained from aerial photographic survey using UAVs, the RMSE systematics has been performed in terms of the photographic survey height. Based on this, the accuracy of topographic plans, the relative frequency of horizontal and vertical distribution of errors, the mean value and the root mean square error (σ) have been determined. Practical implications. The systematics of the RMSE values of cartographic image point coordinates for certain photographic survey parameters and the scale of topographic images makes it possible to take this into account in the project of aerial photographic survey using UAVs of lands for various purposes, as well as to choose the height and photographic equipment according to the required accuracy.

Flash Flood Water Depth Estimation Using SAR Images, Digital Elevation Models, and Machine Learning Algorithms

In this article, the local spatial correlation of multiple remote sensing datasets, such as those from Sentinel-1, Sentinel-2, and digital surface models (DSMs), are linked to machine learning (ML) regression algorithms for flash floodwater depth retrieval. Edge detection filters are applied to remote sensing images to extract features that are used as independent features by ML algorithms to estimate flood depths. Data of dependent variables were obtained from the Hydrologic Engineering Center’s River Analysis System (HEC-RAS 2D) simulation model, as applied to the New Cairo, Egypt, post-flash flood event from 24–26 April 2018. Gradient boosting regression (GBR), random forest regression (RFR), linear regression (LR), extreme gradient boosting regression (XGBR), multilayer perceptron neural network regression (MLPR), k-nearest neighbors regression (KNR), and support vector regression (SVR) were used to estimate floodwater depths; their outputs were compared and evaluated for accuracy using the root-mean-square error (RMSE). The RMSE accuracy for all ML algorithms was 0.18–0.22 m for depths less than 1 m (96% of all test data), indicating that ML models are relatively portable and capable of computing floodwater depths using remote sensing data as an input.

Applicability and improvement of different evapotranspiration methods of reference crops in Jiangxi Province

To achieve accurate evaluation of evapotranspiration of reference crops (ET0) in Jiangxi, China, in the absence of systematic climatological data, with reference to the FAO-56 Penman–Monteith (P-M) equation, the Priestley-Taylor (P–T) method, the Makkink method, the Hargreaves-Samani (H–S) method, the Irmak-Allen (I-A) method, the Penman1948 (48PM) method, the Penman-Van Bavel (PVB) method, the Baier-Robertson (B-R) method, the improved Baier-Robertson (M-B-R) method, the Schendel (Sch) method, the Turc method, the Jensen-Haise (J-H) method, and the Brutsaert-Stricker (B-S) method were used to evaluate the daily climatological data collected by 26 weather stations in Jiangxi, China, and 17 weather stations in adjacent provinces. The results were compared with each other and parameter rate determination was conducted. The results indicated that the Turc method exhibited optimized applicability before parameter rate determination and the average root mean square error (RMSE) and the average normalized root mean square error (NRMSE) by this method were 0.39 mm/d and 0.157 mm, respectively. However, parameter rate determination led to negligible improvement in accuracy for this method. The Turc method could be directly applied in Jiangxi (except Nanchang). For special distribution of error after parameter rate determination, all methods exhibited significant errors in Northern Jiangxi. Herein, the 48PM method and the B-S method showed good applicability after parameter rate determination and RMSE and NRMSE of data by these methods ranged in 0.06 ~ 0.34 mm/d and 0.08 ~ 0.27, 8 ~ 27%, respectively, and their d-indices were close to 1. The annual over-estimations in weather stations in Jiangxi were below 30 mm. In the absence of data about relative humidity and wind speed, the P–T method was an appropriate simplified method for Jiangxi. In this case, α was slightly lower than the default value (1.05 ~ 1.18), RMSE was within 0.21 ~ 0.66 mm/d, and NRMSE was within 0.08 ~ 0.308 ~ 30%. Accuracy of RMSE, d-index, and NRMSE of data by the P–T method, the I-A method, and the PVB method was consistent with all stations, while that by the Mak method was slightly lower, which could be attributed to severe over-estimation in July and August. RMSE of the H–S method, the B-R method, the M-B-R method, the J-H method, and the Sch method were above 0.75 mm/d and these methods were not suitable for accurate evaluation of ET0 in Jiangxi, China. The annual ET0 was calculated by various methods (except the 48PM method and the B-S method) exhibited significant variation around 2003. This may be attributed to significant changes in certain meteorological factors over recent years.

Modeling of TST Using Biological Signals and KL-Hausdorff Distance Similarity Measure for Gene Based Parkinson’s Disease Recognition

Genetic research experienced drastic transformation since past decades, which benefits the biological area eventually for the detection of neurodegenerative ailment like Parkinson’s disease (PD). Recently, rigorous investigate had been conceded out for of PDs detection instigated through-sequence -and recessive auto-somal-of dominant-genes such as PARK2, LRRK2, SNCA, PARK7 and PINK1. Several inherent based similarity degree representations such as Cosine similarity and Hamming Distance model were introduced for the detection of these genes. However, these representations detect 2 to 3 gene sequence barely by maximum Root Mean Square Error (RMSE) and minimum accuracy rate. The ratio of misclassification is too great for prevailing scheme. To perceive PD through low RMSE and high accuracy a Kullback-Leibler Hausdroff distance (KL-H) similarity measure model is proposed so as to discover the affected patient pattern efficiently. It works in two phases, in first, protein sequence of amino acid is determined with the use of model transcription, splicing and translation (TST). The second stage in turn distinguish PD that depends on the model of similarity measure which comprise assessment of template sequence and specified sequence with the use of Hausdorff distance and KL-distance process. The property of nucleotide density in KL distance measure algorithm was employed. The result analysis and comparative study were presented among the proposed and existing system. We attained maximum accuracy of 88%, with sensitivity 67.86%, specificity 93.81%, precision 76%, F1 score 71.69%, minimum RMSE (12%) and FPR (6.19%)in comparison to the prevailing similarity measurement model.

Prediction of PM2.5 Concentration Based on the LSTM-TSLightGBM Variable Weight Combination Model

PM2.5 is one of the main pollutants that cause air pollution, and high concentrations of PM2.5 seriously threaten human health. Therefore, an accurate prediction of PM2.5 concentration has great practical significance for air quality detection, air pollution restoration, and human health. This paper uses the historical air quality concentration data and meteorological data of the Beijing Olympic Sports Center as the research object. This paper establishes a long short-term memory (LSTM) model with a time window size of 12, establishes a T-shape light gradient boosting machine (TSLightGBM) model that uses all information in the time window as the next period of prediction input, and establishes a LSTM-TSLightGBM model pair based on an optimal weighted combination method. PM2.5 hourly concentration is predicted. The prediction results on the test set show that the mean squared error (MAE), root mean squared error (RMSE), and symmetric mean absolute percentage error (SMAPE) of the LSTM-TSLightGBM model are 11.873, 22.516, and 19.540%, respectively. Compared with LSTM, TSLightGBM, the recurrent neural network (RNN), and other models, the LSTM-TSLightGBM model has a lower MAE, RMSE, and SMAPE, and higher prediction accuracy for PM2.5 and better goodness-of-fit.

Estimating Soil Organic Matter Content Using Sentinel-2 Imagery by Machine Learning in Shanghai

Soil organic matter (SOM) plays an important role in the field of climate change and terrestrial ecosystems. SOM in large areas, especially in urban areas, is difficult to monitor and estimate by traditional methods. Urban land structure is complex, and soil is a mixture of organic and inorganic constituents with different physical and chemical properties. Previous studies showed that remote sensing techniques that provide diverse data in the visible-near-infrared (VNIR)-shortwave infrared (SWIR) spectral range, are promising in the prediction of SOM content on a large scale. Sentinel-2 covers the important spectral bands (VNIR-SWIR) for SOM prediction with a short revisit time. Thus, this article aimed to evaluate the capacity of Sentinel-2 for SOM prediction in an urban area (i.e., Shanghai). 103 bare soil samples filtrated from 398 soil samples at a depth of 20 cm were selected. Three methods, partial least square regression (PLSR), artificial neural network (ANN), and support vector machine (SVM), were applied. The root mean square error (RMSE) of modelling (mRMSE) and the coefficient of determination ( $R^{2}$ ) of modelling $(mR^{2})$ were used to reflect the accuracy of the model. The results show that PLSR has the poorest performance. ANN has the highest modelling accuracy (mRMSE = 7.387 g kg $^{-1}$ , $mR^{2}=0.446$ ). The ANN prediction accuracy of RMSE (pRMSE) is 4.713 g kg $^{-1}$ and the prediction accuracy of $R^{2}~(pR^{2})$ is 0.723. For SVR, the pRMSE is 4.638 g kg $^{-1}$ , and the $pR^{2}$ is 0.732. The prediction accuracy of SVR is slightly higher than that of ANN. The spatial distribution of SOM demonstrates that the value obtained by ANN is the closest to the range of the bare soil samples, and ANN performs better in vegetation-covered areas. Therefore, Sentinel-2 can be used to estimate SOM content in urban areas, and ANN is a promising method for SOM estimation.

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

The potential or reference evapotranspiration (ET0) is considered as one of the fundamental variables for irrigation management, agricultural planning, and modeling different hydrological pr°Cesses, and therefore, its accurate prediction is highly essential. The study validates the feasibility of new temperature based heuristic models (i.e., group method of data handling neural network (GMDHNN), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree)) for estimating monthly ET0. The outcomes of the newly developed models are compared with empirical formulations including Hargreaves-Samani (HS), calibrated HS, and Stephens-Stewart (SS) models based on mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency. Monthly maximum and minimum temperatures (Tmax and Tmin) observed at two stations in Turkey are utilized as inputs for model development. In the applications, three data division scenarios are utilized and the effect of periodicity component (PC) on models’ accuracies are also examined. By importing PC into the model inputs, the RMSE accuracy of GMDHNN, MARS, and M5Tree models increased by 1.4%, 8%, and 6% in one station, respectively. The GMDHNN model with periodic input provides a superior performance to the other alternatives in both stations. The recommended model reduced the average error of MARS, M5Tree, HS, CHS, and SS models with respect to RMSE by 3.7–6.4%, 10.7–3.9%, 76–75%, 10–35%, and 0.8–17% in estimating monthly ET0, respectively. The HS model provides the worst accuracy while the calibrated version significantly improves its accuracy. The GMDHNN, MARS, M5Tree, SS, and CHS models are also compared in estimating monthly mean ET0. The GMDHNN generally gave the best accuracy while the CHS provides considerably over/under-estimations. The study indicated that the only one data splitting scenario may mislead the modeler and for better validation of the heuristic methods, more data splitting scenarios should be applied.

Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm.

Suspended sediment load (SSL) estimation is a required exercise in water resource management. This article proposes the use of hybrid artificial neural network (ANN) models, for the prediction of SSL, based on previous SSL values. Different input scenarios of daily SSL were used to evaluate the capacity of the ANN-ant lion optimization (ALO), ANN-bat algorithm (BA) and ANN-particle swarm optimization (PSO). The Goorganrood basin in Iran was selected for this study. First, the lagged SSL data were used as the inputs to the models. Next, the rainfall and temperature data were used. Optimization algorithms were used to fine-tune the parameters of the ANN model. Three statistical indexes were used to evaluate the accuracy of the models: the root-mean-square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). An uncertainty analysis of the predicting models was performed to evaluate the capability of the hybrid ANN models. A comparison of models indicated that the ANN-ALO improved the RMSE accuracy of the ANN-BA and ANN-PSO models by 18% and 26%, respectively. Based on the uncertainty analysis, it can be surmised that the ANN-ALO has an acceptable degree of uncertainty in predicting daily SSL. Generally, the results indicate that the ANN-ALO is applicable for a variety of water resource management operations.