Decrease In Root Mean Square Error Research Articles

A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction

In the quest for sustainable urban development, precise quantification of urban green space is paramount. This research delineates the implementation of a Cosine Adaptive Particle Swarm Optimization Long Short-Term Memory (CAPSO-LSTM) model, utilizing a comprehensive dataset from Beijing (1998–2021) to train and test the model. The CAPSO-LSTM model, which integrates a cosine adaptive mechanism into particle swarm optimization, advances the optimization of long short-term memory (LSTM) network hyperparameters. Comparative analyses are conducted against conventional LSTM and Partical Swarm Optimization (PSO)-LSTM frameworks, employing mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) as evaluative benchmarks. The findings indicate that the CAPSO-LSTM model exhibits a substantial improvement in prediction accuracy over the LSTM model, manifesting as a 66.33% decrease in MAE, a 73.78% decrease in RMSE, and a 57.14% decrease in MAPE. Similarly, when compared to the PSO-LSTM model, the CAPSO-LSTM model demonstrates a 58.36% decrease in MAE, a 65.39% decrease in RMSE, and a 50% decrease in MAPE. These results underscore the efficacy of the CAPSO-LSTM model in enhancing urban green space area prediction, suggesting its significant potential for aiding urban planning and environmental policy formulation.

Application of the improved dung beetle optimizer, muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area, China

Due to the lack of accurate data and complex parameterization, the prediction of groundwater depth is a challenge for numerical models. Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas. In this study, two new models are applied to the prediction of groundwater depth in the Ningxia area, China. The two models combine the improved dung beetle optimizer (DBO) algorithm with two deep learning models: The Multi-head Attention–Convolution Neural Network–Long Short Term Memory networks (MH-CNN-LSTM) and the Multi-head Attention–Convolution Neural Network–Gated Recurrent Unit (MH-CNN-GRU). The models with DBO show better prediction performance, with larger R (correlation coefficient), RPD (residual prediction deviation), and lower RMSE (root-mean-square error). Compared with the models with the original DBO, the R and RPD of models with the improved DBO increase by over 1.5%, and the RMSE decreases by over 1.8%, indicating better prediction results. In addition, compared with the multiple linear regression model, a traditional statistical model, deep learning models have better prediction performance.摘要本研究将两个新模型应用于位于中国西北干旱半干旱区的宁夏地区地下水深度预测. 这两个模型将改进的蜣螂优化 (DBO) 算法与两个深度学习模型相结合, 即多头注意力-卷积神经网络-长短期记忆网络和多头注意力-回旋神经网络-门控递归单元. 带有DBO的模型预测结果表现出更大的相关系数 (R) , 残差预测偏差 (RPD) 和较低的均方根误差 (RMSE) , 预测结果更好. 此外, 与DBO模型相比, 改进后的DBO模型的R和RPD增加了1.5%以上, RMSE降低了1.8%以上, 表明预测结果更好. 与传统的统计模型多元线性回归模型相比, 深度学习模型具有更好的预测性能.

Motion correction of 3D dynamic contrast-enhanced ultrasound imaging without anatomical B-Mode images: Pilot evaluation in eight patients.

Dynamic contrast-enhanced ultrasound (DCE-US) is highly susceptible to motion artifacts arising from patient movement, respiration, and operator handling and experience. Motion artifacts can be especially problematic in the context of perfusion quantification. In conventional 2D DCE-US, motion correction (MC) algorithms take advantage of accompanying side-by-side anatomical B-Mode images that contain time-stable features. However, current commercial models of 3D DCE-US do not provide side-by-side B-Mode images, which makes MC challenging. This work introduces a novel MC algorithm for 3D DCE-US and assesses its efficacy when handling clinical data sets. In brief, the algorithm uses a pyramidal approach whereby short temporal windows consisting of three consecutive frames are created to perform local registrations, which are then registered to a master reference derived from a weighted average of all frames. We applied the algorithm to imaging studies from eight patients with metastatic lesions in the liver and assessed improvements in original versus motion corrected 3D DCE-US cine using: (i) frame-to-frame volumetric overlap of segmented lesions, (ii) normalized correlation coefficient (NCC) between frames (similarity analysis), and (iii) sum of squared errors (SSE), root-mean-squared error (RMSE), and r-squared (R2 ) quality-of-fit from fitted time-intensity curves (TIC) extracted from a segmented lesion. We noted improvements in frame-to-frame lesion overlap across all patients, from 68%±13% without correction to 83%±3% with MC (p=0.023). Frame-to-frame similarity as assessed by NCC also improved on two different sets of time points from 0.694±0.057 (original cine) to 0.862±0.049 (corresponding MC cine) and 0.723±0.066 to 0.886±0.036 (p ≤ 0.001 for both). TIC analysis displayed a significant decrease in RMSE (p=0.018) and a significant increase in R2 goodness-of-fit (p=0.029) for the patient cohort. Overall, results suggest decreases in 3D DCE-US motion after applying the proposed algorithm.

Quantitative Analysis of Near-Infrared Spectroscopy Using the BEST-1DConvNet Model

In the quest for enhanced precision in near-infrared spectroscopy (NIRS), in this study, the application of a novel BEST-1DConvNet model for quantitative analysis is investigated against conventional support vector machine (SVM) approaches with preprocessing such as multiplicative scatter correction (MSC) and standard normal variate (SNV). We assessed the performance of these methods on NIRS datasets of diesel, gasoline, and milk using a Fourier Transform Near-Infrared (FT-NIR) spectrometer having a wavelength range of 900–1700 nm for diesel and gasoline and 4000–10,000 nm for milk, ensuring comprehensive spectral capture. The BEST-1DConvNet’s effectiveness in chemometric predictions was quantitatively gauged by improvements in the coefficient of determination (R2) and reductions in the root mean square error (RMSE). The BEST-1DConvNet model achieved significant performance enhancements compared to the MSC + SNV + 1D + SVM model. Notably, the R2 value for diesel increased by approximately 48.85% despite a marginal RMSE decrease of 0.92%. R2 increased by 11.30% with a 3.32% RMSE reduction for gasoline, and it increased by 8.71%, accompanied by a 3.51% RMSE decrease for milk. In conclusion, the BEST-1DConvNet model demonstrates superior predictive accuracy and reliability in NIRS data analysis, marking a substantial leap forward in spectral analysis technology. This advancement could potentially streamline their integration into various industrial applications and highlight the role of convolutional neural networks in future chemometric methodologies.

An investigation of inversion method to measure the radial velocity of Kuroshio from Sentinel-1 SAR data

ABSTRACT Studies on the retrieval of ocean surface current by Synthetic Aperture Radar (SAR) mainly concentrate on coastal regions with land coverage, allowing for continuous land to estimate non-geophysical Doppler shifts. As a strong western boundary in the Western Pacific, the Kuroshio flows through mostly islands or open sea, and lacks land coverage for range bias correction. We propose a non-geophysical Doppler shift correction algorithm suitable for Kuroshio observation. Three solutions matching different land coverage were adopted in the range direction, and the scalloping was removed by using average filtering in the azimuth direction. Comparison between SAR radial velocity and Global Surface Lagrangian Drifter (GLD) reveals that the correlation coefficient (R 2) increase to 0.686, and root mean square error (RMSE) of 0.215 m/s after non-geophysical corrections, while the wind-wave bias corrections make R 2 increase to 0.806 and RMSE decrease to 0.13 m/s. There is also a good consistency between SAR inversion and Regional Ocean Circulation Model (ROMS). Sensitivity analysis illustrates that ascending pass is more suitable for monitoring Kuroshio, and high incidence angles are less affected by ocean conditions and non-geophysical factors. The algorithm can be used in regions with discontinuous or absent land.

Enhancing K-Nearest Neighbors Algorithm in Wireless Sensor Networks through Stochastic Fractal Search and Particle Swarm Optimization

The utilization of wireless sensor networks (WSNs) holds significant importance in diverse data collection applications. Efficient operation of computers, especially in predictive tasks, is imperative for obtaining accurate results within WSNs. This research introduces an innovative approach employing Stochastic Fractal Search-Particle Swarm Optimization (SFS-PSO) to enhance the performance of the K-Nearest Neighbors (KNN) algorithm. The proposed methodology initiates with the establishment of a particle population, dynamically adjusting their positions and velocities and integrating a diffusion process. Through an iterative process of incremental adjustments and evaluations, the algorithm fine-tunes its parameters, resulting in a refined KNN regression model. The enhanced model exhibits substantial improvements, as indicated by the notable reduction in root mean square error (RMSE) and mean absolute error (MAE), accompanied by a strengthened correlation between variables. The favorable outcomes underscore the efficacy of the SFS-PSO optimization technique in augmenting the KNN algorithm's performance within wireless sensor networks. In simpler terms, the application of SFS-PSO in conjunction with KNN leads to a significant decrease in RMSE, reaching a value as low as 0.00894, demonstrating the notable effectiveness of this optimization approach in refining the predictive capabilities of the KNN algorithm in the context of WSNs.

An Improved VMD–EEMD–LSTM Time Series Hybrid Prediction Model for Sea Surface Height Derived from Satellite Altimetry Data

Changes in sea level exhibit nonlinearity, nonstationarity, and multivariable characteristics, making traditional time series forecasting methods less effective in producing satisfactory results. To enhance the accuracy of sea level change predictions, this study introduced an improved variational mode decomposition and ensemble empirical mode decomposition–long short-term memory hybrid model (VMD–EEMD–LSTM). This model decomposes satellite altimetry data from near the Dutch coast using VMD, resulting in components of the intrinsic mode functions (IMFs) with various frequencies, along with a residual sequence. EEMD further dissects the residual sequence obtained from VMD into second-order components. These IMFs decomposed by VMD and EEMD are utilized as features in the LSTM model for making predictions, culminating in the final forecasted results. The experimental results, obtained through a comparative analysis of six sets of Dutch coastal sea surface height data, confirm the excellent accuracy of the hybrid model proposed (root mean square error (RMSE) = 47.2 mm, mean absolute error (MAE) = 33.3 mm, coefficient of determination (R2) = 0.9). Compared to the VMD-LSTM model, the average decrease in RMSE was 58.7%, the average reduction in MAE was 60.0%, and the average increase in R2 was 49.9%. In comparison to the EEMD-LSTM model, the average decrease in RMSE was 27.0%, the average decrease in MAE was 28.0%, and the average increase in R2 was 6.5%. The VMD–EEMD–LSTM model exhibited significantly improved predictive performance. The model proposed in this study demonstrates a notable enhancement in global mean sea lever (GMSL) forecasting accuracy during testing along the Dutch coast.

Wind turbine output power prediction by a segmented multivariate polynomial-XGBoost model

ABSTRACT Accurate prediction of wind turbine output power is important to ensure safe, stable, and efficient wind power generation. In this study, a hybrid prediction model based on a combination of segmented multivariate polynomial and extreme gradient boosting (XGBoost) under different operating conditions is proposed. First, the wind farm supervisory control and data acquisition (SCADA) data are processed using isolated forest for outliers, in order to improve model prediction accuracy. Then, random forest is used for feature selection, where wind speed, rotor speed, pitch angle, wind direction and temperature are found to be the most relevant features related to the output power. Next, based on the analysis of the wind turbine characteristic curves, its operating state is divided into three phases: constant power (CP), constant speed (CS), and maximum power point tracking (MPPT). Different polynomials are established for each phase, the CP phase is of order 5, the CS phase is of order 7, and the MPPT phase is of order 4. Finally, the XGBoost model is trained for power prediction with polynomial new features for each phase. The segmented multivariate polynomial-XGBoost model has a mean absolute percentage error (MAPE) of 0.04, a root mean square error (RMSE) of 49, and a coefficient of determination R2 of 0.99. Compared to the single XGBoost model and the unsegmented polynomial-XGBoost model, the MAPE decreases by 42% and 33% respectively, while RMSE decreases by 50% and 12% respectively, resulting in high prediction accuracy.

The effect of electricity consumption determinants in household load forecasting models

Usually, household electricity consumption fluctuates, often driven by several electrical consumption determinants such as income, household size, and price. Recently, research studies on the investigation of predictor variables in household electricity consumption have increased especially in the developing and newly industrialized countries. However, the studies just focus on identifying the predictor variables of household electricity consumption that influence load forecasting models. In Tanzania, for instance, scholars found that using the “income” determinant improves the performance of a forecasting model. The scholars suggest without any empirical bases that adding more predictor variables would have improved the accuracy of the model. This study aims to analyze the effect of the number of predictor variables on household load forecasting performance based on Tanzania’s data. Nonlinear regression based on a Weibull function and multivariate adaptive regression splines approaches are used for this purpose. Our findings indicate that income, household size, and number of appliances are common predictor variables of household consumption in developing countries. The measured forecasting root-mean-square error (RMSE) when using income, household size, and the number of appliances is 0.8244, 1.2314, and 0.9868, respectively. Finally, we forecasted load using all three determinants and the RMSE dropped to 0.7031. Having obtained the smaller value of RMSE when all predictors are used reveals that the inclusion of all three predictor variables in load forecasting leads to a significant decrease in RMSE by 14.73%. Therefore, the study recommends using multiple predictor variables in load forecasting models to increase accuracy.

Tuning spatial parameters of Geographical Random Forest: the case of agricultural drought

Machine learning algorithms are widely used methods in geographical research. However, these algorithms are not properly exploiting the underlying spatial relationships present in the geographical data. One of the approaches, which addresses this problem, is based on an ensemble of local models, which are constructed from samples in close proximity to the location of prediction. This concept was applied to the Random Forest (RF) algorithm, creating a Geographical Random Forest (GRF). This study aims to further develop GRF by tuning the spatial parameters for each location in case of agricultural drought. In addition to tuning, the explanatory property of RF within the framework GRF is explored. Four machine learning models were constructed; regular RF, regular RF with spatial covariates, GRF, and GRF with the tuning of spatial parameters. Models were evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Although the decrease in RMSE in this very case is relatively small, the method may provide higher improvement with different datasets.

Reconstruction of Land Surface Temperature Derived from FY-4A AGRI Data Based on Two-Point Machine Learning Method

Land surface temperature (LST) is one of the most important parameters of the interface between the earth surface and the atmosphere, and it plays a significant role in many research fields, such as agriculture, climate, hydrology, and the environment. However, the thermal infrared band of remote sensors is easily affected by clouds and aerosols, leading to many data gaps in LST products, which restricts the subsequent application of these products. In this paper, Beijing, China, is selected as the study area, and the LST data retrieved from Fengyun 4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) are reconstructed based on the two-point machine learning method. Firstly, the two-point machine learning model is built to reconstruct the theoretical clear-sky LST from simulated and actual images, and the accuracy of the reconstruction results is evaluated compared with the random forest algorithm and the inverse distance weighted method. Secondly, the actual LST under the influence of clouds is reconstructed by using the ERA5 reanalysis LST data as the auxiliary data, and the reconstruction accuracy is then evaluated by the field measurement LST data. The experimental results show that (1) the prediction accuracy of the two-point machine learning method is higher than that of the random forest method in both simulated data and actual data experiments; (2) the R2 of reconstructed LST under theoretical clear-sky conditions is 0.6860 and the root mean square error (RMSE) is 2.9 K, while the R2 of the reconstructed accuracy of actual LST under clouds is 0.7275 and the RMSE is 2.6 K, i.e., the RMSE decreases by 10.34%; (3) the two-point machine method combined with the auxiliary ERA5 LST data can well reconstruct LST under cloudy conditions and present a reasonable LST distribution.

Predicting the Surface Soil Texture of Cultivated Land via Hyperspectral Remote Sensing and Machine Learning: A Case Study in Jianghuai Hilly Area

Soil reflectance spectra and hyperspectral images have great potential to monitor and evaluate soil texture in large-scale scenarios. In hilly areas, sand, clay, and silt have similar spectral characteristics in visible, near-infrared, and short-wave infrared (VNIR-SWIR) reflection spectra. Soil texture spectra belong to mixed spectra despite some differences in particle size, mineral composition, and water content, making their distinction difficult. The accurate identification of the content within different particle sizes is difficult as it involves capturing spectral reflection features. Therefore, this study aimed to predict soil texture content through machine learning and unmixing the soil texture’s spectra while also comparing their respective modelling performances. Taking typical cultivated land in the Jianghuai hills as an example, the GaoFen-5 Advanced Hyperspectral Imaging (GF-5 AHSI) laboratory spectra of soil samples were used to predict sand, silt, and clay particle contents using partial least squares regression (PLSR) and convolutional neural networks (CNNs). The entire spectra of VNIR-SWIR regions were smoothed, and the dimensions were reduced via principal component analysis (PCA). The prediction models of sand, silt, and clay particle content were constructed, and inversion maps were generated using AHSI. The results showed that the PCA-CNN model achieved a higher prediction precision than the PCA-PLSR in both ASD and GF-5 data. Clay content exhibited the highest predictive performance with a coefficient of determination (R2) of 0.948 and 0.908 and a root mean square error (RMSE) of 26.51 g/kg and 31.24 g/kg, respectively, which represented a 39.0% and 79.8% increase in R2 and a 57% and 57.1% decrease in RMSE compared to that of the PCA-PLSR. This method indicates that the PCA-CNN model can effectively achieve nonlinear interactions between multiple spectral components and better model and fit spectral mixing processes; moreover, it provides an alternative method for investigating the spatial distribution of soil texture.

Radiometric Cross-Calibration of Wide-Field-of-View Cameras Based on Gaofen-1/6 Satellite Synergistic Observations Using Landsat-8 Operational Land Imager Images: A Solution for Off-Nadir Wide-Field-of-View Associated Problems

The Gaofen-1 satellite is equipped with four wide-field-of-view (WFV) instruments, enabling an impressive spatial resolution of 16 m and a combined swath exceeding 800 km. These WFV images have shown their valuable applications across diverse fields. However, achieving accurate radiometric calibration is an essential prerequisite for establishing reliable connections between satellite signals and biophysical, as well as biochemical, parameters. However, observations with large viewing angles (>20°) pose new challenges due to the bidirectional reflectance distribution function (BRDF) effect having a pronounced impact on the accuracy of cross-radiation calibrations, especially for the off-nadir WFV1 and WFV4 cameras. To overcome this challenge, a novel approach was introduced utilizing the combined observations from the Gaofen-1 and Gaofen-6 satellites, with Landsat-8 OLI serving as a reference sensor. The key advantage of this synergistic observation strategy is the ability to obtain a greater number of image pairs that closely resemble Landsat-8 OLI reference images in terms of geometry and observation dates. This increased availability of matching images ensures a more representative dataset of the observation geometry, enabling the derived calibration coefficients to be applicable across various sun–target–sensor geometries. Then, the geometry angles and bidirectional reflectance information were put into a Particle Swarm Optimization (PSO) algorithm incorporating radiative transfer modeling. This PSO-based approach formulates cross-calibration as an optimization problem, eliminating the reliance on complex BRDF models and satellite-based BRDF products that can be affected by cloud contamination. Extensive validation experiments involving satellite data and in situ measurements demonstrated an average uncertainty of less than eight percent for the proposed cross-radiation calibration scheme. Comparisons of top-of-atmosphere (TOA) results calibrated using our proposed scheme, the previous traditional radiative transfer modeling using MODIS BRDF products for BRDF correction (RTM-BRDF) method, and official coefficients reveal the superior accuracy of our method. The proposed scheme achieves a 36.99% decrease in root mean square error (RMSE) and a 38.13% increase in mean absolute error (MAE) compared to official coefficients. Moreover, it achieves comparable accuracy to the RTM-BRDF method while eliminating the need for MODIS BRDF products, with a decrease in RMSE exceeding 14% for the off-nadir WFV1 and WFV4 cameras. The results substantiate the efficacy of the proposed scheme in enhancing cross-calibration accuracy by improving image match-up selection, efficiently removing BRDF effects, and expanding applicability to diverse observation geometries.

A novel physiological signal denoising method coupled with multispectral adaptive wavelet denoising(MAWD) and unsupervised source counting algorithm(USCA)

In order to improve the quality of physiological signals, a combined study of blind source separation and wavelet thresholding methods was conducted, resulting in the proposal of a multispectral adaptive wavelet denoising (MAWD) method. This method was employed in conjunction with an improved unsupervised source counting algorithm (USCA). To evaluate the effectiveness of the proposed approach, three methods were used to calculate signal-to-noise ratio (SNR) and root mean square error (RMSE): soft thresholding, hard thresholding, and adaptive thresholding. The results demonstrated that the proposed method exhibited strong applicability under soft thresholding. Specifically, compared to hard thresholding, the enhanced signal using soft thresholding showed an approximately 44.2% increase in SNR and a 28.8% decrease in RMSE, along with a 1.4% reduction in processing time. Moreover, when compared to adaptive thresholding, soft thresholding exhibited approximately 706% improvement in SNR, a 16.7% decrease in RMSE, and a 3.0% reduction in processing time. Multiple experiments were conducted to determine the optimal peak detection threshold range for USCA, which was found to be within the interval [0.001, 0.0001]. This range facilitated the separation of more sources, thereby enhancing the separation effectiveness and accuracy. To substantiate the effectiveness of the USCA method, tests were conducted on publicly available datasets of EMG, ECG, and EEG signals, all of which consistently demonstrated the advantages of this approach. Data AvailabilityThe authors do not have permission to share data.

Improving Forest Canopy Height Estimation Using a Semi-Empirical Approach to Overcome TomoSAR Phase Errors

Forest canopy height is an important forest indicator parameter. Synthetic aperture radar tomography (TomoSAR) is an effective method to characterize forest canopy height and describe forest 3D structure; however, the residual phase error of TomoSAR affects the focus of the relative reflectance and can lead to errors in forest canopy height estimation. Therefore, this paper proposes a semi-empirical method to overcome the residual phase effects on forest canopy height estimation. In this study, we used airborne multi-baseline UAVSAR data to estimate forest canopy height via TomoSAR techniques and applied a semi-empirical method to improve forest canopy height estimation without phase calibration to mitigate the effects of phase error. The process is divided into three stages: the first step uses a semi-empirical method to initially determine the optimal relative reflectance loss threshold (K) by excluding the inverse extremes; in the second and third steps, the percentile height was used to gradually reduce the height interval between the upper and lower envelopes to minimize overestimation of extreme values and the lower vegetation. When the root mean square error (RMSE) was minimized, the percentile combinations were determined between the inversion results and a LiDAR dataset of the area. The results show that the canopy height estimation results are not satisfactory when relying solely on the K value to estimate the height difference between the envelope at the top of the forest and the ground; the best result was obtained when K = 0.4, but the corresponding R2 value was only 0.13, and the RMSE was 15.23 m. In our proposed method, the K value is determined as 0.3 by excluding the extreme values of the inversion result in the initial step—the corresponding R2 and RMSE values were 0.59 and 10.73 m, respectively, representing an RMSE decrease of 29.54% relative to the initial K value. After two steps of correction overestimation, the inversion accuracy was significantly improved with an R2 value of 0.65 and an RMSE of 9.69 m, corresponding to an RMSE decrease of 36.38%. Overall, the findings of the study represent an important reference for optimizing future spaceborne TomoSAR forest canopy height estimates.

Employing Multiple Low-Dose PET Images (at Different Dose Levels) as Prior Knowledge to Predict Standard-Dose PET Images.

The existing deep learning-based denoising methods predicting standard-dose PET images (S-PET) from the low-dose versions (L-PET) solely rely on a single-dose level of PET images as the input of deep learning network. In this work, we exploited the prior knowledge in the form of multiple low-dose levels of PET images to estimate the S-PET images. To this end, a high-resolution ResNet architecture was utilized to predict S-PET images from 6 to 4% L-PET images. For the 6% L-PET imaging, two models were developed; the first and second models were trained using a single input of 6% L-PET and three inputs of 6%, 4%, and 2% L-PET as input to predict S-PET images, respectively. Similarly, for 4% L-PET imaging, a model was trained using a single input of 4% low-dose data, and a three-channel model was developed getting 4%, 3%, and 2% L-PET images. The performance of the four models was evaluated using structural similarity index (SSI), peak signal-to-noise ratio (PSNR), and root mean square error (RMSE) within the entire head regions and malignant lesions. The 4% multi-input model led to improved SSI and PSNR and a significant decrease in RMSE by 22.22% and 25.42% within the entire head region and malignant lesions, respectively. Furthermore, the 4% multi-input network remarkably decreased the lesions' SUVmean bias and SUVmax bias by 64.58% and 37.12% comparing to single-input network. In addition, the 6% multi-input network decreased the RMSE within the entire head region, within the lesions, lesions' SUVmean bias, and SUVmax bias by 37.5%, 39.58%, 86.99%, and 45.60%, respectively. This study demonstrated the significant benefits of using prior knowledge in the form of multiple L-PET images to predict S-PET images.

Achieving clinically optimal balance between accuracy and simplicity of a formula for manual use: Development of a simple formula for estimating liver graft weight with donor anthropometrics.

In developing a formula for manual use in clinical settings, simplicity is as important as accuracy. Whole-liver (WL) mass is often estimated using demographic and anthropometric information to calculate the standard liver volume or recommended graft volume in liver transplantation. Multiple formulas for estimating WL mass have been reported, including those with multiple independent variables. However, it is unknown whether multivariable models lead to clinically meaningful improvements in accuracy over univariable models. Our goal was to quantitatively define clinically meaningful improvements in accuracy, which justifies an additional independent variable, and to identify an estimation formula for WL graft weight that best balances accuracy and simplicity given the criterion. From the Japanese Liver Transplantation Society registry, which contains data on all liver transplant cases in Japan, 129 WL donor-graft pairs were extracted. Among the candidate models, those with the smallest cross-validation (CV) root-mean-square error (RMSE) were selected, penalizing model complexity by requiring more complex models to yield a ≥5% decrease in CV RMSE. The winning model by voting with random subsets was fitted to the entire dataset to obtain the final formula. External validity was assessed using CV. A simple univariable linear regression formula using body weight (BW) was obtained as follows: WL graft weight [g] = 14.8 × BW [kg] + 439.2. The CV RMSE (g) and coefficient of determination (R2) were 195.2 and 0.548, respectively. In summary, in the development of a simple formula for manually estimating WL weight using demographic and anthropometric variables, a clinically acceptable trade-off between accuracy and simplicity was quantitatively defined, and the best model was selected using this criterion. A univariable linear model using BW achieved a clinically optimal balance between simplicity and accuracy, while one using body surface area performed similarly.

Objectivity verification experiment of the dynamic system response curve method for streamflow simulation

Error correction methods are widely used to improve the performance of streamflow modeling. The Dynamic system response curve (DSRC) is a newly developed error correction method that utilizes error feedback correction to obtain updated variables and thus enhances model accuracy. While the DSRC method is simple and practical, its objectivity has not been systematically demonstrated. To address this issue, the paper proposes an experiment to verify its objectivity. First, three hydrological models (Xinanjiang (XAJ), Hydrologiska Byråns Vattenavdelning (HBV) and vertically hybrid yielding (VHY)) were calibrated and three corrected areal mean rainfall series were obtained by using the DSRC-based models (XAJ-DSRC, HBV-DSRC and VHY-DSRC). The corrected rainfall series from one hydrological model was then used as input to other hydrological models, and its performance was compared to those of using the original rainfall series by five performance measures (Nash-Sutcliffe efficiency coefficient (NSE), root mean squared error (RMSE), percent bias (PBIAS), mean absolute error (MAE) and correlation coefficient (R)). Finally, this objectivity verification experiment was applied to different model combinations for further demonstration. Case studies of the Sunshui River and Shiquan catchments in China showed that when the DSRC-corrected rainfall series from either hydrological model was applied to the original or other hydrological models, an increase in NSE and R values and a decrease in RMSE and MAE values were observed. The absolute values of PBIAS were within 10%. Accordingly, the hypothesis that the DSRC method is objective is not rejected based on the results of the experiment.

Spectral method for macro and micronutrient prediction in soybean leaves using interval partial least squares regression

Studies on the use of hyperspectral remote sensing for nutrient prediction in soybean (Glycine max L.) leaves are limited, especially regarding the selection of wavelengths. Therefore, the objective of this study was to estimate macro and micronutrients in soybean leaves by employing reflectance data modeled by Partial Least Squares Regression (PLSR) from bands selected by the iPLS (Interval PLS), method with a script developed for MATLAB R2018a. The experimental area was treated with different doses of limestone, with and without incorporation, also with phosphogypsum application in additional treatments. The soybean crops of 2018/2019 and 2019/2020 had the nutritional status evaluated by a random collection of the third fully developed trefoil in the R2 (full flowering) and R5.5 (76–100% grain filling) reproductive phenological stages. The leaf samples were submitted to conventional laboratory analyses. The Ca, Mg, K, P, S, Cu, Fe, Mn, and Zn elements in the leaves were quantified through spectrometry. A field spectroradiometer performed spectral data acquisition in the visible-near infrared, short-wave infrared spectral range of soybean leaves. The spectral curves were subjected to iPLS, and PLSR models were fitted. After iPLS selection, the coefficient of determination (R2) values of PLSR models were on average 15.3% and 16.6% higher in calibration and validation, respectively, compared to classical methods. The PLSR model for Mn, which obtained R2 above 0.90 in the calibration and validation phases, stood out. The Root Mean Square Error (RMSE) decreased, on average, more than 15.0%, both in calibration and validation, after applying the iPLS selection. The models for Ca and Mn stood out with over a 30.0% decrease in RMSE, in calibration and validation. The iPLS selection was efficient in improving the performance and accuracy of the PLSR models, increasing the R2 and decreasing the RMSE, opening new possibilities for the analysis and prediction of macro and micronutrients in plants.

Estimating high-resolution PM2.5 concentration in the Sichuan Basin using a random forest model with data-driven spatial autocorrelation terms

The Sichuan Basin (SCB) is severely polluted by fine particulate matter (PM2.5). Accurate PM2.5 concentration is important for pollution control and epidemiological studies. Evidence indicates that the distribution of PM2.5 is spatially clustered. Additionally, the high local variation in PM2.5 in densely populated areas indicates the necessity of high-resolution PM2.5 estimation. However, spatial clustering and local variation are not considered in current studies in the SCB, which may limit the prediction accuracy of PM2.5 estimation. In this study, we estimated the PM2.5 concentration at 0.01° (approximately 1 km) resolution using a random forest model with data-driven spatial autocorrelation terms (DDW-RF) considering both the first-law-of-geography-based similarity and spatial clustering of PM2.5. The repeated 10-fold cross-validations revealed that compared to the traditional RF model, the optimal model had an 18.31% decrease in the root mean square error (RMSE) and a 4.68% increase in the coefficient of determination (R2). The distribution of PM2.5 revealed another heavily polluted area in the northeastern SCB, including Nanchong and Dazhou besides the two commonly known heavily PM2.5 polluted areas in the western and southern SCB. Then, we built a downscaled model in the megacity Chengdu, which estimated PM2.5 at 0.001° resolution with a 0.156 μg/m3 (1.72%) decrease in RMSE compared to those of 0.01° estimations. The accurate and high-resolution PM2.5 estimates generated by DDW-RF and downscaled models in this study could be beneficial for accurate health effect estimation not only in the whole SCB but also in the city areas with high variable concentrations.