Relative Root Mean Square Error Research Articles

Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction

Precise streamflow estimation plays a key role in optimal water resource use, reservoirs operations, and designing and planning future hydropower projects. Machine learning models were successfully utilized to estimate streamflow in recent years In this study, a new approach, covariance matrix adaptation evolution strategy (CMAES), was utilized to improve the accuracy of seven machine learning models, namely extreme learning machine (ELM), elastic net (EN), Gaussian processes regression (GPR), support vector regression (SVR), least square SVR (LSSVR), extreme gradient boosting (XGB), and radial basis function neural network (RBFNN), in predicting streamflow. The CMAES was used for proper tuning of control parameters of these selected machine learning models. Seven input combinations were decided to estimate streamflow based on previous lagged temperature and streamflow data values. For numerical prediction accuracy comparison of these machine learning models, six statistical indexes are used, i.e., relative root mean squared error (RRMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE), and the Kling–Gupta efficiency agreement index (KGE). In contrast, this study uses scatter plots, radar charts, and Taylor diagrams for graphically predicted accuracy comparison. Results show that SVR provided more accurate results than the other methods, especially for the temperature input cases. In contrast, in some streamflow input cases, the LSSVR and GPR were better than the SVR. The SVR tuned by CMAES with temperature and streamflow inputs produced the least RRMSE (0.266), MAE (263.44), and MAPE (12.44) in streamflow estimation. The EN method was found to be the worst model in streamflow prediction. Uncertainty analysis also endorsed the superiority of the SVR over other machine learning methods by having low uncertainty values. Overall, the SVR model based on either temperature or streamflow as inputs, tuned by CMAES, is highly recommended for streamflow estimation.

Comparison of rainfall generators with regionalisation for the estimation of rainfall erosivity at ungauged sites

Abstract. Rainfall erosivity values are required for soil erosion prediction. To calculate the mean annual rainfall erosivity (R), long-term high-resolution observed rainfall data are required, which are often not available. To overcome the issue of limited data availability in space and time, four methods were employed and evaluated: direct regionalisation of R, regionalisation of 5 min rainfall, disaggregation of daily rainfall into 5 min time steps, and a regionalised stochastic rainfall model. The impact of station density is considered for each of the methods. The study is carried out using 159 recording and 150 non-recording (daily) rainfall stations in and around the federal state of Lower Saxony, Germany. In addition, the minimum record length necessary to adequately estimate R was investigated. Results show that the direct regionalisation of mean annual erosivity is best in terms of both relative bias and relative root mean square error (RMSE), followed by the regionalisation of the 5 min rainfall data, which yields better results than the rainfall generation models, namely an alternating renewal model (ARM) and a multiplicative cascade model. However, a key advantage of using regionalised rainfall models is the ability to generate time series that can be used for the estimation of the erosive event characteristics. This is not possible if regionalising only R. Using the stochastic ARM, it was assessed that more than 60 years of data are needed in most cases to reach a stable estimate of annual rainfall erosivity. Moreover, the temporal resolution of measuring devices was found to have a significant effect on R, with coarser temporal resolution leading to a higher relative bias.

Estimating aboveground biomass changes in a human-modified tropical montane forest of Borneo using multi-temporal airborne LiDAR data

Estimating aboveground biomass changes in tropical mountains is difficult due to the small-scale anthropogenic land use activities such as selective logging. This study examined how multi-temporal airborne LiDAR data could estimate AGB changes in Borneo's tropical montane forest. Using airborne LiDAR data acquired in 2012 and 2017, we compared direct and indirect approaches to estimating the AGB changes. The direct method predicts the AGB change directly based on differences in LiDAR variables between the two time points whereas, the indirect method first constructs a model for predicting the AGB for each time point and then estimates the changes. The direct approach produced a model with an adjusted R2 of 0.321 and a relatively high RMSE (6.37 Mg/ha/year; relative RMSE: 134.36%). On the other hand, annual AGB changes derived from the indirect approach had a low RMSE value (1.413 Mg/ha/year; relative RMSE: 29.80%) and were strongly correlated with the field AGB changes (R2 = 0.988). We estimated the AGB changes using the indirect approach to be −7.49 Mg/ha/year for AGB loss and 8.91 Mg/ha/year for AGB gain. We identified land use conversion as the primary driver of AGB changes in the montane forest since the rate of AGB decrease in state-land was higher than in the managed forest. The LiDAR-based approach provides high-resolution estimates of AGB changes by enlarging field plots to more extensive area coverage, facilitating the adoption of incentive-based carbon conservation mechanisms.

Code and Data Repository for Bi-fidelity Surrogate Modelling: Showcasing the Need for New Test Instances

The code implements Kriging and Co-Kriging as surrogate models. That is, the implementation generates a sample of a bi-fidelity function (a high fidelity sample and a low fidelity sample of specified size), and trains the chosen surrogate model, without the added functionality of choosing further samples. Both of these models are described in the appendix given in the appendix folder. Multiple literature test instances are implemented here, as well as a novel instance creation procedure which adds a disturbance to a high-fidelity function to create a low-fidelity function. In this implementation functions from the COCO test suite16 are used as the high fidelity functions. The performance of both Kriging and Co-Kriging is assessed by calculating the Relative Root Mean Squared Error (RRMSE) between the trained model and the high fidelity source.

Using Mathematical Models Based On Unmanned Aerial Vehicle Optical Imagery to Estimate Tree and Stand Characteristics

This study continues the research in a 100-year-old black pine forest, consisting of stands with different structures. A sample was selected, consisting of six sampling plots (30 subsampling plots) with 508 trees for which the diameter (d), the height (h), and the crown diameter (cw) were measured. Models were generated at sample level (general models), as well as at stand level, based on the cw measured in the field. The general models estimated the sample volume at a relative RMSE (RMSE %) of 20.1% and those adapted to the stand structure at an RMSE of 2.8%. At the level of the sampling plots, the general models underestimated the volume of the higher productivity stands by 24% and overestimated the volume of the lower productivity stands by 27%.

Power correlations as complementary tools for generalised analysis and preliminary design of Stirling engines

The use of dimensionless variables facilitates the generalised analysis of Stirling engines and provides tools for preliminary design, validation of simulation codes and feasibility studies of energy conversion systems. This article provides correlations to estimate the dimensionless values of maximum indicated and brake power, as well as their corresponding speeds. The concept of effective pressure allows power correlations to be related to gas circuit models and mechanical efficiency calculations. Previous correlations based on data from eight very different prototypes are revised using the criterion of minimising the relative root mean square errors (RRMSE) and sensitivity analysis to select influential variables. The application of previously established performance models provided additional data from the M102C and ST05G engines for validation purposes. The procedure also allows detecting inconsistent measurements. Data deviations seem acceptable for most operating points of the 10 analysed engines. The errors inherent to the procedure, as well as the number and quality of experimental data, determine the following average values of accuracy indicators: RRMSE = 3.20% for the dimensionless maximum indicated power, RRMSE = 6.35% for the dimensionless speed corresponding to the maximum indicated power, RRMSE = 6.39% for the dimensionless maximum brake power, and RRMSE = 10.93% for the dimensionless speed corresponding to the maximum brake power.

One to multiple mapping dual learning: Learning multiple signals from one mixture

Single channel blind source separation (SCBSS) refers to separating multiple sources from a mixture collected by a single sensor. Existing methods for SCBSS have limited performance in separating multiple sources and generalization. To address these problems, an algorithm is proposed in this paper to separate multiple sources from a mixture by designing a parallel dual generative adversarial network (PDualGAN) that can build the relationship between a mixture and the corresponding multiple sources to achieve one-to-multiple cross-domain mapping. This algorithm can be applied to a variety of mixtures including both instantaneous and convolutive mixtures. In addition, new datasets for single channel source separation are created which include the mixtures and corresponding sources for this study. Experiments were performed on four different datasets including both one-dimensional and two-dimensional signals. Experimental results show that the proposed algorithm outperforms state-of-the-art algorithms, measured with peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), source-to-distortion ratio (SDR), source-to-interferences ratio (SIR), relative root mean squared error (RRMSE) and correlation.

Sequency encoding single pixel spectroscopy based on Hadamard transform.

Single pixel spectroscopy based on Hadamard transform (SPS-HT) has been applied widely because of its capability of wavelength multiplexing and associated advantage in signal-to-noise ratio. In this paper, we propose a sequency encoding single pixel spectroscopy (SESPS) based on two-dimensional (2D) masks for concurrent coding of all Hadamard coefficients instead of one-dimensional (1D) Hadamard masks (only coding one coefficient at a time) widely used in the traditional SPS-HT. Moreover, each Hadamard coefficient is coded along the time dimension with a different sequency value such that the alternating current (AC) measurements of the time-domain signal can be used to reconstruct all Hadamard coefficients simultaneously, which reduces the influence of noise and dramatically speeds up data acquisition. We demonstrate that the SESPS with 32 spectral channels can accelerate spectral measurements from white light sources and fluorescence particles by around 14 times and 70 times, respectively, compared to measurements using a commercial spectrometer when the relative root mean square error (RMSE) is around 3% or smaller. The acceleration factors can be boosted by an extra 4 times when only eight spectral channels are used to achieve a compression ratio of 4:1, in which the relative RMSEs change only marginally. Compared to our previous SPS-HT, this new scheme can increase the speed by three orders of magnitude. This technique is expected to be useful in applications requiring high-speed spectral measurements such as the spectral flow cytometry and on-site medical diagnosis using fluorescence or Raman spectroscopy.

Investigation of Two Prediction Models of Maximum Usable Frequency for HF Communication Based on Oblique- and Vertical-Incidence Sounding Data

As one of the key technologies of HF communication, the maximum usable frequency (MUF) prediction method has been widely discussed. To experimentally confirm the reliability of commonly used MUFs prediction models for high-frequency communication, we have compared maximum observed frequencies (MOFs) and predicted MUFs to assess the accuracy of two typical prediction models. The root-mean-square error (RMSE) and relative RMSE (RRMSE) between oblique sounding MOFs and the predicted MUFs were used to assess the model’s accuracy. The oblique sounding path was from Changchun to Jinyang, and the vertical-sounding ionosonde was located in Beijing, which was approximately the midpoint of the oblique sounding circuit. The statistical analysis results show that: (a) the trend of prediction results from the Lockwood and the Istituto Nazionale di Geofisica e Vulcanologia (INGV) model are in good agreement with the observations: the mean RMSE and RRMSE of the INGV model are less than those of the Lockwood model; (b) in the four different periods (sunrise, daytime, sunset, and nighttime) of the whole day, the maximum difference of RMSE between the Lockwood and INGV model is 0.14 MHz (the INGV performs better than the LWM), with the corresponding differences of RRMSE being 0.31% at sunrise and 0.68% at daytime; (c) in the four seasons of spring, summer, autumn, and winter, the minimum RMSE values of the Lockwood and INGV models are 1.51 MHz and 1.37 MHz, respectively, which are obtained in winter, and the corresponding RRMSEs are 11.47% and 11.79%, respectively; (d) in the high and low solar activity epochs, the mean RMSEs of the Lockwood and INGV models are 1.63 MHz, and 1.54 MHz, with corresponding mean RRMSE values of 11.47% and 11.55%. In conclusion, the INGV model is more suitable for MUF prediction over Beijing and its adjacent mid-latitude regions from the RMSE comparison of the two models.

Above-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data

Implementing a scheduled and reliable estimation of forest characteristics is important for the sustainable management of forests. This study aimed at evaluating the capability of Sentinel-2 satellite data to estimate above-ground biomass (AGB) in coppice forests of Persian oak (Quercus brantii var. persica) located in Western Iran. To estimate the AGB, field data collection was implemented in 80 square plots (40×40 m, area of 1600 m2). Two diameters of the crown were measured and used to calculate the AGB of each tree based on allometric equations. Then, the performance of satellite data in estimating the AGB was evaluated for the area of study using the field-based AGB (dependent variable) as well as the spectral band values, spectrally-derived vegetation indices (independent variables) and four machine learning (ML) algorithms: Multi-Layer Perceptron Artificial Neural Network (MLPNN), k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Regression (SVR). A five-fold cross-validation was used to verify the effectiveness of models. Examination of the Pearson’s correlation coefficient between AGB and the extracted ‎values showed that IPVI and NDVI vegetation indices had the highest correlation with AGB (r = 0.897). The results indicated that the MLPNN algorithm was the best ML option (RMSE = 1.71 t ha-1; MAE = 1.37 t ha-1; relative RMSE = 24.75%; R2 = 0.87) in estimating the AGB, providing new insights on the capability of remotely sensed-based AGB modeling of sparse Mediterranean forest ecosystems in an area with limited number of field sample plots.

Comparative study of advanced computational techniques for estimating the compressive strength of UHPC

The effect of raw materials on the compressive strength of concrete is a complex process, especially in the case of ultra-high-performance concrete (UHPC), where a higher number of inter-dependent parameters are involved in the strength development. In this era of digitalization, advanced machine learning methods are used to predict the material's mechanical characteristics because of their superior performance compared to conventional and nonlinear statistical regression models. Thus, the goal of the current study is to estimate the compressive strength of UHPC from the designed raw materials using advanced machine learning techniques. The compressive strength of UHPC is predicted from the 14 input parameters, i.e., cement, fly ash, slag, silica fume, nano-silica, limestone powder, sand, coarse aggregate, quartz powder, water, superplasticizer, PE fiber, steel fiber, and curing time. A total of eight machine learning models were compared that include multi-layer perceptron neural network (MLPNN), MLPNN Bootstrap aggregating (MLPNN-BA), MLPNN adaptive boosting (MLPNN-AB), Gradient boosting (GB), Decision tree (DT), DT Bootstrap aggregating (DT-BA), DT adaptive boosting (DT-AB) and Random Forest (RNF). The validation and performance evaluation of the above models were checked by using K-fold cross-validation, mean absolute error (MAE), root mean square error (RSME), coefficient of determination (R2), relative root mean square error (RRMSE), performance index (PI), and Nash Sutcliffe efficiency (NSE). The optimal model was selected based on the results of all statistical checks. It was found the ensembled machine learning models especially decision tree-based models outperform the neural network-based models with higher accuracy and low error. Thus, the recommended machine learning model is random forest having superior prediction capacity followed by DT Bootstrap aggregating and DT adaptive boosting.

The electricity consumption forecast: Adopting a hybrid approach by deep learning and ARIMAX-GARCH models

Modeling and forecasting electricity consumption (EC) help industry managers make better strategic decisions. In this study, a hybrid approach for predicting EC is proposed which first EC is decomposed into approximate and detail parts based on wavelet transfer (WT). Next, the modeling of wavelet components is accomplished based on the adaptive WT (AWT)-long short-term memory (LSTM) and autoregressive integrated moving average with explanatory variable (ARIMAX)-generalized autoregressive conditional heteroscedasticity (GARCH) type models. The key concept of AWT–LSTM is adding an optimal set of adaptive layers through which AWT–LSTM can automatically concentrate on various frequency components based on the dynamic evolution of the input sequence revealing the multi-frequency pattern of EC. Then, the components are predicted separately and eventually, the results are combined nonlinearly through random forest (RF) algorithm. Besides the lag calendar setting, the affecting exogenous variables are applied on the electricity consumption in the structure of hybrid models which include humidity, water precipitation, solar radiation, effective temperature, wind direction, and wind speed. To measure the predictive power, the proposed models were tested using a 24 h ahead forecasting horizon using a sample rate of 30 min in two different seasons (winter and summer). The empirical results indicate the hybrid model of AWT–LSTM-ARIMAX-exponential (EGARCH) in mean-RF with root mean square error (RMSE) = 1.536, mean absolute percentage error (MAPE) = 1.276, normalized mean absolute error (NMAE) = 0.0116, normalized RMSE (NRMSE) = 0.0143, relative RMSE (RRMSE) = 0.1906, Theil’s inequality coefficient (TIC) = 0.01025, sum of squared errors (SSE) = 113.2 values in summer test and RMSE = 1.249, MAPE = 0.869, NMAE = 0.0078, NRMSE = 0.0092, RRMSE = 0.1223, TIC = 0.00533, SSE = 74.9 values in winter test outperforms the benchmark models (WT-feedforward neural network (FFNN)-RF, WT-LSTM-RF, WT-exponential smoothing (ETS)-RF, and WT-seasonal ARIMA with explanatory variable (SARIMAX)-GARCH types models) and alternative filtering methods (Curvelet, Ridgelet, and Contourlet). The results suggest that employing the WT for the decomposition of data and RF for the nonlinear aggregation of sub-signals (predicted values of detail and approximate parts) can significantly improve the prediction power of predictive models.

Estimating Leaf Chlorophyll Content of Moso Bamboo Based on Unmanned Aerial Vehicle Visible Images

Leaf chlorophyll content is an important indicator of the physiological and ecological functions of plants. Accurate estimation of leaf chlorophyll content is necessary to understand energy, carbon, and water exchange between plants and the atmosphere. The leaf chlorophyll content index (CCI) of 109 Moso bamboo samples (19 for training data, 19 for validation data, and 71 for extrapolation data) was measured from December 2019 to May 2021, while their corresponding red–green–blue (RGB) images were acquired using an unmanned aerial vehicle (UAV) platform. A method for estimating leaf CCI based on constructing relationships between field leaf CCI measurements and UAV RGB images was evaluated. The results showed that a modified excess blue minus excess red index and 1.4 × H-S in the hue–saturation–value (HSV) color space were the most suitable variables for estimating the leaf CCI of Moso bamboo. No noticeable difference in accuracy between the linear regression model and backpropagation neural network (BPNN) model was found. Both models performed well in estimating leaf CCI, with an R2 > 0.85 and relative root mean square error (RMSEr) < 15.0% for the validation data. Both models failed to accurately estimate leaf CCI during the leaf-changing period (April to May in off-year), with the problems being overestimation in low leaf CCI and underestimation in high leaf CCI values. At a flight height of 120 m and illumination between 369 and 546 W/m2, the CCI for an independent sample dataset was accurately estimated by the models, with an R2 of 0.83 and RMSEr of 13.78%. Flight height and solar intensity played a role in increasing the generality of the models. This study provides a feasible and straightforward method to estimate the leaf CCI of Moso bamboo based on UAV RGB images.

Inversion of Coniferous Forest Stock Volume Based on Backscatter and InSAR Coherence Factors of Sentinel-1 Hyper-Temporal Images and Spectral Variables of Landsat 8 OLI

Forest stock volume (FSV) is a basic data source for estimating forest carbon sink. It is also a crucial parameter that reflects the quality of forest resources and forest management level. The use of remote sensing data combined with a support vector regression (SVR) algorithm has been widely used in FSV estimation. However, due to the complexity and spatial heterogeneity of the forest biological community, in the FSV high-value area with dense vegetation, the optical re-mote sensing variables tend to be saturated, and the sensitivity of synthetic aperture radar (SAR) backscattering features to the FSV is significantly reduced. These factors seriously affect the ac-curacy of the FSV estimation. In this study, Landsat 8 (L8) Operational Land Imager multispectral images and C-band Sentinel-1 (S1) hyper-temporal SAR images were used to extract three re-mote sensing feature datasets: spectral variables (L8), backscattering coefficients (S1), and inter-ferometric SAR factors (S1-InSAR). We proposed a feature selection method based on SVR (FS-SVR) and compared the FSV estimation performance of FS-SVR and stepwise regression analysis (SRA) on the aforementioned three remote sensing feature datasets. Finally, an estima-tion model of coniferous FSV was constructed using the SVR algorithm in Wangyedian Forest Farm, Inner Mongolia, China, and the spatial distribution map of coniferous FSV was predicted. The experimental results show the following: (1) The coherence amplitude and DSM data ob-tained based on S1 images contain information relat-ed to forest canopy height, and the hy-per-temporal S1 image data significantly enrich the diversity of S1-InSAR feature factors. There-fore, the S1-InSAR dataset has a better FSV response than remote sensing factors such as the S1 backscattering coefficient and L8 vegetation index, and the corresponding root mean square er-ror (RMSE) and relative RMSE (rRMSE) values reached 47.6 m3/ha and 20.9%, respectively. (2) The integrated dataset can provide full play to the synergy of the L8, S1, and S1-InSAR remote sensing data. Its RMSE and rRMSE values are 44.3 m3/ha and 19.4% respectively. (3) The proposed FS-SVR method can better select remote sensing variables suitable for FSV estimation than SRA. The average value of the rRMSE (23.17%) based on the three datasets was 13.8% lower than that of the SRA method (26.87%). This study provides new insights into forest FSV retrieval based on active and passive multisource remote sensing joint data.

Prediction of the Nitrogen, Phosphorus and Potassium Contents in Grape Leaves at Different Growth Stages Based on UAV Multispectral Remote Sensing

The rapid and accurate acquisition of nitrogen, phosphorus and potassium nutrient contents in grape leaves is critical for improving grape yields and quality and for industrial development. In this study, crop growth was non-destructively monitored based on unmanned aerial vehicle (UAV) remote sensing technology. Three irrigation levels (W1, W2 and W3) and four fertilization levels (F3, F2, F1 and F0) were set in this study, and drip irrigation fertilization treatments adopted a complete block design. A correlation analysis was conducted using UAV multispectral image data obtained from 2019 to 2021 and the field-measured leaf nitrogen content (LNC), leaf potassium content (LKC) and leaf phosphorus content (LPC) values; from the results, the vegetation indices (VIs) that were sensitive to LNC, LKC and LPC were determined. By combining spectral indices with partial least squares (PLS), random forest (RF), support vector machine (SVM) and extreme learning machine (ELM) machine-learning algorithms, prediction models were established. Finally, the optimal combinations of spectral variables and machine learning models for predicting LNC, LPC and LKC in each grape growth period were determined. The results showed that: (1) there were high demands for nitrogen during the new shoot growth and flowering periods, potassium was the main nutrient absorbed in the fruit expansion period, and phosphorus was the main nutrient absorbed in the veraison and maturity periods; (2) combining multiple spectral variables with the RF, SVM and ELM models could result in improved LNC, LPC and LKC predictions. The optimal prediction model determination coefficient (R2) derived during the new shoot growth period was above 0.65, and that obtained during the other growth periods was above 0.75. The relative root mean square error (RRMSE) of the above models was below 0.20, and the Willmott consistency index (WIA) was above 0.88. In conclusion, UAV multispectral images have good application effects when predicting nutrient contents in grape leaves. This study can provide technical support for accurate vineyard nutrient management using UAV platforms.

Motion Intention Prediction and Joint Trajectories Generation Toward Lower Limb Prostheses Using EMG and IMU Signals

Powered intelligent lower limb prostheses have been gaining interest as they provide functionality for walking on different terrains. This study proposes a hierarchical planner for intelligent lower limb prostheses based on sensor fusion and a central pattern generator (CPG). Electromyographic (EMG) and inertial measurement unit (IMU) signals were recorded and fused in the feature and decision levels. The high-level planner consists of cascade classifiers with a gait phase dependence. A secondary classifier for each stand phase and swing phase was developed to recognize five gait patterns. The mid-level planner was designed as a walking frequency estimator based on the Newton method. The low-level planner incorporates the CPG models, achieving the planning of lower limb joint trajectories. The proposed layered planner can recognize the users’ walking intention and gait speed in real-time, ensuring coordination between the joints of both legs. Eight healthy subjects were recruited, and the average accuracy of motion pattern recognition reached 99.13% and 99.39% for the standing and swing phases, respectively. The relative root mean square error (RMSE) of the walking frequency estimate was 2.3% under approximately 3.5 m/s gait speed. The promising results indicate that this method effectively predicts the continuous joint angle for lower limb prostheses.

Regional thermal analysis approach: A management tool for predicting water temperature metrics relevant for thermal fish habitat

The field of water resource management, including fisheries, is facing new challenges associated with climate change. This study sheds light on the modeling of water temperature indices (metrics) that describe critical thermal maxima of the Atlantic salmon (salmo salar). These thermal metrics include MaxWaterTmax (interannual mean of maximum summer temperature), MaxNumDay (interannual mean of the number of consecutive days with maximum water temperature > 25 °C and minimum water temperature > 20 °C). The latter is an important indicator to evaluate thermal variability. Three other parameters of a Gaussian function fitted to the interannual daily mean temperatures characterizing the thermal regime of 146 stations located in Eastern Canada were estimated. These three parameters are Gaussian_a (maximum of interannual daily mean temperature), Gaussian_b (mean duration of the warm period), and Gaussian_c (date of occurrence of the interannual maximum temperature). The classical Multiple linear regression model (MLR) and the non-linear Generalized additive model (GAM) were tested and compared to estimate the five thermal metrics. The regression-based approaches involve the identification of thermally homogeneous regions based on three approaches: hierarchical clustering analysis (HCA), regions of influence (ROI) as well as canonical correlation analysis (CCA). Then, the regional MLR and GAM models were applied within the delineated homogenous regions. Also, the regional models were compared to models encompassing all stations (i.e., one region). For each regional estimation model and each thermal metric, a set of optimal explanatory variables were selected using a forward stepwise procedure. The database consisted of 22 environmental predictors related to physiography, topography, climate, land cover and surface deposits. To assess performance of the models, the following statistical metrics were used: coefficient of determination R2, root mean square error (RMSE), bias, relative root mean square error (RRMSE) and percent bias (PBias). The results demonstrate that the non-linear GAM model was consistently better than the simpler MLR model for estimating the five thermal metrics. Results also show that the best practice consists in delineating homogeneous regions before applying the regional GAM model. According to all performance criteria, delineation of regions with the HCA approach is considered to be more flexible and to lead to better performances than neighborhood-based approaches (CCA and ROI).

Modeling site index of selected poplar clones using airborne laser scanning data

Accurate growth and yield projection for plantations is critical for evaluating management decisions and anticipating future yields. Development of site index (SI) models is often costly and can be problematic when new, short-rotation species are introduced, for example, hybrid poplar plantations, which are increasingly common due to their very fast growth and high productivity. Airborne laser scanning (ALS) allows accurate measurement of tree and stand height and is increasingly being used to develop top height models. In this paper, we demonstrate an approach to develop SI models from ALS data across hybrid poplar plantations in Quebec, Canada. We exploit a single time step ALS acquisition to generate top height estimates at 10 m grid level. Using existing information on planting date and management practices, we developed top height models for unique classes of fertilization treatment and clone. The generic models for unfertilized and fertilized stands showed good fit statistics, with R2 of 0.71 and 0.82, respectively. Clone-specific models showed similar goodness of fit, with the best model resulting in an R2 of 0.89 and relative root mean square error (RMSE) of 16.6%. Analysis of variance (ANOVA) results showed that clone, fertilization status, and the interaction term between clone and fertilization were significant. Our results confirm the development of top height models from a chronosequence of ALS data was successful and offers a new approach to derive SI models in single-species plantation sites.

Predicting Antibiotic Effect of Vancomycin Using Pharmacokinetic/Pharmacodynamic Modeling and Simulation: Dense Sampling versus Sparse Sampling.

This study aimed to investigate the effect of a structural pharmacokinetic (PK) model with fewer compartments developed following sparse sampling on the PK parameter estimation and the probability of target attainment (PTA) prediction of vancomycin. Two- and three-compartment PK models of vancomycin were used for the virtual concentration–time profile simulation. Datasets with reduced blood sampling times were generated to support a model with a lesser number of compartments. Monte Carlo simulation was conducted to evaluate the PTA. For the two-compartment PK profile, the total clearance (CL) of the reduced one-compartment model showed a relative bias (RBias) and relative root mean square error (RRMSE) over 90%. For the three-compartment PK profile, the CL of the reduced one-compartment model represented the largest RBias and RRMSE, while the steady-state volume of distribution of the reduced two-compartment model represented the largest absolute RBias and RRMSE. A lesser number of compartments corresponded to a lower predicted area under the concentration–time curve of vancomycin. The estimated PK parameters and predicted PK/PD index from models built with sparse sampling designs that cannot support the PK profile can be significantly inaccurate and unprecise. This might lead to the misprediction of the PTA and selection of improper dosage regimens when clinicians prescribe antibiotics.

Mapping crop leaf area index at the parcel level via inverting a radiative transfer model under spatiotemporal constraints: A case study on sugarcane

Crop leaf area index (LAI) mapping from remote sensing observations is highly demanded for regional agricultural applications, such as crop health monitoring and crop yield prediction. However, the popularly used model inversion method inevitably presents an ill-posed problem, which leads to unstable and inaccurate retrieval results. An agricultural parcel is a relatively homogeneous object due to uniform agricultural practices and similar environmental conditions. Crops inside a parcel generally present the same growth stage and similar growth status. In this study, a new method was proposed by adopting spatiotemporal constraints at the parcel level in the model inversion process for LAI retrieval. Firstly, phenology information of parcels was utilized to constrain the LAI ranges based on the established prior knowledge, which described the temporal variation of LAI during the life cycle of crop. Subsequently, spatial constraint was adopted in the model inversion through a proposed novel cost function, which assumed the spatial autocorrelation of parameters inside a parcel according to the first law of geography. Sugarcane was taken as an example to evaluate the proposed method. The method was applied to Sentinel-2 data and validated using ground-measured LAI data. The retrieved model parameters exhibited smoother spatial patterns and lower intra-parcel spatial variations through the proposed parcel-level inversion method, compared to the conventional pixel-level inversion method. Evaluations of LAI retrieval accuracy showed that the parcel-level inversion method yielded more accurate results (root mean square error (RMSE): 0.34 m2/m2; relative root mean square error (RRMSE): 20.89%), compared to the pixel-level inversion method (RMSE: 0.56 m2/m2; RRMSE: 34.53%). The spatiotemporal constraint strategy presented the ability to prevent severe overestimation and underestimation. Among the validation data set, the accuracy of the severely overestimated samples of the pixel-level method (RMSE: 0.93 m2/m2) was improved via the new method (RMSE: 0.43 m2/m2); the accuracy of the severely underestimated samples of the pixel-level method (RMSE: 0.66 m2/m2) was improved via the new method (RMSE: 0.26 m2/m2). Finally, the proposed method was applied to obtain sugarcane LAI mapping. The study demonstrates that the proposed parcel-level spatiotemporal constraint strategy improves the accuracy of LAI retrieval, has the capability of producing a highly reliable crop LAI mapping, and shows good potential for agricultural applications at regional scales.