scholarly journals Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

2020 ◽  
Vol 13 (1) ◽  
pp. 297
Author(s):  
Rana Muhammad Adnan ◽  
Salim Heddam ◽  
Zaher Mundher Yaseen ◽  
Shamsuddin Shahid ◽  
Ozgur Kisi ◽  
...  
Keyword(s):  
Potential Evapotranspiration ◽  
Irrigation Management ◽  
Model Development ◽  
Absolute Error ◽  
Superior Performance ◽  
Average Error ◽  
Group Method ◽  
Data Splitting ◽  
M5 Model Tree ◽  
Data Division

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.

Model development and control of an auto-refrigerated polystyrene polymerization reactor

2021 ◽  
pp. 014233122110251
Author(s):  
Reyhane Mokhtarname ◽  
Ali Akbar Safavi ◽  
Leonhard Urbas ◽  
Fabienne Salimi ◽  
Mohammad M Zerafat ◽  
...  
Keyword(s):  
Temperature Control ◽  
Model Development ◽  
Heat Removal ◽  
Superior Performance ◽  
Polymerization Process ◽  
Vacuum System ◽  
Control Scheme ◽  
Simulation Results ◽  
And Control ◽  
Operating Plant

Dynamic model development and control of an existing operating industrial continuous bulk free radical styrene polymerization process are carried out to evaluate the performance of auto-refrigerated CSTRs (continuous stirred tank reactors). One of the most difficult tasks in polymerization processes is to control the high viscosity reactor contents and heat removal. In this study, temperature control of an auto-refrigerated CSTR is carried out using an alternative control scheme which makes use of a vacuum system connected to the condenser and has not been addressed in the literature (i.e. to the best of our knowledge). The developed model is then verified using some experimental data of the real operating plant. To show the heat removal potential of this control scheme, a common control strategy used in some previous studies is also simulated. Simulation results show a faster dynamics and superior performance of the first control scheme which is already implemented in our operating plant. Besides, a nonlinear model predictive control (NMPC) is developed for the polymerization process under study to provide a better temperature control while satisfying the input/output and the heat exchanger capacity constraints on the heat removal. Then, a comparison has been also made with the conventional proportional-integral (PI) controller utilizing some common tuning rules. Some robustness and stability analyses of the control schemes investigated are also provided through some simulations. Simulation results clearly show the superiority of the NMPC strategy from all aspects.

scholarly journals Evaluation of Mekong River commission operational flood forecasts, 2000–2012

2014 ◽  
Vol 18 (7) ◽  
pp. 2645-2656 ◽  
Author(s):  
T. C. Pagano
Keyword(s):  
Protective Action ◽  
Absolute Error ◽  
Probability Of Detection ◽  
Mekong River ◽  
Average Error ◽  
Wet Season ◽  
Phnom Penh ◽  
Mekong River Commission ◽  
Flood Level ◽  
Skill Scores

Abstract. This study created a 13-year historical archive of operational flood forecasts issued by the Regional Flood Management and Mitigation Center (RFMMC) of the Mekong River Commission. The RFMMC issues 1- to 5-day daily deterministic river height forecasts for 22 locations throughout the wet season (June–October). When these forecasts reach near flood level, government agencies and the public are encouraged to take protective action against damages. When measured by standard skill scores, the forecasts perform exceptionally well (e.g., 1 day-ahead Nash–Sutcliffe > 0.99) although much of this apparent skill is due to the strong seasonal cycle and the narrow natural range of variability at certain locations. Five-day forecasts upstream of Phnom Penh typically have 0.8 m error standard deviation, whereas below Phnom Penh the error is typically 0.3 m. The coefficients of persistence for 1-day forecasts are typically 0.4–0.8 and 5-day forecasts are typically 0.1–0.7. RFMMC uses a series of benchmarks to define a metric of percentage satisfactory forecasts. As the benchmarks were derived based on the average error, certain locations and lead times consistently appear less satisfactory than others. Instead, different benchmarks were proposed and derived based on the 70th percentile of absolute error over the 13-year period. There are no obvious trends in the percentage of satisfactory forecasts from 2002 to 2012, regardless of the benchmark chosen. Finally, when evaluated from a categorical "crossing above/not-crossing above flood level" perspective, the forecasts have a moderate probability of detection (48% at 1 day ahead, 31% at 5 days ahead) and false alarm rate (13% at 1 day ahead, 74% at 5 days ahead).

scholarly journals Mapping Seasonal High-Resolution PM2.5 Concentrations with Spatiotemporal Bagged-Tree Model across China

2021 ◽  
Vol 10 (10) ◽  
pp. 676
Author(s):  
Junchen He ◽  
Zhili Jin ◽  
Wei Wang ◽  
Yixiao Zhang
Keyword(s):  
Remote Sensing ◽  
Mean Squared Error ◽  
Fine Particulate Matter ◽  
Absolute Error ◽  
Superior Performance ◽  
Tree Model ◽  
Mixed Effect ◽  
Proposed Model ◽  
High Concentrations ◽  
Pm2.5 Concentration

High concentrations of fine particulate matter (PM2.5) are well known to reduce environmental quality, visibility, atmospheric radiation, and damage the human respiratory system. Satellite-based aerosol retrievals are widely used to estimate surface PM2.5 levels because satellite remote sensing can break through the spatial limitations caused by sparse observation stations. In this work, a spatiotemporal weighted bagged-tree remote sensing (STBT) model that simultaneously considers the effects of aerosol optical depth, meteorological parameters, and topographic factors was proposed to map PM2.5 concentrations across China that occurred in 2018. The proposed model shows superior performance with the determination coefficient (R2) of 0.84, mean-absolute error (MAE) of 8.77 μg/m3 and root-mean-squared error (RMSE) of 15.14 μg/m3 when compared with the traditional multiple linear regression (R2 = 0.38, MAE = 18.15 μg/m3, RMSE = 29.06 μg/m3) and linear mixed-effect (R2 = 0.52, MAE = 15.43 μg/m3, RMSE = 25.41 μg/m3) models by the 10-fold cross-validation method. The results collectively demonstrate the superiority of the STBT model to other models for PM2.5 concentration monitoring. Thus, this method may provide important data support for atmospheric environmental monitoring and epidemiological research.

scholarly journals Productive characteristics of banana genotypes submitted to different irrigation water depths under Cerrado conditions

2017 ◽  
Vol 38 (4Supl1) ◽  
pp. 2351
Author(s):  
Luciana Borges e Silva ◽  
Jorge Luís do Nascimento ◽  
Ronaldo Veloso Naves ◽  
Juracy Rocha Braga Filho ◽  
Wilian Henrique Diniz Buso ◽  
...  
Keyword(s):  
Water Depth ◽  
Irrigation Water ◽  
Potential Evapotranspiration ◽  
Block Design ◽  
Irrigation Management ◽  
Fruit Production ◽  
Production Cycle ◽  
Fruit Length ◽  
Water Depths ◽  
Fruit Diameter

Irrigation management associated with other banana agricultural practices can provide an increased productivity and improved fruit quality. This study assessed the productive characteristics of banana genotypes under different irrigation water depths. The experiment was conducted at the experimental area of the School of Agronomy (EA/UFG) in Goiânia, GO, Brazil. The experimental design was a split-plot randomized block design, in which four irrigation water depths (30, 65, 100, and 135% of crop potential evapotranspiration, ETpc) composed the plots and three genotypes (‘FHIA 18’, ‘Grande-Naine’, and ‘Prata’) the subplots, with a spacing of 2.5 × 1.6 m. During the experimental period (first production cycle), the total precipitation was 1719.20 mm. Characterization of genotype development and yield was performed with the following assessments: bunch mass (kg), number of hands, stalk mass (kg), fruit diameter of the second hand (mm), fruit length of the second hand (cm), mass of the second hand (kg), number of fruits of the second hand, total number of fruits, and number of damaged fruits. The cultivar ‘FHIA 18’, differently from the others, showed a significant response to irrigation water depths on productivity. In the genotypes ‘Grande-Naine’ and ‘Prata’, an influence of irrigation was observed only on external and visual characteristics of fruit (diameter, length, and number of damaged fruits). In the genotype ‘Prata’, the irrigation water depth of 965 mm allowed fruit production with a larger diameter. Fruit length in the genotype ‘Prata’ increased linearly as water depth increased. The use of irrigation promoted a reduction in the number of damaged fruits in the genotypes ‘FHIA 18’ and ‘Grande-Naine’.

scholarly journals A Device to Measure a Smoker’s Puffing Topography and Real-Time Puff-By-Puff “Tar” Delivery

2014 ◽  
Vol 26 (2) ◽  
Author(s):  
Sandra J. Slayford ◽  
Barrie E. Frost
Keyword(s):  
Real Time ◽  
Light Emitting Diode ◽  
Absolute Error ◽  
Average Error ◽  
Light Emitting ◽  
Light Obscuration ◽  
Test Study ◽  
Added Benefit ◽  
Volume Characteristics ◽  
The Relationship

AbstractA device for measuring the flow, duration and volume characteristics of human puffing behaviour when smoking cigarettes is described. Cigarettes are smoked through a holder comprising a measured pressure drop across a critical orifice. The holder also contains a Light Emitting Diode (LED) and photodetector that measures light obscuration in order to estimate nicotine-free dry particulate matter (NFDPM, “tar”) delivery. All data are recorded on a puff-by-puff basis and displayed in real time. These NFDPM estimates are known as optical “tar” (OT), and are derived from the calibration of the OT measurement versus gravimetric NFDPM yields of cigarettes under a range of smoking regimes. In a test study, puff volumes from 20-80 mL were recorded to ± 6.0% of a pre-set volume, with an absolute error of 4.7 mL for an 80 mL volume drawn on a lit cigarette, and an average error of less than 2.0 mL across the range 20-80 mL. The relationship between NFDPM and OT was linear (R2 = 0.99) and accurate to ± 1.3 mg per cigarette over the range 1-23 mg per cigarette. The device provides an alternative to the widely used part filter methodology for estimating mouth level exposure with an added benefit that no further laboratory smoking replication or analysis is required. When used in conjunction with the part filter methodology, the puffing behaviour recorded can explain anomalies in the data while providing a second independent estimate.

scholarly journals Heave Motion Measurement by Adaptive Filter Based on Longuet-Higgins Wave Model

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Jiazhen Lu ◽  
Qiuwei Luo ◽  
Yanqiang Yang
Keyword(s):  
Adaptive Filter ◽  
Cutoff Frequency ◽  
Wave Model ◽  
Absolute Error ◽  
Average Error ◽  
Measurement Results ◽  
Highpass Filter ◽  
Normal Method ◽  
Heave Motion ◽  
Rough Sea

A method is proposed to obtain heave motion information based on the Longuet-Higgins wave model. The Longuet-Higgins wave model which is closer to the sea wave is introduced. Based on it, random process of the noise is analyzed and the highpass filter is designed to reduce errors. Then it is the key point in this article that an adaptive algorithm is put forward because of the complexity of the waves. The algorithm adjusts the cutoff frequency to reduce the amplitude attenuation of the filter by analyzing the wave. For the same reason the comprehensive parameter of the phase compensation can be also obtained by the algorithm. Simulation measurement results show that under the rough sea situation the maximum value of absolute error is 0.4942 m according to the normal method, the method is 0.1170 m, and the average error ratio of the rough sea test reduces to 3.89% from 12.54%, which demonstrates that the adaptive filter is more effective in measuring heave movement. A variety of simulation cases show that the adaptive filter can also improve the precision of the heave motion under different sea situations.

scholarly journals Evaluation and Bias Correction in WRF Model Forecasting of Precipitation and Potential Evapotranspiration

2019 ◽  
Vol 20 (5) ◽  
pp. 965-983 ◽  
Author(s):  
Theodor Bughici ◽  
Naftali Lazarovitch ◽  
Erick Fredj ◽  
Eran Tas
Keyword(s):  
Bias Correction ◽  
Potential Evapotranspiration ◽  
Irrigation Management ◽  
Hybrid Approach ◽  
Wrf Model ◽  
Irrigation Scheduling ◽  
Weather Research And Forecasting ◽  
Forecast Bias ◽  
Synoptic Conditions ◽  
High Bias

Abstract A reliable forecast of potential evapotranspiration (ET0) is key to precise irrigation scheduling toward reducing water and agrochemical use while optimizing crop yield. In this study, we examine the benefits of using the Weather Research and Forecasting (WRF) Model for ET0 and precipitation forecasts with simulations at a 3-km grid spatial resolution and an hourly temporal resolution output over Israel. The simulated parameters needed to calculate ET0 using the Penman–Monteith (PM) approach, as well as calculated ET0 and precipitation, were compared to observations from a network of meteorological stations. WRF forecasts of all PM meteorological parameters, except wind speed Ws, were significantly sensitive to seasonality and synoptic conditions, whereas forecasts of Ws consistently showed high bias associated with strong local effects, leading to high bias in the evaluated PM–ET0. Local Ws bias correction using observations on days preceding the forecast and interpolation of the resulting PM–ET0 to other locations led to significant improvement in ET0 forecasts over the investigated area. By using this hybrid forecast approach (WRFBC) that combines WRF numerical simulations with statistical bias corrections, daily ET0 forecast bias was reduced from an annual mean of 13% with WRF to 3% with WRFBC, while maintaining a high model–observation correlation. WRF was successful in predicting precipitation events on a daily event basis for all four forecast lead days. Considering the benefit of the hybrid approach for forecasting ET0, the WRF Model was found to be a high-potential tool for improving crop irrigation management.

scholarly journals Deep Learning for Prediction of Diaphragm Activity from the Surface Electromyogram

2019 ◽  
Vol 5 (1) ◽  
pp. 17-20
Author(s):  
Niclas Bockelmann ◽  
Jan Graßhoff ◽  
Lasse Hansen ◽  
Giacomo Bellani ◽  
Mattias P. Heinrich ◽  
...  
Keyword(s):  
Electrical Activity ◽  
Pearson Correlation ◽  
Muscular Activity ◽  
Absolute Error ◽  
Skin Surface ◽  
Learning Task ◽  
Assisted Ventilation ◽  
Superior Performance ◽  
Respiratory Drive ◽  
Semg Signal

AbstractThe electrical activity of the diaphragm (EAdi) is a novel monitoring parameter for patients under assisted ventilation and is used for assessing the patient’s neural respiratory drive. It is recorded by an array of electrodes placed inside the esophagus at the level of the diaphragm. A noninvasive alternative is the measurement of the electromyogram by means of skin surface electrodes (sEMG). The respiratory sEMG signal, however, is subject to electrocardiographic interference and crosstalk from other muscles and may also pick up a different part of the muscular activity. In this work, we propose to use a deep neural network to predict the electrical activity of the diaphragm as measured by a nasogastric catheter from sEMG measurements. We use a ResNet based architecture and train the network to directly regress the EAdi as a supervised learning task - we further investigate a heatmap based regression approach. The proposed methods are evaluated on a clinical dataset consisting of 77 recordings from mechanically ventilated patients. For the direct regression task, the network’s predictions reach a Pearson correlation coefficient (PCC) of 0.818 with EAdi on the hold-out set. The heatmap regression increases the PCC to 0.830 while at the same time achieving a lower mean absolute error, indicating a superior performance. From our results we conclude that sEMG measurements may be used to predict the internal activity of the diaphragm as measured invasively using a nasogastric catheter.

scholarly journals Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature

2014 ◽  
Vol 7 (3) ◽  
pp. 1247-1250 ◽  
Author(s):  
T. Chai ◽  
R. R. Draxler
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Model Evaluation ◽  
Mean Absolute Error ◽  
Evaluation Studies ◽  
Model Performance ◽  
Absolute Error ◽  
Average Error ◽  
Mean Square

Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric, whereas Willmott et al. (2009) indicated that the sums-of-squares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance.

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