Search

Evapotranspiration is a key component in the terrestrial water cycle, and accurate evapotranspiration estimates are critical for water irrigation management. Although many applicable evapotranspiration models have been developed, they are largely focused on low-altitude regions, with less attention given to alpine ecosystems. In this study, we evaluated the performance of fourteen reference evapotranspiration (ET0) models by comparison with large weight lysimeter measurements. Specifically, we used the Bowen ratio energy balance method (BREB), three combination models, seven radiationbased models, and three temperature-based models based on data from June 2017 to December 2018 in a humid alpine meadow in the northeastern Qinghai-Tibetan Plateau. The daily actual evapotranspiration (ETa) data were obtained using large weighing lysimeters located in an alpine Kobresia meadow. We found that the performance of the fourteen ET0 models, ranked on the basis of their root mean square error (RMSE), decreased in the following order: BREB > Priestley-Taylor (PT) > DeBruin-Keijman (DK) > 1963 Penman > FAO-24 Penman > FAO-56 Penman-Monteith > IRMAK1 > Makkink (1957) > Makkink (1967) > Makkink > IRMAK2 > Hargreaves (HAR) > Hargreaves1 (HAR1) > Hargreaves2 (HAR2). For the combination models, the FAO-24 Penman model yielded the highest correlation (0.77), followed by 1963 Penman (0.75) and FAO-56 PM (0.76). For radiation-based models, PT and DK obtained the highest correlation (0.80), followed by Makkink (1967) (0.69), Makkink (1957) (0.69), IRMAK1 (0.66), and IRMAK2 (0.62). For temperature-based models, the HAR model yielded the highest correlation (0.62), HAR1, and HAR2 obtained the same correlation (0.59). Overall, the BREB performed best, with RMSEs of 0.98, followed by combination models (ranging from 1.19 to 1.27mm day-1 and averaging 1.22mm day-1), radiation-based models (ranging from 1.02 to 1.42mm day-1 and averaging 1.27mm day-1), and temperaturebased models (ranging from 1.47 to 1.48mm day-1 and averaging 1.47mm day-1). Furthermore, all models tended to underestimate the measured ETa during periods of high evaporative demand (i.e., growing season) and overestimated measured ETa during low evaporative demand (i.e., nongrowing season). Our results provide new insights into the accurate assessment of evapotranspiration in humid alpine meadows in the northeastern Qinghai-Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Evapotranspiration is a key component in the terrestrial water cycle, and accurate evapotranspiration estimates are critical for water irrigation management. Although many applicable evapotranspiration models have been developed, they are largely focused on low-altitude regions, with less attention given to alpine ecosystems. In this study, we evaluated the performance of fourteen reference evapotranspiration (ET0) models by comparison with large weight lysimeter measurements. Specifically, we used the Bowen ratio energy balance method (BREB), three combination models, seven radiation-based models, and three temperature-based models based on data from June 2017 to December 2018 in a humid alpine meadow in the northeastern Qinghai–Tibetan Plateau. The daily actual evapotranspiration (ETa) data were obtained using large weighing lysimeters located in an alpine Kobresia meadow. We found that the performance of the fourteen ET0 models, ranked on the basis of their root mean square error (RMSE), decreased in the following order: BREB > Priestley-Taylor (PT) > DeBruin-Keijman (DK) > 1963 Penman > FAO-24 Penman > FAO-56 Penman–Monteith > IRMAK1 > Makkink (1957) > Makkink (1967) > Makkink > IRMAK2 > Hargreaves (HAR) > Hargreaves1 (HAR1) > Hargreaves2 (HAR2). For the combination models, the FAO-24 Penman model yielded the highest correlation (0.77), followed by 1963 Penman (0.75) and FAO-56 PM (0.76). For radiation-based models, PT and DK obtained the highest correlation (0.80), followed by Makkink (1967) (0.69), Makkink (1957) (0.69), IRMAK1 (0.66), and IRMAK2 (0.62). For temperature-based models, the HAR model yielded the highest correlation (0.62), HAR1, and HAR2 obtained the same correlation (0.59). Overall, the BREB performed best, with RMSEs of 0.98, followed by combination models (ranging from 1.19 to 1.27 mm day−1 and averaging 1.22 mm day−1), radiation-based models (ranging from 1.02 to 1.42 mm day−1 and averaging 1.27 mm day−1), and temperature-based models (ranging from 1.47 to 1.48 mm day−1 and averaging 1.47 mm day−1). Furthermore, all models tended to underestimate the measured ETa during periods of high evaporative demand (i.e., growing season) and overestimated measured ETa during low evaporative demand (i.e., nongrowing season). Our results provide new insights into the accurate assessment of evapotranspiration in humid alpine meadows in the northeastern Qinghai–Tibetan Plateau. [ABSTRACT FROM AUTHOR]

This study compared the precision of ability estimation on different types of item response theory models for mixed-format data. Participants in this study were 1625 Junior High School Students in Depok, Indonesia. The mixed-format test was used to measure the students' ability in mathematics. The test used consists of multiple-choice and constructed response. Multiple-choice items are scored dichotomously, whereas constructed response items are scored polytomously. Furthermore, the mixed response data were analyzed using combinations of item response theory models. This study used a combination of Multiple-Choice Model for dichotomous data and Graded response model for polytomous data (MCM+GRM). Analysis of this model combination has never been done simultaneously. Test response data were analysed using PARSCALE. Furthermore, the estimation results were compared with the estimation results from a combination of 3 Parameters Logistic Model and Generalized Partial Credit Model (3PLM+GPCM). There are two criteria evaluation for the level of estimation precision: Root Mean Squared Error (RMSE) and correlation method. Based on the results obtained, the estimated RMSE value for the MCM+GRM is smaller than the estimated RMSE value with the 3PLM+GPCM. Also, the results of the estimated ability with MCM+GRM produce higher correlation values than 3PLM+GPCM. So, it can be concluded that the level precision of the MCM+GRM model is higher than 3PLM+GPCM. Therefore, MCM+GRM is more recommended for estimating students' mathematical ability in mixed-format tests. [ABSTRACT FROM AUTHOR]

• The different meta classifier combining with Hoeffding tree and Forest was explored. • Aspect, landuse, and altitude are the most important factor. • FPA-MB model is more stable and outstanding in landslide susceptibility modeling. Assessing and mapping landslide susceptibility is a powerful approach to decrease the cost of landslide disasters. The aim of this paper is to design combination models by combining the Hoeffding tree and forest by penalizing attributes with MultiBoosting (MB), Random SubSpace (RS) and Rotation Forest (RF) to analyse the results of each combination model for modelling landslide susceptibilities. For this purpose, a case study was conducted in Yanshan County, Jiangxi Province, China. Then, 380 landslide polygons and eleven environmental variables were collected and processed, and they were the input data for the six combination models. The results demonstrated that the combined models performed well. Using validation data, the FPA-RF model obtained a maximum AUC value (0.794), followed by the FPA-RS (0.793), FPA-MB (0.788), VFDT-RF (0.746), VFDT-MB (0.741) and VFDT-RS (0.740) models. The FPA-RF model exhibited the most stable and accurate performance in this paper. Among the eleven environmental variables, aspect, land use, and altitude were the most important variables in the combination models. Therefore, the combination models we developed are useful tools that can decrease losses from landslide disasters. [ABSTRACT FROM AUTHOR]

Highlights • Incomplete traffic information hinders an accurate evaluation of CO 2 emissions. • Technologies such as single estimators and combination models can overcome this issue. • TANINO is based on a combination model and a macro-model of CO 2 evaluation. • Along the ring road of Seville, TANINO improves the results of single estimators. • This monitoring allows a consistent evaluation of the efficiency of carbon policies. Abstract Carbon emissions from road transport are one of the main issues related to modern transport planning. To address them adequately, the acquisition of reliable data about traffic flow is an essential prerequisite. However, the large quantity and the heterogeneity of available information often cause problems; missing or incomplete data are one of the most critical aspects. This paper discusses how technology handles imperfect information in order to obtain more accurate quantification of CO 2 emissions. First, an analysis of single estimators and combination models is provided, highlighting their main characteristics. Then, the TANINO model (T ool for the A nalysis of N on-conservative Carbon Emissions I n Tra N sp O rt) is presented, jointly developed at the University of Seville and at the IUAV University of Venice. It consists of two different modules: the first is a combination model that optimizes the results of three traffic flow single estimators, while the second is a macro-model of carbon evaluation, which takes into account road infrastructure, vehicle type and traffic conditions. TANINO is then tested to calculate CO 2 emissions along the ring road of the Spanish city of Seville, showing its more efficient performance, compared to the single estimators normally adopted for such aims. Transport planning can benefit from the adequate knowledge of traffic flows and related CO 2 emissions, since it allows a more reliable monitoring of the progresses granted by specific carbon policies. [ABSTRACT FROM AUTHOR]

Objective: We tested whether bedside testing for H-FABP is, alone or integrated in combination models, useful for rapid risk stratification of non-high-risk PE. Methods: We prospectively studied 136 normotensive patients with confirmed PE. H-FABP was determined using a qualitative bedside-test showing a positive result for plasma concentration >7 ng/ml. Results: Overall, 11 patients (8.1 %) had an adverse 30-day outcome. Of 58 patients (42.6 %) with a positive H-FABP bedside-test, 9 (15.5 %) had an unfavourable course compared to 2 of 78 patients (2.6 %) with a negative test result ( p = 0.009). Logistic regression analysis indicated a sevenfold increased risk for an adverse outcome (95 % CI, 1.45-33.67; p = 0.016) for patients with a positive H-FABP bedside-test. Additive prognostic information were obtained by a novel score including the H-FABP bedside-test (1.5 points), tachycardia (2 points), and syncope (1.5 points) (OR 11.57 [2.38-56.24]; p = 0.002 for ≥3 points). Increasing points were associated with a continuous exponential increase in the rate of an adverse 30-day outcome (0 % for patients with 0 points and 44.4 % for ≥5 points). Notably, this simple score provided similar prognostic value as the combination of the H-FABP bedside-test with echocardiographic signs of right ventricular dysfunction (OR 12.73 [2.51-64.43]; p = 0.002). Conclusions: Bedside testing for H-FABP appears a useful tool for immediate risk stratification of non-high-risk patients with acute PE, who may be at increased risk of an adverse outcome, in particular if integrated in a novel score without the need of echocardiographic examination. [ABSTRACT FROM AUTHOR]