scholarly journals Evaluation of Evaporation from Water Reservoirs in Local Conditions at Czech Republic

Hydrology ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 153
Author(s):  
Eva Melišová ◽  
Adam Vizina ◽  
Martin Hanel ◽  
Petr Pavlík ◽  
Petra Šuhájková
Keyword(s):  
Czech Republic ◽  
Random Forest ◽  
Linear Regression ◽  
Statistical Methods ◽  
Stepwise Regression ◽  
The Other ◽  
Water Reservoirs ◽  
Random Forest Regression ◽  
Local Conditions ◽  
The Difference

Evaporation is an important factor in the overall hydrological balance. It is usually derived as the difference between runoff, precipitation and the change in water storage in a catchment. The magnitude of actual evaporation is determined by the quantity of available water and heavily influenced by climatic and meteorological factors. Currently, there are statistical methods such as linear regression, random forest regression or machine learning methods to calculate evaporation. However, in order to derive these relationships, it is necessary to have observations of evaporation from evaporation stations. In the present study, the statistical methods of linear regression and random forest regression were used to calculate evaporation, with part of the models being designed manually and the other part using stepwise regression. Observed data from 24 evaporation stations and ERA5-Land climate reanalysis data were used to create the regression models. The proposed regression formulas were tested on 33 water reservoirs. The results show that manual regression is a more appropriate method for calculating evaporation than stepwise regression, with the caveat that it is more time consuming. The difference between linear and random forest regression is the variance of the data; random forest regression is better able to fit the observed data. On the other hand, the interpretation of the result for linear regression is simpler. The study introduced that the use of reanalyzed data, ERA5-Land products using the random forest regression method is suitable for the calculation of evaporation from water reservoirs in the conditions of the Czech Republic.

scholarly journals URGENSI PENETAPAN KRITERIA FAKIR MISKIN BAGI PENYALURAN ZAKAT DI INDONESIA

2018 ◽  
Vol 4 (1) ◽  
pp. 167
Author(s):  
Kuntarno Noor Aflah
Keyword(s):  
Comparative Research ◽  
Basic Needs ◽  
Point Of View ◽  
The Other ◽  
Local Conditions ◽  
Other Hand ◽  
The Difference ◽  
The Government ◽  
Literacy Research

Poverty has many definitions, parameters, and standards. From the viewpoint of Islam, many theologians define and measure poverty by various terms and sizes. The difference among theologians’ opinion is caused by poverty terms contained in the Qur’an and Hadits. “Fakir” and “poor” have many meanings. It allows a wide interpretation of the verse and word from theologians. It is also seen from the regulation point in Indonesia, there are many definitions, standards and parameters of poverty. The difference of point of view on determination of poverty criteria and regulations according to Islam in Indonesia shows that the ways of ijtihad by theologians and the government elements is very open. The absence of standard stipulation held, encouraging the writer to conduct a comparative research in this paper; through literacy research. Syafi’i sect does not specify a quantitative standard for poverty. Poverty is only categorized on requirement. As long as people are not able to cover 50% of their basic needs, they are called as fakir. If people are only able to cover close to 70% of their basic needs then they are categorized as poor. Meanwhile, according to Hanafi sect, the qualitative standards turned to the Syafi’i sect. Poor conditions are more severe than the fakir. Besides,the quantitative standard of poverty is one nisab of zakat or the equivalent of 85 grams of gold. On the other hand, BPS and BKKBN formulate the concept and standard of poverty by economic concepts. Poverty is conceptualized as the inability of someone to meet basic consumption needs of the formulation adapted to local conditions respectively.

scholarly journals Applying Random Forest Model Algorithm to GFR Estimation

2020 ◽  
Author(s):  
Peijia Liu ◽  
Dong Yang ◽  
Shaomin Li ◽  
Yutian Chong ◽  
Wentao Hu ◽  
...  
Keyword(s):  
Random Forest ◽  
Kidney Disease ◽  
Linear Regression ◽  
Regression Model ◽  
Regression Models ◽  
Random Forest Regression ◽  
Variable Model ◽  
Data Set ◽  
Development Data ◽  
Better Than

Abstract Background The utilization of estimating-GFR equations is critical for kidney disease in the clinic. However, the performance of the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation has not improved substantially in the past eight years. Here we hypothesized that random forest regression(RF) method could go beyond revised linear regression, which is used to build the CKD-EPI equationMethods 1732 participants were enrolled in this study totally (1333 in development data set from Tianhe District and 399 in external data set Luogang District). Recursive feature elimination (RFE) is applied to the development data to select important variables and build random forest models. Then same variables were used to develop the estimated GFR equation with linear regression as a comparison. The performances of these equations are measured by bias, 30% accuracy , precision and root mean square error(RMSE).Results Of all the variables, creatinine, cystatin C, weight, body mass index (BMI), age, uric acid(UA), blood urea nitrogen(BUN), hematocrit(HCT) and apolipoprotein B(APOB) were selected by RFE method. The results revealed that the overall performance of random forest regression models ascended the revised regression models based on the same variables. In the 9-variable model, RF model was better than revised linear regression in term of bias, precision ,30%accuracy and RMSE(0.78 vs 2.98, 16.90 vs 23.62, 0.84 vs 0.80, 16.88 vs 18.70, all P<0.01 ). In the 4-variable model, random forest regression model showed an improvement in precision and RMSE compared with revised regression model. (20.82 vs 25.25, P<0.01, 19.08 vs 20.60, P<0.001). Bias and 30%accurancy were preferable, but the results were not statistically significant (0.34 vs 2.07, P=0.10, 0.8 vs 0.78, P=0.19, respectively).Conclusions The performances of random forest regression models are better than revised linear regression models when it comes to GFR estimation.

scholarly journals Arbitrage Pricing Model Based on Factor Analysis-Random Forest Regression and its application

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Common Factor ◽  
Linear Regression Method ◽  
National Bureau ◽  
Pricing Model ◽  
Arbitrage Pricing ◽  
Random Forest Regression ◽  
The Common ◽  
Factor Variable

Firstly, this paper establishes K-factor linear model and arbitrage pricing model (ATP) according to ‘the Asset Pricing Model-Arbitrage Pricing Theory’, Then from 2001 to 2017, the Statistical Yearbook of the National Bureau of Statistics collected 10 factors as the original factors such as gross national product, gross industrial product and gross tertiary industry product. After synthesis and simplification, three common factors are extracted to replace ten original factors.The first common factor variable is used to reflect the overall economic level of the country;The second common factor variable reflects a country's inflation rate;The third public factor variable reflects the total annual net export trade situation of the country. After the common factor is determined, the value of the common factor is calculated from the original data.Collect the annual return of 10 stocks for 17 years and do twice random forest regression,we get the arbitrage pricing model. Then, based on the same common factor data, another arbitrage pricing model is obtained by imitating the linear regression method of previous similar papers. By comparing the pricing error, we can find the pricing effect of the model obtained by random forest regression is better than that of the model obtained by linear regression.

scholarly journals A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat

2019 ◽  
Vol 11 (8) ◽  
pp. 920 ◽  
Author(s):  
Syed Haleem Shah ◽  
Yoseline Angel ◽  
Rasmus Houborg ◽  
Shawkat Ali ◽  
Matthew F. McCabe
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Vegetation Indices ◽  
Learning Approach ◽  
Random Forest Regression ◽  
Leaf Chlorophyll ◽  
Machine Learning Approach ◽  
Input Variables ◽  
Non Destructive

Developing rapid and non-destructive methods for chlorophyll estimation over large spatial areas is a topic of much interest, as it would provide an indirect measure of plant photosynthetic response, be useful in monitoring soil nitrogen content, and offer the capacity to assess vegetation structural and functional dynamics. Traditional methods of direct tissue analysis or the use of handheld meters, are not able to capture chlorophyll variability at anything beyond point scales, so are not particularly useful for informing decisions on plant health and status at the field scale. Examining the spectral response of plants via remote sensing has shown much promise as a means to capture variations in vegetation properties, while offering a non-destructive and scalable approach to monitoring. However, determining the optimum combination of spectra or spectral indices to inform plant response remains an active area of investigation. Here, we explore the use of a machine learning approach to enhance the estimation of leaf chlorophyll (Chlt), defined as the sum of chlorophyll a and b, from spectral reflectance data. Using an ASD FieldSpec 4 Hi-Res spectroradiometer, 2700 individual leaf hyperspectral reflectance measurements were acquired from wheat plants grown across a gradient of soil salinity and nutrient levels in a greenhouse experiment. The extractable Chlt was determined from laboratory analysis of 270 collocated samples, each composed of three leaf discs. A random forest regression algorithm was trained against these data, with input predictors based upon (1) reflectance values from 2102 bands across the 400–2500 nm spectral range; and (2) 45 established vegetation indices. As a benchmark, a standard univariate regression analysis was performed to model the relationship between measured Chlt and the selected vegetation indices. Results show that the root mean square error (RMSE) was significantly reduced when using the machine learning approach compared to standard linear regression. When exploiting the entire spectral range of individual bands as input variables, the random forest estimated Chlt with an RMSE of 5.49 µg·cm−2 and an R2 of 0.89. Model accuracy was improved when using vegetation indices as input variables, producing an RMSE ranging from 3.62 to 3.91 µg·cm−2, depending on the particular combination of indices selected. In further analysis, input predictors were ranked according to their importance level, and a step-wise reduction in the number of input features (from 45 down to 7) was performed. Implementing this resulted in no significant effect on the RMSE, and showed that much the same prediction accuracy could be obtained by a smaller subset of indices. Importantly, the random forest regression approach identified many important variables that were not good predictors according to their linear regression statistics. Overall, the research illustrates the promise in using established vegetation indices as input variables in a machine learning approach for the enhanced estimation of Chlt from hyperspectral data.

Comparison Of Mean Platelet Survival Time In A Non-Radioisotope Method Calculatend With New Model With Mean Survival Time In CR-51 Methods

1981 ◽  
Author(s):  
T Tsukada ◽  
T Tango
Keyword(s):  
Linear Regression ◽  
Survival Time ◽  
The Other ◽  
Gamma Model ◽  
Radioisotope Method ◽  
Mean Survival Time ◽  
Normal Platelet ◽  
Other Hand ◽  
Platelet Survival ◽  
The Difference

Platelet survival time using non-radioisotope method measuring malondialdehyde(MDA) generation stimulated by arachidonate prior to and after intake of aspirin(ASA) and Cr-51 method was measured simultaneously in 18 cases with ITP. In cases with normal platelet survival time MDA generation curve showed linear or the letter-S pattern. On the other hand, all cases with shorter survival time had MDA generation curve with letter-S pattern. To describe the S pattern of MDA generation curve following model was devised on the assumption : 1) ASA inhibits MDA generation in megakaryocytes so that newly produced platelets show impaired MDA generation, 2) ability of MDA generation in platelets exposed to ASA recovers during circulating.where a is the disappearance time of inhibiting effect of ASA on megakaryocyte MDA generation and b is the time necessary for recovery of MDA generation in platelets exposed to ASA. Mean survival time(MST) is calculated by l/λ.The difference of MST between non-radioisotope method and Cr-51 method( gamma model) in 11 cases with MST of less than 4 days in Cr-51 method were 2.9 ± 0.4 days in platelet survival time (PST) at which MDA generation attained to pre-ASA levels, 2.7 ± 0.4 days in MST calculated with linear regression of MDA generation curve and 1.8 ± 0.4(SEM) days in MST calculated by the above model. On the other hand, in cases with MST of more than 4 days in Cr-51 method the difference of MST in both methods were 1.4 ± 0.3(SEM) days in PST, 0.6 ± 0.2 days in linear regression and 0.6 ± 0.2 days when MST was calculated with the above formula.These results suggest that the model reported is so far the better fitting model for the MDA generation curve in cases with normal and shortened platelet survival.

Forecasting primary delay recovery of high-speed railway using multiple linear regression, supporting vector machine, artificial neural network, and random forest regression

2019 ◽  
Vol 46 (5) ◽  
pp. 353-363 ◽  
Author(s):  
Chaozhe Jiang ◽  
Ping Huang ◽  
Javad Lessan ◽  
Liping Fu ◽  
Chao Wen
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Prediction Accuracy ◽  
High Speed ◽  
Support Vector ◽  
Random Forest Regression ◽  
High Speed Railway ◽  
Buffer Time ◽  
Artificial Neural

Accurate prediction of recoverable train delay can support the train dispatchers’ decision-making with timetable rescheduling and improving service reliability. In this paper, we present the results of an effort aimed to develop primary delay recovery (PDR) predictor model using train operation records from Wuhan-Guangzhou (W-G) high-speed railway. To this end, we first identified the main variables that contribute to delay, including dwell buffer time, running buffer time, magnitude of primary delay time, and individual sections’ influence. Different models are applied and calibrated to predict the PDR. The validation results on test datasets indicate that the random forest regression (RFR) model outperforms the other three alternative models, namely, multiple linear regression (MLR), support vector machine (SVM), and artificial neural networks (ANN) regarding prediction accuracy measure. Specifically, the evaluation results show that when the prediction tolerance is less than 1 min, the RFR model can achieve up to 80.4% of prediction accuracy, while the accuracy level is 44.4%, 78.5%, and 78.5% for MLR, SVM, and ANN models, respectively.

scholarly journals Development of Predictive Model for Radon-222 Estimation in the Atmosphere using Stepwise Regression and Grid Search Based-Random Forest Regression

2021 ◽  
pp. 132-139
Author(s):  
Omodele Olubi ◽  
Ebeneze Oniya ◽  
Taoreed Owolabi
Keyword(s):  
Random Forest ◽  
Stepwise Regression ◽  
Meteorological Parameters ◽  
Activity Concentration ◽  
Absolute Humidity ◽  
Grid Search ◽  
Random Forest Regression ◽  
Significant Interest ◽  
Polar Research Institute ◽  
Field Measuring

This work develops predictive models for estimating radon (222Rn) activity concentration in the atmosphere using novel grid search based random forest regression (GS-RFR) and stepwise regression (SWR). The developed models employ meteorological parameters which include the temperature, pressure, relative and absolute humidity, wind speed and wind direction as descriptors.  Experimental data of radon concentration and meteorological parameters from two observatories of the Korea Polar Research Institute in Antarctica (King Sejong and Jang Bogo) have been employed in this work.  The performance of the developed models was assessed using three different performance measuring parameters. On the basis of root mean square error (RMSE), the GS-RFR shows better performance over the SWR. An improvement of 64.09 % and 15.19 % was obtained on the training and test datasets, respectively at King Sejong station. At the Jang Bogo station, an improvement of 75.04 % and 28.04 % was obtained on the training and test datasets, respectively. The precision and robustness of the developed models would be of significant interest in determining the concentration of radon (222Rn) activity concentration in the atmosphere for various physical applications especially in regions where field measuring equipment for radon is not available or measurements have been interrupted.

scholarly journals Practical Dynamical-Statistical Reconstruction of Ocean’s Interior from Satellite Observations

2021 ◽  
Vol 13 (24) ◽  
pp. 5085
Author(s):  
Hengqian Yan ◽  
Ren Zhang ◽  
Huizan Wang ◽  
Senliang Bao ◽  
Chengzu Bai
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Satellite Data ◽  
Satellite Observations ◽  
The Other ◽  
Practical Application ◽  
Robust Performance ◽  
Statistical Reconstruction ◽  
Argo Profiles ◽  
First Time

The algorithms based on Surface Quasi-Geostrophic (SQG) dynamics have been developed and validated by many researchers through model products, however it is still doubtful whether these SQG-based algorithms are worth using in terms of observed data. This paper analyzes the factors impeding the practical application of SQG and makes amends by a simple “first-guess (FG) framework”. The proposed framework includes the correction of satellite salinity and the estimation of the FG background, making the SQG-based algorithms applicable in realistic circumstances. The dynamical-statistical method SQG-mEOF-R is thereafter applied to satellite data for the first time. The results are compared with two dynamical algorithms, SQG and isQG, and three empirical algorithms, multivariate linear regression (MLR), random forest (RF), and mEOF-R. The validation against Argo profiles showed that the SQG-mEOF-R presents a robust performance in mesoscale reconstruction and outperforms the other five algorithms in the upper layers. It is promising that the SQG-mEOF-R and the FG framework are applicable to operational reconstruction.

scholarly journals Applying Random Forest Model Algorithm to GFR estimation

2020 ◽  
Author(s):  
Peijia Liu ◽  
Dong Yang ◽  
Shaomin Li ◽  
Yutian Chong ◽  
Ming Li ◽  
...  
Keyword(s):  
Random Forest ◽  
Kidney Disease ◽  
Linear Regression ◽  
Regression Model ◽  
Regression Models ◽  
Random Forest Regression ◽  
Variable Model ◽  
Data Set ◽  
Development Data ◽  
Better Than

Abstract Background The utilization of estimating-GFR equations is critical for kidney disease in the clinic. However, the performance of the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation has not improved substantially in the past eight years. Here we hypothesized that random forest regression(RF) method could go beyond revised linear regression, which is used to build the CKD-EPI equation Methods 1732 participants were enrolled in this study totally (1333 in development data set from Tianhe District and 399 in external data set Luogang District). Recursive feature elimination (RFE) is applied to the development data to select important variables and build random forest models. Then same variables were used to develop the estimated GFR equation with linear regression as a comparison. The performances of these equations are measured by bias, 30% accuracy, precision and root mean square error(RMSE). Results Of all the variables, creatinine, cystatin C, weight, body mass index (BMI), age, uric acid(UA), blood urea nitrogen(BUN), hematocrit(HCT) and apolipoprotein B(APOB) were selected by RFE method. The results revealed that the overall performance of random forest regression models ascended the revised regression models based on the same variables. In the 9-variable model, RF model was better than revised linear regression in term of bias, precision ,30%accuracy and RMSE(0.78 vs 2.98, 16.90 vs 23.62, 0.84 vs 0.80, 16.88 vs 18.70, all P < 0.01 ). In the 4-variable model, random forest regression model showed an improvement in precision and RMSE compared with revised regression model. (20.82 vs 25.25, P < 0.01, 19.08 vs 20.60, P < 0.001). Bias and 30%accurancy were preferable, but the results were not statistically significant (0.34 vs 2.07, P = 0.10, 0.8 vs 0.78, P = 0.19, respectively). Conclusions The performances of random forest regression models are better than revised linear regression models when it comes to GFR estimation.

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