scholarly journals Stream Flow Generation for Simulating Yearly Bed Change at an Ungauged Stream in Monsoon Region

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 554
Author(s):  
Woong Hee Lee ◽  
Heung Sik Choi ◽  
Dongwoo Lee ◽  
Byungwoong Choi
Keyword(s):  
Stream Flow ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Monsoon Region ◽  
Mean Flow ◽  
Bankfull Discharge ◽  
River Bed ◽  
Flow Generation ◽  
Annual Discharge

The stream flow generation method is necessary for predicting yearly bed change at an ungauged stream in Monsoon region where there is no hydrologic and hydraulic information. This study developed the stream flow generation method of daily mean flow for each month over a year for bed change simulation at an ungauged stream. The hydraulic geometries of cross-sections and the corresponding bankfull indicators of the Byeongseong river of 4 km reach were analyzed to estimate the bankfull discharge. The estimated bankfull discharge of the target reach was 77.50 m3/s, and the total annual discharge estimated 3720 m3/s through the correlation equation with the bankfull discharge. The measured total annual discharge of the Byeongseong river was 3887.30 m3/s, which is greater by 167.30 m3/s of 4.3% relative error. The volume and bed changes over a year by the Center for Computational Hydroscience and Engineering Two-Dimension (CCHE2D) model simulated using the measured discharge during 2013 and 2014 coincided with the surveyed in the same period. Estimated total annual discharge was used for the scenarios of stream flow generation. The generated stream flow using the flow apportioned to each month on the basis of the flow percentage in an adjacent stream simulated the river bed most appropriately. The generated stream flow using the flow based on the monthly rainfall percentage of the rainfall station in the target stream basin also simulated river bed well, which is confirmed as an alternative. Quantitatively, the root mean square error (RMSE), mean bias error (MBE), and mean absolute percentage error (MAPE) in-depth change of thalweg between the measured and the simulated were found to be 0.25 m, 0.04 m, and 0.44%, respectively. The result of the simulated cross-sectional river bed change for target reach coincided well with the surveyed. The proposed method is highly applicable to generate the stream flow for analyzing the yearly bed change at an ungauged stream in Monsoon region.

scholarly journals The Analysis of Dynamic O/D Adjustment for Bicycle Traffic Demand Estimation with AIMSUN Simulation Model: A Case Study of Nakhon Sawan Municipality in Thailand

2018 ◽  
Vol 12 (1) ◽  
pp. 352-365 ◽  
Author(s):  
Karn Chalermwongphan ◽  
Prapatpong Upala
Keyword(s):  
Simulation Model ◽  
Root Mean Square ◽  
Goodness Of Fit ◽  
Simulation Software ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Mean Square ◽  
Goodness Of Fit Test ◽  
Traffic Demand

Aim: This research aimed to present the process of estimating bicycle traffic demand in order to design bike routes that meet the daily transportation needs of the people in Nakhon Sawan Municipality. Methods: The primary and secondary traffic data were collected to develop a virtual traffic simulation model with the use of the AIMSUN simulation software. The model validation method was carried out to adjust the origin and destination survey data (O/D matrix) by running dynamic O/D adjustment. The 99 replication scenarios were statistically examined and assessed using the goodness-of-fit test. The 9 measures, which were examined, included: 1) Root Mean Square Error (RMSE), 2) Root Mean Square Percentage Error (RMSPE%), 3) Mean Absolute Deviation (MAD), 4) Mean Bias Error (MBE), 5) Mean Percentage Error (MPE%), 6) Mean Absolute Percentage Error (MAPE%), 7) Coefficient of Determination (R2), 8) GEH Statistic (GEH), and 9) Thiel’s U Statistic (Theil’s U). Results: The resulting statistical values were used to determine the acceptable ranges according to the acceptable indicators of each factor. Conclusion: It was found that there were only 8 scenarios that met the evaluation criteria. The selection and ranking process was consequently carried out using the multi-factor scoring method, which could eliminate errors that might arise from applying only one goodness-of-fit test measure.

scholarly journals Estimation of monthly global solar radiation in Buenos Aires: preliminary analysis

2014 ◽  
Vol 5 (1) ◽  
pp. 669-680
Author(s):  
Susan G. Lakkis ◽  
Mario Lavorato ◽  
Pablo O. Canziani
Keyword(s):  
Solar Radiation ◽  
Buenos Aires ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Estimation Accuracy ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Third Order ◽  
Sunshine Hours

Six existing models and one proposed approach for estimating global solar radiation were tested in Buenos Aires using commonly measured meteorological data as temperature and sunshine hours covering the years 2010-2013. Statistical predictors as mean bias error, root mean square, mean percentage error, slope and regression coefficients were used as validation criteria. The variability explained (R2), slope and MPE indicated that the higher precision could be excepted when sunshine hours are used as predictor. The new proposed approach explained almost 99% of the RG variability with deviation of less than ± 0.1 MJm-2day-1 and with the MPE smallest value below 1 %. The well known Ångström-Prescott methods, first and third order, was also found to perform for the measured data with high accuracy (R2=0.97-0.99) but with slightly higher MBE values (0.17-0.18 MJm-2day-1). The results pointed out that the third order Ångström type correlation did not improve the estimation accuracy of solar radiation given the highest range of deviation and mean percentage error obtained.  Where the sunshine hours were not available, the formulae including temperature data might be considered as an alternative although the methods displayed larger deviation and tended to overestimate the solar radiation behavior.

scholarly journals A Comparison of Models for Estimating Solar Radiation from Sunshine Duration in Croatia

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Tihomir Betti ◽  
Ivana Zulim ◽  
Slavica Brkić ◽  
Blanka Tuka
Keyword(s):  
Mean Absolute Error ◽  
Sunshine Duration ◽  
Model Performance ◽  
Absolute Error ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Mean Square ◽  
Using Data ◽  
Statistical Indicators

The performance of seventeen sunshine-duration-based models has been assessed using data from seven meteorological stations in Croatia. Conventional statistical indicators are used as numerical indicators of the model performance: mean absolute percentage error (MAPE), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). The ranking of the models was done using the combination of all these parameters, all having equal weights. The Rietveld model was found to perform the best overall, followed by Soler and Dogniaux-Lemoine monthly dependent models. For three best-performing models, new adjusted coefficients are calculated, and they are validated using separate dataset. Only the Dogniaux-Lemoine model performed better with adjusted coefficients, but across all analysed locations, the adjusted models showed improvement in reduced maximum percentage error.

scholarly journals Assessment of Artificial Neural Networks for Hourly Solar Radiation Prediction

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Tamer Khatib ◽  
Azah Mohamed ◽  
K. Sopian ◽  
M. Mahmoud
Keyword(s):  
Neural Networks ◽  
Solar Radiation ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Climate Conditions ◽  
The Neural Networks ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Radiation Conditions

This paper presents an assessment for the artificial neural network (ANN) based approach for hourly solar radiation prediction. The Four ANNs topologies were used including a generalized (GRNN), a feed-forward backpropagation (FFNN), a cascade-forward backpropagation (CFNN), and an Elman backpropagation (ELMNN). The three statistical values used to evaluate the efficacy of the neural networks were mean absolute percentage error (MAPE), mean bias error (MBE) and root mean square error (RMSE). Prediction results show that the GRNN exceeds the other proposed methods. The average values of the MAPE, MBE and RMSE using GRNN were 4.9%, 0.29% and 5.75%, respectively. FFNN and CFNN efficacies were acceptable in general, but their predictive value was degraded in poor solar radiation conditions. The average values of the MAPE, MBE and RMSE using the FFNN were 23%, −.09% and 21.9%, respectively, while the average values of the MAPE, MBE and RMSE using CFNN were 22.5%, −19.15% and 21.9%, respectively. ELMNN fared the worst among the proposed methods in predicting hourly solar radiation with average MABE, MBE and RMSE values of 34.5%, −11.1% and 34.35%. The use of the GRNN to predict solar radiation in all climate conditions yielded results that were highly accurate and efficient.

scholarly journals Mapping and evaluation of reference evapotranspiration methods under arid conditions

2018 ◽  
Vol 1 ◽  
pp. 39-45
Author(s):  
Mahmoud M.A. ◽  
Ali G.M.
Keyword(s):  
Reference Evapotranspiration ◽  
Climatic Conditions ◽  
Estimation Methods ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Distribution Maps ◽  
Climatic Regions ◽  
Arid Conditions ◽  
Vital Factor

Reference evapotranspiration ( ETo ) is a vital factor in water resources managing and planning. Various estimation methods have been developed for different climatic regions and according to the available data. Therefore, the reliability of such methods depends upon climatic conditions. The present investigation evaluates four temperature based methods: FAO Blaney-Criddle (BC), Turc, Jensen-Haise (JH) and Hargreaves (HG), and two radiation based methods: FAO-radiation (FAO-rad) and Priestley-Taylor (PT) in comparison with the FAO-PM method under arid conditions of Libya. In order to select the best ETo method, the percentage error of estimate ( PE ), the root mean square error ( RMSE ), and mean bias error ( MBE ) were calculated. The obtained ETo values (FAO-PM and the average of best-estimated monthly ETo ) were utilized to generate spatial distribution maps of ETo with the aid of Kriging technique. Statistical analysis of the obtained results revealed that, Turc equation fitted well for the northern part of the study area, which include Nalut, Zuara, Mosrata, Sirt, Shahat, Derna, Tubruk, Hon, Galo and Gagbub. While for southern zone, HG equation performed better for Opari and Tazirbu, BC equation for Kufra and Ghadames, FAO-Rad equation for Sebha; and JH equation for Ghat.

scholarly journals An artificial neural network-based prediction model for utilization of coal ash in production of fired clay bricks: A review

2021 ◽  
Vol 53 (1) ◽  
pp. 37-53
Author(s):  
Milica Vidak-Vasic ◽  
Lato Pezo ◽  
Vivek Gupta ◽  
Sandeep Chaudhary ◽  
Zagorka Radojevic
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Coal Ash ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Clay Bricks ◽  
Coal Ashes ◽  
Artificial Neural

This study analyzed the last 20 years` data available on power plant coal ashes used in clay brick production. The statistical analysis has been carried out for a total of 302 cases based on the relevant parameters reported in the literature. The chemical composition of the clays and coal ashes, percentage incorporation and maximum particle size of ash, size of fired samples, peak firing temperature, and the corresponding soaking time were selected as inputs for modeling. The product characteristics i.e. open porosity, water absorption, and compressive strength was taken as output parameters. An artificial neural network model has been developed and showed a satisfactory fit to experimental data and predicted the observed output variables with the overall coefficient of determination (r2) of 0.972 during the training period. Besides, the reduced chi-square, mean bias error, root mean square error, and mean percentage error were utilized to check the correctness of the obtained model, which proved the network generalization capability. The sensitivity analysis of the model suggested that the quantity of Na2O coming from brick clays, the percentages of SiO2 and K2O coming from ashes, and MgO coming from clays were the most influential parameters in descending order for the ash-clay composite bricks` quality, mostly owing to the influence of fluxes during firing.

scholarly journals Neural Methods Comparison for Prediction of Heating Energy Based on Few Hundreds Enhanced Buildings in Four Season’s Climate

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5453 ◽  
Author(s):  
Tomasz Szul ◽  
Krzysztof Nęcka ◽  
Thomas G. Mathia
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Residential Building ◽  
Regression Trees ◽  
Predictive Modelling ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
New Paradigm

Sustainable development and the increasing demand for equitable energy use as well as the reduction of waste of energy are the author’s social and scientific motivations. This new paradigm is the selection of a pertinent methodology to evaluate the efficiency of habitat thermomodernization, which is one of the scientific tasks of the presented study. In order to meet the social and scientific requirements, 380 buildings from the end of the last century (made of large plate technology), which were thermally improved at the beginning of the XXI century, were designed for a comparative analysis of the predictive modelling of heating energy consumption. A specific set of important variables characterizing the examined buildings has been identified. Groups of variables were used to estimate the energy consumption in such a way as to achieve a compromise between the difficulty of obtaining them and the quality of forecast. To predict energy consumption, the six most appropriate neural methods were used: artificial neural networks (ANN), general regression trees (CART), exhaustive regression trees (CHAID), support regression trees (SRT), support vectors (SV), and method multivariant adaptive regression splines (MARS). The quality assessment of the developed models used the mean absolute percentage error (MAPE) also known as mean absolute percentage deviation (MAPD), as well as mean bias error (MBE), coefficient of variance of the root mean square error (CV RMSE) and coefficient of determination (R2), which are accepted as statistical calibration standards by (American Society of Heating, Refrigerating and Air-Conditioning Engineers) ASHRAE. On this basis, the most effective method has been chosen, which gives the best results and therefore allows to forecast with great precision the energy consumption (after thermal improvement) for this type of residential building.

Evaluation of the regression parameters of the Angstrom-Page model for predicting global solar radiation

2013 ◽  
Vol 24 (2) ◽  
pp. 46-49 ◽  
Author(s):  
Solomon Agbo
Keyword(s):  
Solar Radiation ◽  
Statistical Tests ◽  
Global Solar Radiation ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Statistical Parameters ◽  
New Model ◽  
Page Model ◽  
Regression Parameters

A simple and empirical model for the estimation of average monthly global solar radiation for a Nigerian location is presented. Regression coefficients satisfying the Angstrom-page model have been obtained using clearness index (KT) and the relative sunshine data for the location. The test of validity of the model was done by evaluating the following statistical parameters: the mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE) and the correlation coefficient (CC). The results obtained from the statistical tests show that the new model is reliable for high precision estimation of global solar radiation. A comparison between the new model and other models is presented.

scholarly journals A Comparative Study of Some Regression Models to Estimate the Global Solar Radiation on a Horizontal Surface from Sunshine Duration and Meteorological Parameters for Ghardaïa Site, Algeria

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Kacem Gairaa ◽  
Yahia Bakelli
Keyword(s):  
Solar Radiation ◽  
Horizontal Plane ◽  
Regression Models ◽  
Meteorological Parameters ◽  
Sunshine Duration ◽  
Global Solar Radiation ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Quadratic Models

A comparison between some regression correlations for predicting the global solar radiation received on a horizontal plane has been processed. Seven models for estimating the global solar radiation from sunshine duration and two meteorological parameters (air temperature and relative humidity) are presented. The root mean square error (RMSE), mean bias error (MBE), correlation coefficient (CC), and percentage error () have been also computed to test the accuracy of the proposed models. Comparisons between the measured and the calculated values have been made. The results obtained show that the linear and quadratic models are the most suitable for estimating the global solar radiation from sunshine duration, and for the models based on meteorological parameters, Abdalla and Ojosu's models give the best performance with a CC of 0.898 and 0.892, respectively.

scholarly journals Power Prediction and Technoeconomic Analysis of a Solar PV Power Plant by MLP-ABC and COMFAR III, considering Cloudy Weather Conditions

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
M. Khademi ◽  
M. Moadel ◽  
A. Khosravi
Keyword(s):  
Power Plant ◽  
Output Power ◽  
Weather Conditions ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Solar Pv ◽  
Economic Point ◽  
Cloudy Weather ◽  
The Mean

The prediction of power generated by photovoltaic (PV) panels in different climates is of great importance. The aim of this paper is to predict the output power of a 3.2 kW PV power plant using the MLP-ABC (multilayer perceptron-artificial bee colony) algorithm. Experimental data (ambient temperature, solar radiation, and relative humidity) was gathered at five-minute intervals from Tehran University’s PV Power Plant from September 22nd, 2012, to January 14th, 2013. Following data validation, 10665 data sets, equivalent to 35 days, were used in the analysis. The output power was predicted using the MLP-ABC algorithm with the mean absolute percentage error (MAPE), the mean bias error (MBE), and correlation coefficient (R2), of 3.7, 3.1, and 94.7%, respectively. The optimized configuration of the network consisted of two hidden layers. The first layer had four neurons and the second had two neurons. A detailed economic analysis is also presented for sunny and cloudy weather conditions using COMFAR III software. A detailed cost analysis indicated that the total investment’s payback period would be 3.83 years in sunny periods and 4.08 years in cloudy periods. The results showed that the solar PV power plant is feasible from an economic point of view in both cloudy and sunny weather conditions.

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