scholarly journals Simple and Low-Cost Procedure for Monthly and Yearly Streamflow Forecasts during the Current Hydrological Year

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1038 ◽  
Author(s):  
Fernando Delgado-Ramos ◽  
Carmen Hervás-Gámez
Keyword(s):  
Low Cost ◽  
Model Performance ◽  
Absolute Error ◽  
Integrated Water Resources Management ◽  
Coefficient Of Determination ◽  
Water Volume ◽  
Cumulative Probability ◽  
Water Users ◽  
The Mean ◽  
The Right

Accurately forecasting streamflow values is essential to achieve an efficient, integrated water resources management strategy and to provide consistent support to water decision-makers. We present a simple, low-cost, and robust approach for forecasting monthly and yearly streamflows during the current hydrological year, which is applicable to headwater catchments. The procedure innovatively combines the use of well-known regression analysis techniques, the two-parameter Gamma continuous cumulative probability distribution function and the Monte Carlo method. Several model performance statistics metrics (including the Coefficient of Determination R2; the Root-Mean-Square Error RMSE; the Mean Absolute Error MAE; the Index of Agreement IOA; the Mean Absolute Percent Error MAPE; the Coefficient of Nash-Sutcliffe Efficiency NSE; and the Inclusion Coefficient IC) were used and the results showed good levels of accuracy (improving as the number of observed months increases). The model forecast outputs are the mean monthly and yearly streamflows along with the 10th and 90th percentiles. The methodology has been successfully applied to two headwater reservoirs within the Guadalquivir River Basin in southern Spain, achieving an accuracy of 92% and 80% in March 2017. These risk-based predictions are of great value, especially before the intensive irrigation campaign starts in the middle of the hydrological year, when Water Authorities have to ensure that the right decision is made on how to best allocate the available water volume between the different water users and environmental needs.

scholarly journals Simple and Low-cost Model for Monthly and Yearly Streamflow Forecasts during the Current Hydrological Year

2018 ◽  
Author(s):  
Fernando Delgado-Ramos ◽  
Carmen Hervás-Gámez
Keyword(s):  
Cost Model ◽  
Low Cost ◽  
Integrated Water Resources Management ◽  
Water Volume ◽  
Cumulative Probability ◽  
Prediction Errors ◽  
Water Users ◽  
The Monte Carlo Method ◽  
Streamflow Forecasts ◽  
Guadalquivir River

Forecasting streamflow accurately is essential to achieve an efficient integrated water resources management strategy and provide consistent support to water decision-makers. We present a simple, low-cost and robust approach for forecasting monthly and yearly streamflow during the hydrological year in course, applicable to headwater catchments. It combines the use of regression analysis techniques, the two-parameter Gamma continuous cumulative probability distribution function and the Monte Carlo method. It is based on a probabilistic comparison of the progression of the current hydrological year with the historic observed series. The methodology has been successfully applied to two headwater reservoirs within the Guadalquivir River Basin in southern Spain. The root-mean-square error and correlation coefficient were used to measure the accuracy of the model and the results showed good levels of reliability. The outputs are the probabilistic monthly and yearly streamflow and 80% confidence interval. Further reductions in prediction errors may be achieved from increasing the number of observed years. These risk-based predictions are of great value, especially, before the intensive irrigation campaign starts (usually in April), when Water Authorities are to take responsible management decisions about the best allocation of the available water volume between the different water users and environmental needs.

scholarly journals Alometric Model for Estimation of Leaf Area of Garcinia brasiliensis Mart. Through Non-destructive Method

2019 ◽  
Vol 11 (10) ◽  
pp. 154
Author(s):  
Vinicius de Souza Oliveira ◽  
Cássio Francisco Moreira de Carvalho ◽  
Juliany Morosini França ◽  
Flávia Barreto Pinto ◽  
Karina Tiemi Hassuda dos Santos ◽  
...  
Keyword(s):  
Leaf Area ◽  
Linear Equations ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Maximum Width ◽  
Linear Dimensions ◽  
Length Width ◽  
Independent Variable ◽  
The Mean ◽  
Non Destructive

The objective of the present study was to test and establish mathematical models to estimate the leaf area of Garcinia brasiliensis Mart. through linear dimensions of the length, width and product of both measurements. In this way, 500 leaves of trees with age between 4 and 6 years were collected from all the cardinal points of the plant in the municipality of São Mateus, North of the State of Espírito Santo, Brazil. The length (L) along the main midrib, the maximum width (W), the product of the length with the width (LW) and the observed leaf area (OLA) were obtained for all leaves. From these measurements were adjusted linear equations of first degree, quadratic and power, in which OLA was used as dependent variable as function of L, W and LW as independent variable. For the validation, the values of L, W and LW of 100 random leaves were substituted in the equations generated in the modeling, thus obtaining the estimated leaf area (ELA). The values of the means of ELA and OLA were tested by Student’s t test 5% of probability. The mean absolute error (MAE), root mean square error (RMSE) and Willmott’s index d for all proposed models were also determined. The choice of the best model was based on the non significant values in the comparison of the means of ELA and OLA, values of MAE and RMSE closer to zero and value of the index d and coefficient of determination (R2) close to unity. The equation that best estimates leaf area of Garcinia brasiliensis Mart. in a way non-destructive is the power model represented by por ELA = 0.7470(LW)0.9842 and R2 = 0.9949.

Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

2020 ◽  
Vol 50 (10) ◽  
pp. 1012-1024
Author(s):  
Meimei Wang ◽  
Jiayuan Lin
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Laser Scanning ◽  
Tree Height ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Surface Model ◽  
Individual Tree ◽  
Aerial Vehicle ◽  
The Mean

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

scholarly journals Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete

Materials ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1072 ◽  
Author(s):  
Dong Van Dao ◽  
Hai-Bang Ly ◽  
Huong-Lan Thi Vu ◽  
Tien-Thinh Le ◽  
Binh Thai Pham
Keyword(s):  
Compressive Strength ◽  
Civil Engineering ◽  
Mean Squared Error ◽  
Model Performance ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Dry Density ◽  
Statistical Measures ◽  
Foamed Concrete

Development of Foamed Concrete (FC) and incessant increases in fabrication technology have paved the way for many promising civil engineering applications. Nevertheless, the design of FC requires a large number of experiments to determine the appropriate Compressive Strength (CS). Employment of machine learning algorithms to take advantage of the existing experiments database has been attempted, but model performance can still be improved. In this study, the performance of an Artificial Neural Network (ANN) was fully analyzed to predict the 28 days CS of FC. Monte Carlo simulations (MCS) were used to statistically analyze the convergence of the modeled results under the effect of random sampling strategies and the network structures selected. Various statistical measures such as Coefficient of Determination (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used for validation of model performance. The results show that ANN is a highly efficient predictor of the CS of FC, achieving a maximum R2 value of 0.976 on the training part and an R2 of 0.972 on the testing part, using the optimized C-ANN-[3–4–5–1] structure, which compares with previous published studies. In addition, a sensitivity analysis using Partial Dependence Plots (PDP) over 1000 MCS was also performed to interpret the relationship between the input parameters and 28 days CS of FC. Dry density was found as the variable with the highest impact to predict the CS of FC. The results presented could facilitate and enhance the use of C-ANN in other civil engineering-related problems.

scholarly journals Improving the Accuracy of Low-Cost Sensor Measurements for Freezer Automation

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6389
Author(s):  
Kyriakos Koritsoglou ◽  
Vasileios Christou ◽  
Georgios Ntritsos ◽  
Georgios Tsoumanis ◽  
Markos G. Tsipouras ◽  
...  
Keyword(s):  
Temperature Sensor ◽  
Nonlinear Response ◽  
Low Cost ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Generalization Error ◽  
Acceptable Error ◽  
The Mean ◽  
Digital Temperature Sensor ◽  
Monitoring Devices

In this work, a regression method is implemented on a low-cost digital temperature sensor to improve the sensor’s accuracy; thus, following the EN12830 European standard. This standard defines that the maximum acceptable error regarding temperature monitoring devices should not exceed 1 °C for the refrigeration and freezer areas. The purpose of the proposed method is to improve the accuracy of a low-cost digital temperature sensor by correcting its nonlinear response using simple linear regression (SLR). In the experimental part of this study, the proposed method’s outcome (in a custom created dataset containing values taken from a refrigerator) is compared against the values taken from a sensor complying with the EN12830 standard. The experimental results confirmed that the proposed method reduced the mean absolute error (MAE) by 82% for the refrigeration area and 69% for the freezer area—resulting in the accuracy improvement of the low-cost digital temperature sensor. Moreover, it managed to achieve a lower generalization error on the test set when compared to three other machine learning algorithms (SVM, B-ELM, and OS-ELM).

scholarly journals PM2.5 Modeling and Historical Reconstruction over the Continental USA Utilizing GOES-16 AOD

2021 ◽  
Vol 13 (23) ◽  
pp. 4788
Author(s):  
Xiaohe Yu ◽  
David J. Lary ◽  
Christopher S. Simmons
Keyword(s):  
Weather Forecasting ◽  
Model Performance ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
High Temporal Resolution ◽  
Historical Reconstruction ◽  
Spatial And Temporal Patterns ◽  
Ancillary Data ◽  
Machine Learning Model ◽  
Pollution Event

In this study, we present a nationwide machine learning model for hourly PM2.5 estimation for the continental United States (US) using high temporal resolution Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) data, meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF) and ancillary data collected between May 2017 and December 2020. A model sensitivity analysis was conducted on predictor variables to determine the optimal model. It turns out that GOES16 AOD, variables from ECMWF, and ancillary data are effective variables in PM2.5 estimation and historical reconstruction, which achieves an average mean absolute error (MAE) of 3.0 μg/m3, and a root mean square error (RMSE) of 5.8 μg/m3. This study also found that the model performance as well as the site measured PM2.5 concentrations demonstrate strong spatial and temporal patterns. Specifically, in the temporal scale, the model performed best between 8:00 p.m. and 11:00 p.m. (UTC TIME) and had the highest coefficient of determination (R2) in Autumn and the lowest MAE and RMSE in Spring. In the spatial scale, the analysis results based on ancillary data show that the R2 scores correlate positively with the mean measured PM2.5 concentration at monitoring sites. Mean measured PM2.5 concentrations are positively correlated with population density and negatively correlated with elevation. Water, forests, and wetlands are associated with low PM2.5 concentrations, whereas developed, cultivated crops, shrubs, and grass are associated with high PM2.5 concentrations. In addition, the reconstructed PM2.5 surfaces serve as an important data source for pollution event tracking and PM2.5 analysis. For this purpose, from May 2017 to December 2020, hourly PM2.5 estimates were made for 10 km by 10 km and the PM2.5 estimates from August through November 2020 during the period of California Santa Clara Unite (SCU) Lightning Complex fires are presented. Based on the quantitative and visualization results, this study reveals that a number of large wildfires in California had a profound impact on the value and spatial-temporal distributions of PM2.5 concentrations.

scholarly journals A MODIFIED EMPIRICAL MODEL FOR ESTIMATING THE WETTED ZONE DIMENSIONS UNDER DRIP IRRIGATION

2015 ◽  
Vol 76 (15) ◽  
Author(s):  
Ahmed A. M. Al-Ogaidi ◽  
Aimrun Wayayok ◽  
Md Rowshon Kamal ◽  
Ahmed Fikri Abdullah
Keyword(s):  
Empirical Model ◽  
Drip Irrigation ◽  
Irrigation System ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Irrigation Systems ◽  
Modified Model ◽  
Model Efficiency ◽  
The Mean ◽  
Statistical Criteria

Drip irrigation system has become one of the most common irrigation systems especially in arid and semi-arid regions due to its advantages in saving water. One of the most essential considerations in designing these systems is the dimensions of the wetted soil volume under emitters. These dimensions are significant in choosing the proper emitter spacing along the laterals and the suitable distance between laterals. In this study, a modified empirical equations for estimating the horizontal and vertical extend of the wetted zone under surface emitters were suggested. Data from published papers includes different conditions of soil properties and emitter discharge were used in deriving the empirical model using the nonlinear regression. The developed model has high value for coefficient of determination, R2. The results from the developed model were compared with results of other empirical models derived by other researchers. Some statistical criteria were used to evaluate the model performance which are the mean error ME, root mean square error RMSE, and model efficiency EF. The results revealed that the modified model showed good performance in predicting the wetted zone dimensions and it can be used in design and management of drip irrigation systems. 

scholarly journals Measurements of wave damping by a grease ice slick in Svalbard using off-the-shelf sensors and open-source electronics

2017 ◽  
Vol 63 (238) ◽  
pp. 372-381 ◽  
Author(s):  
JEAN RABAULT ◽  
GRAIG SUTHERLAND ◽  
OLAV GUNDERSEN ◽  
ATLE JENSEN
Keyword(s):  
Wave Propagation ◽  
Open Source ◽  
Wave Attenuation ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Power Spectral ◽  
The Mean ◽  
The One ◽  
The Waves ◽  
Best Fit

ABSTRACTVersatile instruments assembled from off-the-shelf sensors and open-source electronics are used to record wave propagation and damping measured by Inertial Motion Units (IMUs) in a grease ice slick near the shore in Adventfjorden, Svalbard. Viscous attenuation of waves due to the grease ice slick is clearly visible by comparing the IMU data recorded by the different instruments. The frequency dependent spatial damping of the waves is computed by comparing the power spectral density obtained from the different IMUs. We model wave attenuation using the one-layer model of Weber from 1987. The best-fit value for the effective viscosity is ν = (0.95 ± 0.05 × 10−2)m2 s−1, and the coefficient of determination is R2 = 0.89. The mean absolute error and RMSE of the damping coefficient are 0.037 and 0.044m−1, respectively. These results provide continued support for improving instrument design for recording wave propagation in ice-covered regions, which is necessary to this area of research as many authors have underlined the need for more field data.

Attributiveness of a mass flow analysis model for integrated water resources assessment under data-scarce conditions

2013 ◽  
Vol 67 (2) ◽  
pp. 261-270 ◽  
Author(s):  
B. Helm ◽  
T. Terekhanova ◽  
J. Tränckner ◽  
M. Venohr ◽  
P. Krebs
Keyword(s):  
Water Quality Management ◽  
Flow Analysis ◽  
Model Performance ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Emission Sources ◽  
Analysis Model ◽  
River Systems ◽  
Systems Model ◽  
Nutrient Emissions

Nutrients in river systems originate from multiple emission sources, follow various pathways, and are subject to processes of conversion and fate. One approach to tackle this complexity is to apply balance-oriented models. Although these models operate on a coarse temporal and spatial scale, they are capable of assessing the significance of the different emission sources and their results can be the basis for developing integrated water quality management schemes. In this paper we propose and apply a methodology to evaluate the attributiveness of such model results with regard to the modelled emission pathways. The MONERIS (MOdelling Nutrient Emissions in RIver Systems) model is set up, assuming plausible ranges of emission levels from four principal sources. The sensitivity of model performance is computed and related to the contribution from the pathways. The approach is applied for a case study in the upper Western Bug catchment (Ukraine). Coefficient of determination (R²) is found insensitive against the model assumptions, at levels around 0.65 for nitrogen and 0.55 for phosphorous emissions. Relative mean absolute error is minimized around 0.2 for both nutrients, but with equifinal combinations of the varied emission pathways. Model performance is constrained by the ranges of the emission assumptions to a limited extent only.

scholarly journals Performance Evaluation of Mike Nam Rainfall-Runoff (R-R) Model in Daily Flow Simulation (Case Study: Gonbad Catchment in Hamedan)

2020 ◽  
Vol 10 (1) ◽  
pp. 1-6
Author(s):  
Yasamin Sajadi Bami ◽  
Jahangir Porhemmat ◽  
Hossein Sedghi ◽  
Navid Jalalkamali
Keyword(s):  
Flow Rate ◽  
Model Performance ◽  
Flow Simulation ◽  
Coefficient Of Determination ◽  
Rainfall Runoff ◽  
Mean Flow ◽  
Rainfall Runoff Model ◽  
The Right ◽  
Runoff Model

AbstractNowadays, many hydrological rainfall-runoff (R-R) models, both distributed and lumped, have been developed to simulate the catchment. However, selecting the right model to simulate a specific catchment has always been a challenge. A proper understanding of the model and its advantages and limitations is essential for selecting the appropriate model for the purpose of the study. To this end, several studies have been carried out to evaluate the performance of hydrological models for specific areas (mountainous, marshy and so on). This study was conducted aimed at evaluating the performance of MIKE11 NAM lumped conceptual hydrological rainfall-runoff model in simulation of daily flow rate in Gonbad catchment. The NAM model was calibrated and validated using flow rate data of three hydrometric stations of the Gonbad catchment. The model performance was evaluated using Percent bias (PBIAS) and the coefficient of determination or Nash-Sutcliffe coefficient. A Nash Sutcliffe efficiency (NSE) of 0.80, 0.89 and 080 were obtained during calibration, whereas, for the validation period, NSE of 0.81, 0.87 and 0.71 were obtained for Nemooneh sub catchment, Shahed sub catchment and Gonbad catchment respectively. Percent bias of -0.6, 1.5 and 6.3 were achieved for calibration, while -2.7, 7.6 and -4.2 were acquired during validation for Nemooneh sub catchment, Shahed sub catchment and Gonbad catchment respectively. Based on the results, the MIKE 11 NAM lumped conceptual model was capable of simulating daily mean flow rate and mean flow volume.

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