scholarly journals Calibration and validation of the AquaCrop model for the soybean crop grown under different levels of irrigation in the Motopiba region, Brazil

2017 ◽  
Vol 48 (1) ◽  
Author(s):  
Vicente de Paulo Rodrigues da Silva ◽  
Roberta Araújo e Silva ◽  
Girlene Figueiredo Maciel ◽  
Célia Campos Braga ◽  
José Luiz Cabral da Silva Júnior ◽  
...  
Keyword(s):  
Model Performance ◽  
Absolute Error ◽  
Efficiency Index ◽  
Water Levels ◽  
Coefficient Of Determination ◽  
Yield Response ◽  
Statistical Parameters ◽  
Yield Data ◽  
Climate Conditions ◽  
Aquacrop Model

ABSTRACT: The water-driven AquaCrop model to simulate yield response has been calibrated and validated for soybean cultivated under different water levels irrigation in Matopiba region, Brazil. The crop was submitted to seven irrigation treatments during the dry season and a dry treatment in the rainy season. The model was parameterized and calibrated by using soybean yield data collected at field level. Model performance was evaluated by using the following statistical parameters: prediction error (Pe), Nash-Sutcliffe efficiency index (E), coefficient of determination (R2), mean absolute error (MAE), root mean square error normalized (RMSEN) and Willmott’s index (d). The statistical analyses of the AquaCrop model calibrated for the Matopiba region disclosed error acceptable for yield prediction of soybean grown under tropical climate conditions. Results also indicated that the C2 soybean cultivar is more resistant to water stress than the C1 soybean grown in the Matopiba region, Brazil. In the treatments when the crop was well supplied with water, at least in one phase, the yield was greater than those with drought stress at last in one phase.

scholarly journals Evapotranspiration simulated by CRITERIA and AquaCrop models in stony soils

2015 ◽  
Vol 10 (2) ◽  
pp. 67 ◽  
Author(s):  
Pasquale Campi ◽  
Francesca Modugno ◽  
Alejandra Navarro ◽  
Fausto Tomei ◽  
Giulia Villani ◽  
...  
Keyword(s):  
Statistical Tests ◽  
Efficiency Index ◽  
Balance Model ◽  
Coefficient Of Determination ◽  
Wheat Crop ◽  
Area Index ◽  
Aquacrop Model ◽  
Daily Evapotranspiration ◽  
Daily Scale ◽  
The Impact

The performance of a water balance model is also based on the ability to correctly perform simulations in heterogeneous soils. The objective of this paper is to test CRITERIA and AquaCrop models in order to evaluate their suitability in estimating evapotranspiration at the field scale in two types of soil in the Mediterranean region: non-stony and stony soil. The first step of the work was to calibrate both models under the non-stony conditions. The models were calibrated by using observations on wheat crop (leaf area index or canopy cover, and phenological stages as a function of degree days) and pedo-climatic measurements. The second step consisted in the analysing the impact of the soil type on the models performances by comparing simulated and measured values. The outputs retained in the analysis were soil water content (at the daily scale) and crop evapotranspiration (at two time scales: daily and crop season). The model performances were evaluated through four statistical tests: normalised difference (D%) at the seasonal time scale; and relative root mean square error (RRMSE), efficiency index (EF), coefficient of determination (r<sup>2</sup>) at the daily scale. At the seasonal scale, values of D% were less than 15% in stony and on-stony soils, indicating a good performance attained by both models. At the daily scale, the RRMSE values (2) indicate the inadequacy of AquaCrop to simulate correctly daily evapotranspiration. The higher performance of CRITERIA model to simulate daily evapotranspiration in stony soils, is due to the soil submodel, which requires the percentage skeleton as an input, while AquaCrop model takes into account the presence of skeleton by reducing the soil volume.

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 Using artificial neural network (ANN) for prediction of sediment loads, application to the Mellah catchment, northeast Algeria

2017 ◽  
Vol 33 (1) ◽  
pp. 47-55 ◽  
Author(s):  
Housseyn Bouzeria ◽  
Abderrahmane N. Ghenim ◽  
Kamel Khanchoul
Keyword(s):  
Suspended Sediment ◽  
Time Series Data ◽  
Water Discharge ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Sediment Discharge ◽  
Statistical Parameters ◽  
Discharge Data ◽  
Sediment Loads

AbstractIn this study, we present the performances of the best training algorithm in Multilayer Perceptron (MLP) neural networks for prediction of suspended sediment discharges in Mellah catchment. Time series data of daily suspended sediment discharge and water discharge from the gauging station of Bouchegouf were used for training and testing the networks. A number of statistical parameters, i.e. root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and coefficient of determination (R2) were used for performance evaluation of the model. The model produced satisfactory results and showed a very good agreement between the predicted and observed data. The results also showed that the performance of the MLP model was capable to capture the exact pattern of the sediment discharge data in the Mellah catchment.

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.

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 Carrot Yield Mapping: A Precision Agriculture Approach Based on Machine Learning

AI ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 229-241
Author(s):  
Marcelo Chan Fu Wei ◽  
Leonardo Felipe Maldaner ◽  
Pedro Medeiros Netto Ottoni ◽  
José Paulo Molin
Keyword(s):  
Precision Agriculture ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Spectral Band ◽  
Model Performance ◽  
Important Predictor ◽  
Predictor Variables ◽  
Coefficient Of Determination ◽  
Statistical Parameters ◽  
Entire Dataset

Carrot yield maps are an essential tool in supporting decision makers in improving their agricultural practices, but they are unconventional and not easy to obtain. The objective was to develop a method to generate a carrot yield map applying a random forest (RF) regression algorithm on a database composed of satellite spectral data and carrot ground-truth yield sampling. Georeferenced carrot yield sampling was carried out and satellite imagery was obtained during crop development. The entire dataset was split into training and test sets. The Gini index was used to find the five most important predictor variables of the model. Statistical parameters used to evaluate model performance were the root mean squared error (RMSE), coefficient of determination (R2) and mean absolute error (MAE). The five most important predictor variables were the near-infrared spectral band at 92 and 79 days after sowing (DAS), green spectral band at 50 DAS and blue spectral band at 92 and 81 DAS. The RF algorithm applied to the entire dataset presented R2, RMSE and MAE values of 0.82, 2.64 Mg ha−1 and 1.74 Mg ha−1, respectively. The method based on RF regression applied to a database composed of spectral bands proved to be accurate and suitable to predict carrot yield.

scholarly journals Validation of the procedure: Quantification of the degradation index of Photovoltaic Grid Connection Systems

2021 ◽  
Vol 2 (5) ◽  
pp. 8-13
Author(s):  
Proenza Y. Roger ◽  
Camejo C. José Emilio ◽  
Ramos H. Rubén
Keyword(s):  
Mean Absolute Error ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Degradation Index ◽  
Statistical Parameters ◽  
Grid Connection ◽  
The Mean

The results obtained from the validation of the procedure ‟Quantification of the degradation index of Photovoltaic Grid Connection Systems” are presented, using statistical parameters, which corroborate its accuracy, achieving a coefficient of determination of 0.9896, a percentage of the root of the mean square of the error RMSPE = 1.498% and a percentage of the mean absolute error MAPE = 1.15%, evidencing the precision of the procedure.

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 Hydrological Evaluation of PERSIANN-CDR Rainfall over Upper Senegal River and Bani River Basins

2018 ◽  
Vol 10 (12) ◽  
pp. 1884 ◽  
Author(s):  
Khalidou Bâ ◽  
Luis Balcázar ◽  
Vitali Diaz ◽  
Febe Ortiz ◽  
Miguel Gómez-Albores ◽  
...  
Keyword(s):  
Hydrological Modeling ◽  
Model Performance ◽  
Water Levels ◽  
Coefficient Of Determination ◽  
Climate Data ◽  
Senegal River ◽  
Precipitation Estimation ◽  
Main Input ◽  
Three Rivers ◽  
Climate Data Record

This study highlights the advantage of satellite-derived rainfall products for hydrological modeling in regions of insufficient ground observations such as West African basins. Rainfall is the main input for hydrological models; however, gauge data are scarce or difficult to obtain. Fortunately, several precipitation products are available. In this study, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) was analyzed. Daily discharges of three rivers of the Upper Senegal basin and one of the Upper Niger basin, as well as water levels of Manantali reservoir were simulated using PERSIANN-CDR as input to the CEQUEAU model. First, CEQUEAU was calibrated and validated using raw PERSIANN-CDR, and second, rainfalls were bias-corrected and the model was recalibrated. In both cases, ERA-Interim temperatures were used. Model performance was evaluated using Nash–Sutcliffe efficiency (NSE), mean percent bias (MPBIAS), and coefficient of determination (R2). With raw PERSIANN-CDR, most years show good performance with values of NSE > 0.8, R2 > 0.90, and MPBIAS < 10%. However, bias-corrected PERSIANN-CDR did not improve the simulations. The findings of this study can be used to improve the design of dam projects such as the ongoing dam constructions on the three rivers of the Upper Senegal Basin.

Estimation of spatial soil depth and its application for shallow landslides and debris flow assessment: case study at Mt. Jiri, S. Korea

2020 ◽  
Author(s):  
Minseok Kim ◽  
Jisu Kim ◽  
Hyun-Joo Oh ◽  
Jinkwan Kim
Keyword(s):  
Debris Flow ◽  
Depth Distribution ◽  
Soil Depth ◽  
Critical Role ◽  
Model Performance ◽  
Absolute Error ◽  
Shallow Landslide ◽  
Test Method ◽  
Coefficient Of Determination ◽  
Gps Rtk

&lt;p&gt;Soil depth plays critical role in prediction studies reflecting hydrologic mechanism such as shallow landslide and debris flow although there are many parameters. Thus, many researchers are studying the estimation of soil depth distribution using various methods such as a kriging and artificial neural networks (ANNs) since it is not easy to get a detailed soil depth distribution in field. The aims of this study are 1) to estimate detailed spatial distribution of soil depth (various methods such as ANNs, Kriging, s- and z-model, and c-model) and, 2) to apply them for assessment of shallow landslide instability and debris flow. To do this, soil depth of 760 points using knocking pole test method and elevation datasets using GPS-RTK were collected at Mt Jiri, South Korea. To analysis the accuracy of each estimated soil depth distribution, the lowest root mean square error (RMSE), mean absolute error (MAE) and the highest values of the coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt;) were applied and, ANNs method showed reasonable result better than did others. In the effect of shallow landslide instability and debris flow assessment with the each soil depth distribution results, soil depth distribution using an ANNs method also showed high simulated model performance by modified success ratio (MSR). These results indicated that ANNs can be one of the methods to estimate the soil depth distribution for improvement of accuracy of shallow landslide instability mapping and debris flow assessment.&lt;/p&gt;

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