scholarly journals DSSAT-CSM Soil Module: Modeling Topsoil Water Holding Capacity in the two Dry Savanna Zones of Kano State, Nigeria.

2019 ◽  
pp. 1-6
Keyword(s):  
Mean Squared Error ◽  
Coefficient Of Determination ◽  
Intrinsic Properties ◽  
Squared Error ◽  
Crop Simulation ◽  
Index Of Agreement ◽  
Sudan Savanna ◽  
Water Holding ◽  
Nigerian Savanna ◽  
Savanna Soil

The objective of this study was to test the efficiency of the Hydraulic Pedotrans- fer Functions (PTFs) employed in the Decision Support System for Agrotechnol- ogy Transfer – Crop Simulation Model (DSSAT-CSM) in modeling topsoil WHC in Northern Guinea Savanna (NGS) and Sudan Savanna (SS) of Kano State in Nigeria. Coefficient of determination (R2), Root Mean Squared Error (RMSE), and Index of Agreement (d-index) were the three statistical methods used to test the fitness between predicted, and laboratory observed WHC of dis- turbed, auger sampled topsoil. Findings of the study established that the PTFs fitted in the algorithm of DSSAT-CSM soil water sub module made a significant topsoil WHC estimation in NGS with statistics R² = 0.352, RMSE = 0.03, and d- Index = 0.71. However, the model did not estimate the WHC validly in Sudan Savanna, with insignificant statistics of R² = 0.031, RMSE of 0.10, and 0.44 as the index of agreement. The conclusion drawn was that DSSAT made fair and poor predictions of topsoil WHC in NGS and SS soils respectively, irrespective of texture and other intrinsic properties. Based on the findings above, we recom- mend the development of local PTFs alternatives to be used with DSSAT’s algo- rithm for Nigerian Savanna soil

scholarly journals Comparison of statistical indices for the evaluation of crop models performance

2021 ◽  
Vol 74 (3) ◽  
pp. 9675-9684
Author(s):  
Tatiana María Saldaña Villota ◽  
José Miguel Cotes Torres
Keyword(s):  
Mean Squared Error ◽  
Potato Crop ◽  
Coefficient Of Determination ◽  
Model Assessment ◽  
Data Set ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Statistical Indices ◽  
Index Of Agreement ◽  
Relative Root

This study presents a comparison of the usual statistical methods used for crop model assessment. A case study was conducted using a data set from observations of the total dry weight in diploid potato crop, and six simulated data sets derived from the observationsaimed to predict the measured data. Statistical indices such as the coefficient of determination, the root mean squared error, the relative root mean squared error, mean error, index of agreement, modified index of agreement, revised index of agreement, modeling efficiency, and revised modeling efficiency were compared. The results showed that the coefficient of determination is not a useful statistical index for model evaluation. The root mean squared error together with the relative root mean squared error offer an excellent notion of how deviated the simulations are in the same unit of the variable and percentage terms, and they leave no doubt when evaluating the quality of the simulations of a model.

Machine learning and Grad-Cam based vascular aging assessment using photoplethysmogram (Preprint)

2021 ◽  
Author(s):  
Hangsik Shin
Keyword(s):  
Machine Learning ◽  
Correlation Coefficient ◽  
Age Estimation ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Vascular Aging ◽  
Squared Error ◽  
Vascular Age

BACKGROUND Arterial stiffness due to vascular aging is a major indicator for evaluating cardiovascular risk. OBJECTIVE In this study, we propose a method of estimating age by applying machine learning to photoplethysmogram for non-invasive vascular age assessment. METHODS The machine learning-based age estimation model that consists of three convolutional layers and two-layer fully connected layers, was developed using segmented photoplethysmogram by pulse from a total of 752 adults aged 19–87 years. The performance of the developed model was quantitatively evaluated using mean absolute error, root-mean-squared-error, Pearson’s correlation coefficient, coefficient of determination. The Grad-Cam was used to explain the contribution of photoplethysmogram waveform characteristic in vascular age estimation. RESULTS Mean absolute error of 8.03, root mean squared error of 9.96, 0.62 of correlation coefficient, and 0.38 of coefficient of determination were shown through 10-fold cross validation. Grad-Cam, used to determine the weight that the input signal contributes to the result, confirmed that the contribution to the age estimation of the photoplethysmogram segment was high around the systolic peak. CONCLUSIONS The machine learning-based vascular aging analysis method using the PPG waveform showed comparable or superior performance compared to previous studies without complex feature detection in evaluating vascular aging. CLINICALTRIAL 2015-0104

Application of Artificial Neural Network Over Nickel-Based Catalyst for Combined Steam-Carbon Dioxide of Methane Reforming (CSDRM)

2020 ◽  
Vol 20 (9) ◽  
pp. 5716-5719 ◽  
Author(s):  
Cho Hwe Kim ◽  
Young Chul Kim
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Coefficient Of Determination ◽  
Feed Ratio ◽  
Squared Error ◽  
Highly Nonlinear ◽  
Feed Forward Network ◽  
Artificial Neural

The application of artificial neural network (ANN) for modeling, combined steam-carbon dioxide reforming of methane over nickel-based catalysts, was investigated. The artificial neural network model consisted of a 3-layer feed forward network, with hyperbolic tangent function. The number of hidden neurons is optimized by minimization of mean square error and maximization of R2 (R square, coefficient of determination) and set of 8 neurons. With feed ratio, flow rate, and temperature as independent variables, methane, carbon dioxide conversion, and H2/CO ratio, were measured using artificial neural network. Coefficient of determination (R2) values of 0.9997, 0.9962, and 0.9985 obtained, and MAE (Mean Absolute Error), MSE (Mean Squared Error), RMSE (Root Mean Squared Error), and MAPE (Mean Absolute Percentage Error) showed low value. This study indicates ANN can successfully model a highly nonlinear process and function.

scholarly journals A hydrological model skill score and revised R-squared

2021 ◽  
Author(s):  
Charles Onyutha
Keyword(s):  
Mathematical Modeling ◽  
Goodness Of Fit ◽  
Hydrological Model ◽  
Mean Squared Error ◽  
Skill Score ◽  
Coefficient Of Determination ◽  
Model Skill ◽  
Squared Error ◽  
Quality Coefficient ◽  
Correlation Term

Abstract Despite the advances in methods of statistical and mathematical modeling, there is considerable lack of focus on improving how to judge models’ quality. Coefficient of determination (R2) is arguably the most widely applied ‘goodness-of-fit’ metric in modelling and prediction of environmental systems. However, known issues of R2 are that it: (i) can be low and high for an accurate and imperfect model, respectively; (ii) yields the same value when we regress observed on modelled series and vice versa; and (iii) does not quantify a model's bias (B). A new model skill score E and revised R-squared (RRS) are presented to combine correlation, term B and capacity to capture variability. Differences between E and RRS lie in the forms of correlation and the term B used for each metric. Acceptability of E and RRS was demonstrated through comparison of results from a large number of hydrological simulations. By applying E and RRS, the modeller can diagnostically identify and expose systematic issues behind model optimizations based on other ‘goodness-of-fits’ such as Nash–Sutcliffe efficiency (NSE) and mean squared error. Unlike NSE, which varies from −∞ to 1, E and RRS occur over the range 0–1. MATLAB codes for computing E and RRS are provided.

scholarly journals Forecasting New Student Candidates Using the Random Forest Method

2020 ◽  
Vol 11 (1) ◽  
pp. 44
Author(s):  
Rahmat Robi Waliyansyah ◽  
Nugroho Dwi Saputro
Keyword(s):  
Random Forest ◽  
Mean Squared Error ◽  
College Education ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Squared Error ◽  
Random Forest Method ◽  
Study Programs ◽  
New Students ◽  
New Student

College education institutions regularly hold new student admissions activities, and the number of new students can increase and can also decrease. University of PGRI Semarang (UPGRIS) on the development of new student admissions for the 2014/2015 academic year up to 2018/2019 with so many admissions selection stages. To meet the minimum comparison requirements between the number of students with the development of human resources, facilities, and infrastructure, it is necessary to predict how much the number of students increases each year. To make a prediction system or forecasting, the number of prospective new students required a good forecasting method and sufficiently precise calculations to predict the number of prospective students who register. In this study, the method to be taken is the Random Forest method. For the evaluation of forecasting models used Random Sampling and Cross-validation. The parameter used is Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). The results of this study obtained the five highest and lowest study programs in the admission of new students. Therefore, UPGRIS will make a new strategy for the five lowest study programs so that the desired number of new students is achieved

Modelling of CO2 Loading in DEA by Using Peak Ratio of Raman Spectroscopy

2014 ◽  
Vol 625 ◽  
pp. 188-191
Author(s):  
Muhammad Zubair Shahid ◽  
Abdulhalim Shah Maulud ◽  
Mohammad Azmi Bustam
Keyword(s):  
Carbon Dioxide ◽  
Raman Spectroscopy ◽  
Processing Time ◽  
Mean Squared Error ◽  
Raman Peak ◽  
Coefficient Of Determination ◽  
Absorption Process ◽  
Peak Ratio ◽  
Squared Error ◽  
Wide Range

Carbon dioxide (CO2) capturing has been an important issue for decades. Alkanoamines, such as diethanolamine (DEA) have been widely used for CO2separation by absorption process. During this process, CO2loading measurement is an imperative action for a proper process control. Currently used methods are titration based which requires a long processing time. In this work Raman spectroscopy is used to model and predict the CO2loading in wide range (0-0.97 CO2mole/amine mole). The models are developed by using Raman peak ratios to minimize the error due to peaks fluctuations. The Raman peak ratio of 1022 cm-1/1461cm-1has been found as a good fit with the coefficient of determination (R2) of 0.92 and mean squared error (MSE) of 0.00656 CO2mole2/ amine mole2in prediction of CO2loading.

scholarly journals A Comparison of Performance of Artificial Neural Networks for Prediction of Heavy Metals Concentration in Groundwater Resources of Toyserkan Plain

2017 ◽  
Vol 4 (1) ◽  
pp. 11792-11792 ◽  
Author(s):  
Meysam Alizamir ◽  
Soheil Sobhanardakani
Keyword(s):  
Heavy Metals ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Squared Error ◽  
Groundwater Resources ◽  
Coefficient Of Determination ◽  
Multi Layer Perceptron ◽  
Squared Error ◽  
Artificial Neural ◽  
Statistical Indicators

Nowadays, about 50% the world’s population is living in dry and semi dry regions and has utilized groundwater as a source of drinking water. Therefore, forecasting of pollutant content in these regions is vital. This study was conducted to compare the performance of artificial neural networks (ANNs) for prediction of As, Zn, and Pb content in groundwater resources of Toyserkan Plain. In this study, two types of artificial neural networks (ANNs), namely multi-layer perceptron (MLP) and Radial Basis Function (RBF) approaches, were examined using the observations of As, Zn, and Pb concentrations in groundwater resources of Toyserkan plain, Western Iran. Two statistical indicators, the coefficient of determination (R2) and root mean squared error (RMSE) were employed to evaluate the performances of various models. The results indicated that the best performance could be obtained by MLP, in terms of different statistical indicators during training and validation periods.

scholarly journals A hybrid model for modelling the salinity of the Tafna River in Algeria

2019 ◽  
Vol 40 (1) ◽  
pp. 127-135 ◽  
Author(s):  
Khemissi Houari ◽  
Tarik Hartani ◽  
Boualem Remini ◽  
Abdelouhab Lefkir ◽  
Leila Abda ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Efficiency Coefficient ◽  
Inference System ◽  
Squared Error ◽  
Good Agreement

Abstract In this paper, the capacity of an Adaptive-Network-Based Fuzzy Inference System (ANFIS) for predicting salinity of the Tafna River is investigated. Time series data of daily liquid flow and saline concentrations from the gauging station of Pierre du Chat (160801) were used for training, validation and testing the hybrid model. Different methods were used to test the accuracy of our results, i.e. coefficient of determination (R2), Nash–Sutcliffe efficiency coefficient (E), root of the mean squared error (RSR) and graphic techniques. The model produced satisfactory results and showed a very good agreement between the predicted and observed data, with R2 equal (88% for training, 78.01% validation and 80.00% for testing), E equal (85.84% for training, 82.51% validation and 78.17% for testing), and RSR equal (2% for training, 10% validation and 49% for testing).

scholarly journals Performance Evaluation of ANFIS and GA-ANFIS for Predicting Stock Market Indices

2021 ◽  
Vol 13 (7) ◽  
pp. 1
Author(s):  
Farnaz Ghashami ◽  
Kamyar Kamyar
Keyword(s):  
Genetic Algorithm ◽  
Stock Market ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
The United States ◽  
Coefficient Of Determination ◽  
Improve Performance ◽  
Inference System ◽  
Squared Error ◽  
Neuro Fuzzy

A model of Adaptive Neuro-Fuzzy Inference System (ANFIS) trained with an evolutionary algorithm, namely Genetic Algorithm (GA) is presented in this paper. Further, the model is tested on the NASDAQ stock market indices which is among the most widely followed indices in the United States. Empirical results show that by determining the parameters of ANFIS (premise and consequent parameters) using GA, we can improve performance in terms of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), coefficient of determination (R-Squared) in comparison with using solely ANFIS.

Estimating Daily Solar Radiation Using Hargreaves Model in Eastern Malaysia

2014 ◽  
Vol 699 ◽  
pp. 564-569 ◽  
Author(s):  
Mohd Irwan Yusoff ◽  
Muhamad Irwanto ◽  
Safwati Ibrahim ◽  
Gomesh Nair ◽  
Syed Idris Syed Hassan ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Mean Squared Error ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Percentage Error ◽  
Model Estimation ◽  
Estimation Model ◽  
Radiation Data ◽  
Squared Error ◽  
Hargreaves Model

This paper presents the forecasting of solar radiation in Kelantan, Eastern Malaysia for the year of 2011 using Hargreaves model. This estimation is based on latitude and daily minimum and maximum temperature in Kelantan. The measured and estimated solar radiation data were compared for the year 2011 and analyzed using coefficient of residual mass (CRM), root mean squared error (RMSE), coefficient of determination (R2) and percentage error (e). The results showed that the value ofCRMis 0.09, it indicates the tendency of the estimation model to under-estimate the measure solar radiation. Meanwhile, the value ofRMSEis 8.21% and the value ofR2is 0.8661, closed to 1 indicates that about 86.61% of the total variation is explained in the data. For thee, the value is 7.98%, it indicates that the model estimation is good.

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