scholarly journals Application of Soft Computing (ANN) Techniques to study the relationship between Solar Activity Features and Total Column Ozone

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Seema Pande ◽  
Mahesh Chandra Mathpal ◽  
Bimal Pande
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Activity ◽  
Solar Flares ◽  
Total Column Ozone ◽  
Active Prominence ◽  
Artificial Neural Network Ann ◽  
Column Ozone ◽  
The Relationship ◽  
Total Column

Using 30 years data (1986-2015) we have made an attempt to study the dependency of total column ozone (TCO) on solar activity features: solar flares (SF), solar active prominence (SAP) and sunspot numbers (SN) for two hill stations of Uttarakhand viz. Nainital (29.40 N.79.470E) and Mussorie (30.270 N 78.060 E) by Artificial neural network (ANN) technique. Our study supports the fact that solar activity features contribute to the production of ozone.

scholarly journals Application of Soft Computing (ANN) Techniques to study the relationship between Solar Activity Features and Total Column Ozone

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Seema Pande ◽  
Mahesh Chandra Mathpal ◽  
Bimal Pande
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Activity ◽  
Solar Flares ◽  
Total Column Ozone ◽  
Active Prominence ◽  
Artificial Neural Network Ann ◽  
Column Ozone ◽  
The Relationship ◽  
Total Column

Using 30 years data (1986-2015) we have made an attempt to study the dependency of total column ozone (TCO) on solar activity features: solar flares (SF), solar active prominence (SAP) and sunspot numbers (SN) for two hill stations of Uttarakhand viz. Nainital (29.40 N.79.470E) and Mussorie (30.270 N 78.060 E) by Artificial neural network (ANN) technique. Our study supports the fact that solar activity features contribute to the production of ozone

scholarly journals Solving multiple linear regression problem using artificial neural network

2022 ◽  
Vol 12 (1) ◽  
pp. 770
Author(s):  
Mohammad S. Khrisat ◽  
Ziad A. Alqadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Problem ◽  
Simple Method ◽  
Scientific Experiments ◽  
Linear Regression Problem ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Relationship ◽  
Linear Regressions

<span>Multiple linear regressions are an important tool used to find the relationship between a set of variables used in various scientific experiments. In this article we are going to introduce a simple method of solving a multiple rectilinear regressions (MLR) problem that uses an artificial neural network to find the accurate and expected output from MLR problem. Different artificial neural network (ANN) types with different architecture will be tested, the error between the target outputs and the calculated ANN outputs will be investigated. A recommendation of using a certain type of ANN based on the experimental results will be raised.</span>

Minimum Input Variances for Modelling Rainfall-runoff Using ANN

2014 ◽  
Vol 69 (3) ◽  
Author(s):  
Zulkarnain Hassan ◽  
Supiah Shamsudin ◽  
Sobri Harun
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Conceptual Models ◽  
Data Series ◽  
Rainfall Runoff ◽  
Antecedent Rainfall ◽  
Runoff Series ◽  
Artificial Neural Network Ann ◽  
The Relationship

This paper presents the study of possible input variances for modeling the long-term runoff series using artificial neural network (ANN). ANN has the ability to derive the relationship between the inputs and outputs of a process without the physics being provided to it, and it is believed to be more flexible to be used compared to the conceptual models [1]. Data series from the Kurau River sub-catchment was applied to build the ANN networks and the model was calibrated using the input of rainfall, antecedent rainfall, temperature, antecedent temperature and antecedent runoff. In addition, the results were compared with the conceptual model, named IHACRES. The study reveal that ANN and IHACRES can simulate well for mean runoff but ANN gives a remarkable performance compared to IHACRES, if the model customizes with a good configuration.  

Examining the Value, Satisfaction, and Loyalty Relationship Under Online Framework Using PLS-ANN

2021 ◽  
pp. 2224-2250
Author(s):  
Himanshu Sharma ◽  
Anu G. Aggarwal ◽  
Abhishek Tandon
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Artificial Neural Network ◽  
Customer Value ◽  
Moderating Effect ◽  
Competitive Advantages ◽  
Effect Relationship ◽  
Second Stage ◽  
Artificial Neural Network Ann ◽  
The Relationship

Electronic commerce has gained popularity over the years and forced the businesses to go online in order to achieve competitive advantages. Digitalization has led to the dynamicity of markets and this poses a question on the loyalty of customers towards these e-commerce websites.This chapter attempts to examine the relationship between website service quality (WSQ), social media reviews (SMR), customer value (CV), customer satisfaction (CS), and website loyalty (LOY); with the moderating effect of switching cost. Also, the most influential predictor in each cause-effect relationship is determined. The hypothesized conceptual framework is validated using a two-stage approach. In the first stage, the hypotheses are tested and path loadings are generated using PLS-SEM approach. Also, the moderating effect is studied using PLS. Second stage utilizes the advantages of artificial neural network (ANN) to obtain the best explanatory variable in each independent-dependent association.

scholarly journals An artificial neural network technique for downscaling GCM outputs to RCM spatial scale

2011 ◽  
Vol 18 (6) ◽  
pp. 1013-1028 ◽  
Author(s):  
R. Chadwick ◽  
E. Coppola ◽  
F. Giorgi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regional Climate ◽  
Correction Method ◽  
Lapse Rate ◽  
Climate Modelling ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Relationship

Abstract. An Artificial Neural Network (ANN) approach is used to downscale ECHAM5 GCM temperature (T) and rainfall (R) fields to RegCM3 regional model scale over Europe. The main inputs to the neural network were the ECHAM5 fields and topography, and RegCM3 topography. An ANN trained for the period 1960–1980 was able to recreate the RegCM3 1981–2000 mean T and R fields with reasonable accuracy. The ANN showed an improvement over a simple lapse-rate correction method for T, although the ANN R field did not capture all the fine-scale detail of the RCM field. An ANN trained over a smaller area of Southern Europe was able to capture this detail with more precision. The ANN was unable to accurately recreate the RCM climate change (CC) signal between 1981–2000 and 2081–2100, and it is suggested that this is because the relationship between the GCM fields, RCM fields and topography is not constant with time and changing climate. An ANN trained with three ten-year "time-slices" was able to better reproduce the RCM CC signal, particularly for the full European domain. This approach shows encouraging results but will need further refinement before becoming a viable supplement to dynamical regional climate modelling of temperature and rainfall.

scholarly journals Statistical Study of Solar Forcing of Total Column Ozone Variation Over Three Cities in Kenya

2016 ◽  
Vol 8 (3) ◽  
pp. 77
Author(s):  
Carolyne M. M. Songa ◽  
Jared H. O. Ndeda ◽  
Gilbert Ouma
Keyword(s):  
Solar Activity ◽  
Sunspot Number ◽  
Correlation Coefficients ◽  
Total Column Ozone ◽  
Solar Activity Indices ◽  
Multiple Correlation Coefficients ◽  
Highly Correlated ◽  
Column Ozone ◽  
Total Column ◽  
Activity Indices

In this study, a statistical analysis between three solar activity indices (SAI) namely; sunspot number (ssn), F10.7 index (sf) and Mg II index (mg) and total column ozone (TCO) time series over three cities in Kenya namely; Nairobi (1.17º S; 36.46º E), Kisumu (0.03º S; 34.45º E) and Mombasa (4.02º S; 39.43º E) for the period 1985 - 2011 are considered. Pearson and cross correlations, linear and multiple regression analyses are performed. All the statistical analyses are based on 95% confidence level. SAI show decreasing trend at significant levels with highest decrease in international sunspot number and least in Mg II index. TCO are highly correlated with each other at (0.936&lt; r &lt; 0.955, p &lt; 0.001). SAI are also highly correlated with each other at (0.941&lt; r &lt; 0.976, p &lt; 0.001) and are significantly positively correlated with TCO over the study period except Mg II index at Kisumu. TCO and SAI have correlations at both long and short lags. At all the cities, F10.7 index has an immediate impact and Mg II index has a delayed impact on TCO. A linear relationship exists between the two variables in all the cities. An increase in TCO of about 2 – 3 % (Nairobi), 1 – 2% (Kisumu) and 3 – 4 % (Mombasa) is attributed to solar activity indices. The multiple correlation coefficients and significant levels obtained show that 3 – 5% of the TCO at Nairobi, Kisumu and Mombasa can be predicted by the SAI.

Solar activity-total column ozone relationships: Observations and model studies with heterogeneous chemistry

1997 ◽  
Vol 102 (D1) ◽  
pp. 1561-1569 ◽  
Author(s):  
C. S. Zerefos ◽  
K. Tourpali ◽  
B. R. Bojkov ◽  
D. S. Balis ◽  
B. Rognerund ◽  
...  
Keyword(s):  
Solar Activity ◽  
Heterogeneous Chemistry ◽  
Total Column Ozone ◽  
Model Studies ◽  
Column Ozone ◽  
Total Column

scholarly journals Ozone layer adaptive model from direct relationship between solar activity and total column ozone for the tropical equator-Andes-Colombian region

Atmósfera ◽  
2018 ◽  
Vol 31 (2) ◽  
pp. 155-164 ◽  
Author(s):  
Julio César González-Navarrete ◽  
◽  
Julián Salamanca ◽  
Ingrid Mónica Pinzón-Verano ◽  
◽  
...  
Keyword(s):  
Solar Activity ◽  
Direct Relationship ◽  
Ozone Layer ◽  
Adaptive Model ◽  
Total Column Ozone ◽  
Column Ozone ◽  
Total Column

scholarly journals Prediction of water discharge and sediment in teak forested area using artificial neural network model

2017 ◽  
Vol 6 (1) ◽  
pp. 73
Author(s):  
Nining Wahyuningrum
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Discharge ◽  
Ann Modeling ◽  
Highly Nonlinear ◽  
Condition Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Relationship Of ◽  
The Relationship

Information on the relationship of rainfall with discharge and sediment are required in watershed management.This relationship is known to be highly nonlinear and complex. Although discharge and sediment has been monitored continuously, but sometimes the information is not or less complete. In this condition, modeling is indispensable.The research objective is to create a model to predict the monthly direct runoff and sediment using Artificial Neural Network (ANN).The model was tested using rainfall data at t-3 and t-4 as input, and discharge and sediment at t+3 and t+4 as output. The data used is the data from 2001 to 2014. The results showed that of some models tested there are two models for the prediction of discharge and two models for sediment.The model was chosen because it has the smallest MSE, the largest R2 and satisfying K (0.5 to 0.65).Thus, these models can be used to predict discharge andsediment for a period of t+3 and t+4. Prediction of discharge of t+3 and t+4 may use Q t+3 = 0,64 Q t-3 + 0,05 and Q t+4 = 0,65 Q t-4 + 0,074 res pectively, while for predicting sediment of t+3 and t+4 may use equations QS t+3 = 0,45 QS t-3 + 0,052 and QS t+4 = 0,45 QS t-4 + 0,052. This ANN modeling can be applied to predict the flow and sediment in other locations with an architecture adapted to the conditions of available data.

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