scholarly journals Climatology at any point : A neural network solution

MAUSAM ◽  
2021 ◽  
Vol 64 (2) ◽  
pp. 231-250
Author(s):  
PULAK GUHATHAKURTA ◽  
AJIT TYAGI ◽  
B. MUKHOPADHYAY
Keyword(s):  
Neural Network ◽  
Atmospheric Pressure ◽  
Spatial Interpolation ◽  
Meteorological Parameters ◽  
Geographical Information ◽  
Maximum Temperature ◽  
Long Period ◽  
The Neural Network ◽  
Past Data ◽  
Advance Knowledge

lHkh mi;ksxdrkZvksa] ;kstuk cukus okyksa] vkink izca/ku dkfeZdksa] i;ZVu vkfn }kjk rkieku] vf/kdre rkieku] U;wure rkieku] ok;qeaMyh; nkc] o"kkZ vkfn tSls ekSle izkpyksa dh tyok;q foKku ij lwpukvksa dh mUur tkudkjh dh vR;kf/kd ek¡x jgh gSA fdlh LFkku fo'ks"k esa os/k’kkyk ds vHkko vkSj dHkh&dHkh nh?kZ vof/k ds igys ds vk¡dM+ksa dh vuqiyC/krk ds dkj.k ekSle foKku leqnk; ml LFkku fo’ks"k ds fy, visf{kr lwpukvksa dks miyC/k ugha djk ikrk gSA bl 'kks/k i= esa LFkkfud varosZ’ku ds {ks= esa U;wjy usVodZ ds rqyukRed u, vuqiz;ksx crk, x, gSaA iwjs ckjg eghuksa ds vf/kdre vkSj U;wure nksuksa rkiekuksa  ds fy, U;wjy usVodZ varosZ’ku fun’kZ fodflr fd, x, gSaA ;g ekWMy ml LFkku fo’ks"k ij lkekU; vf/kdre vkSj U;wure rkiekuksa dks rS;kj djus ds fy, lwpukvksa ds :i  esa v{kka’k] ns’kkUrj vkSj mUu;u tSlh HkkSxksfyd lwpukvksa dk mi;ksx djrk gSA varosZ’ku ds fy, LFkkfud ekWMyksa ds fu"iknuksa dh rqyuk vU; lkekU;r% iz;qDr i)fr ds lkFk dh xbZ gSA Advance knowledge of information on  climatology of meteorological parameters like temperature, maximum temperature, minimum temperature, atmospheric pressure, rainfall etc are of great demands from all the users, planners, disaster managements personals, tourism etc. The information is required at the place concerned but this cannot be fulfilled by the meteorological community due to absent of observatory at that place and also some time absent of past data of long period. The present paper has described a comparatively new application of the neural network in the field of spatial interpolation. Neural network interpolation models are developed for both maximum and minimum temperatures for all the twelve months. The model utilizes geographical information like latitude, longitude and elevation as inputs to generate normal maximum and minimum temperatures at a place. The performances of the models are compared with the other commonly used method for spatial interpolation.  

scholarly journals Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2488
Author(s):  
Olubayo M. Babatunde ◽  
Josiah L. Munda ◽  
Yskandar Hamam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Meteorological Parameters ◽  
Production Systems ◽  
Global Solar Radiation ◽  
Maximum Temperature ◽  
Remote Communities ◽  
Artificial Neural ◽  
Solar Powered

The use of solar powered systems is gradually getting more attention due to technological advances as well as cost effectiveness. Thus, solar powered systems like photovoltaic, concentrated solar power, concentrator photovoltaics, as well as hydrogen production systems are now commercially available for electricity generation. A major input to these systems is solar radiation data which is either partially available or not available in many remote communities. Predictive models can be used in estimating the amount and pattern of solar radiation in any location. This paper presents the use of evolutionary algorithm in improving the generalization capabilities and efficiency of multilayer feed-forward artificial neural network for the prediction of solar radiation using meteorological parameters as input. Meteorological parameters which included monthly average daily of: sunshine hour, solar radiation, maximum temperature and minimum temperature were used in the evaluation. Results show that the proposed model returned a RMSE of 1.1967, NSE of 0.8137 and R 2 of 0.8254.

scholarly journals Spatio-Temporal Modeling of Ozone Distribution in Tehran, Iran Based on Neural Network and Geographical Information System

2022 ◽  
Author(s):  
Leila Sherafati ◽  
Hossein Aghamohammadi Zanjirabad ◽  
Saeed Behzadi
Keyword(s):  
Neural Network ◽  
Information System ◽  
Geographical Information System ◽  
Meteorological Parameters ◽  
Interpolation Method ◽  
Temporal Analysis ◽  
Geographical Information ◽  
Temporal Modeling ◽  
Spatiotemporal Model ◽  
Ozone Distribution

Background: Air pollution is one of the most important causes of respiratory diseases that people face in big cities today. Suspended particulates, carbon monoxide, sulfur dioxide, ozone, and nitrogen dioxide are the five major pollutants of air that pose many problems to human health. We aimed to provide an approach for modeling and analyzing the spatiotemporal model of ozone distribution based on Geographical Information System (GIS). Methods: In the first step, by considering the accuracy of different interpolation methods, the Inverse distance weighted (IDW) method was selected as the best interpolation method for mapping the concentration of ozone in Tehran, Iran. In the next step, according to the daily data of Ozone pollutants, the daily, monthly, and annual mean concentrations maps were prepared for the years 2015, 2016, and 2017. Results: Spatial and temporal analysis of the distribution of ozone pollutants in Tehran was performed. The highest concentrations of O3 are found in the southwest and parts of the central part of the city. Finally, a neural network was developed to predict the amount of ozone pollutants according to meteorological parameters. Conclusion: The results show that meteorological parameters such as temperature, velocity and direction of the wind, and precipitation are influential on O3 concentration.

scholarly journals The Integration of Linguistic and Geospatial Features Using Global Context Embedding for Automated Text Geocoding

2021 ◽  
Vol 10 (9) ◽  
pp. 572
Author(s):  
Zheren Yan ◽  
Can Yang ◽  
Lei Hu ◽  
Jing Zhao ◽  
Liangcun Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Classification Problem ◽  
Geographical Information ◽  
Local Context ◽  
Place Names ◽  
Global Features ◽  
Global Context ◽  
The Neural Network ◽  
Textual Data ◽  
Local And Global Features

Geocoding is an essential procedure in geographical information retrieval to associate place names with coordinates. Due to the inherent ambiguity of place names in natural language and the scarcity of place names in textual data, it is widely recognized that geocoding is challenging. Recent advances in deep learning have promoted the use of the neural network to improve the performance of geocoding. However, most of the existing approaches consider only the local context, e.g., neighboring words in a sentence, as opposed to the global context, e.g., the topic of the document. Lack of global information may have a severe impact on the robustness of the model. To fill the research gap, this paper proposes a novel global context embedding approach to generate linguistic and geospatial features through topic embedding and location embedding, respectively. A deep neural network called LGGeoCoder, which integrates local and global features, is developed to solve the geocoding as a classification problem. The experiments on a Wikipedia place name dataset demonstrate that LGGeoCoder achieves competitive performance compared with state-of-the-art models. Furthermore, the effect of introducing global linguistic and geospatial features in geocoding to alleviate the ambiguity and scarcity problem is discussed.

scholarly journals MODELING SOLAR POTENTIAL IN SEMARANG, INDONESIA USING ARTIFICIAL NEURAL NETWORKS

2021 ◽  
pp. 1-8
Author(s):  
Djoko Adi Widodo ◽  
Purwanto Purwanto ◽  
Hermawan Hermawan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Energy ◽  
Solar Irradiance ◽  
Input Data ◽  
Geographical Information ◽  
Maximum Temperature ◽  
Percentage Error ◽  
Data Set ◽  
Artificial Neural

Artificial neural network shows a good performance in predicting renewable energy. Many versions of Artificial Neural Network (ANN) models have been implemented to predict solar potential. This study aims to determine the monthly solar radiation in Semarang, Indonesia using ANN, and to visualize monthly solar irradiance as a map of the solar system of Semarang. This research applied the perceptron multi-layer ANN model, with 7 variables as input data of network learning, which were maximum temperature, relative humidity, wind speed, rainfall, longitude, latitude, and elevation. The input data set was obtained from a NASA normalized geo-satellite database website with a 5-year average daily score. Network training used backpropagation with one of the input layers, two of hidden layers, and one of the output layer. The performance of the model during the analysis of mean absolute percentage error was highly accurate (6.6%) when 12 and 10 neurons were respectively installed in the first and second hidden layers. The result was presented in a monthly map of solar potential within the geographical information system (GIS) environment. The result showed that ANN was able to be one of the alternatives to estimate solar irradiance data. The sun irradiance map can be used by the government of Semarang City to provide information about the solar energy profile for the implementation of the solar energy system. 

scholarly journals Seasonal and Weather Effects on Rheumatoid Arthritis: Myth or Reality?

2020 ◽  
Vol 2020 ◽  
pp. 1-5
Author(s):  
Hamida Azzouzi ◽  
Linda Ichchou
Keyword(s):  
Rheumatoid Arthritis ◽  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Atmospheric Pressure ◽  
Meteorological Parameters ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Maximum Temperature ◽  
Tender Joint Count

Introduction. Many of our rheumatic patients report that weather and seasons affect their symptoms. Objective. The purpose of this study was to examine the effect of meteorological parameters within seasons on rheumatoid arthritis (RA) symptoms. Methods. A retrospective longitudinal study from July 2017 to August 2018 was conducted. Data from three consultations for three seasons were collected and included a tender and swollen joint count, a disease activity score for 28 joints (DAS28), and patient’s pain assessment from their computerized medical record. The weather conditions (minimum and maximum temperature, precipitation, humidity, atmospheric pressure, and wind speed) registered during the same day of consultation for each patient were obtained. Then, the statistical correlation between each meteorological parameter and RA parameters was determined using the multiple linear regression analysis. Results. The data of 117 patients with a mean age of 50.45 ± 12.17 years were analyzed. The mean DAS28 at baseline was 2.44 ± 0.95. The winter in Oujda is cold (average temperature between 10°C and15°C) compared to summer (24.5°C–32.7°C). The spring is wetter with a 71% average humidity. Overall, the tender joint count was significantly correlated with hygrometry (p=0.027) in winter. A similar result was obtained in summer with precipitation (p=0.003). The pain intensity in the summer was negatively correlated with minimum temperatures and atmospheric pressure. However, there was no correlation between meteorological parameters and disease objective parameters for all seasons. Multiple linear regression analysis showed that weather parameters appeared to explain the variability in four RA predictors in the summer. No significant associations were observed in the spring. Conclusion. Our study supported the physicians’ assumption regarding the effect of climate on pain in RA patients.

scholarly journals Decadal trends in climate over India

MAUSAM ◽  
2021 ◽  
Vol 43 (1) ◽  
pp. 7-20
Author(s):  
H.N. SRIVASTAVA ◽  
B.N. DEWAN ◽  
S. K. DIKSHIT ◽  
G. S. PRAKASH RAO ◽  
S.S. SINGH ◽  
...  
Keyword(s):  
Atmospheric Pressure ◽  
Meteorological Parameters ◽  
Warming Trend ◽  
Annual Rainfall ◽  
Maximum Temperature ◽  
Data Series ◽  
Temperature Minimum ◽  
Decadal Variations ◽  
Almost All ◽  
Temperature Surface

Decadal variations of meteorological parameters, vig, temperature (surface air maximum temperature, minimum temperature and upper air up to middle troposphere), station level pressure and seasonal and annual rainfall are studied for the period 1901 to 1986 (upper air data available from 1951 onwards), Tests of significance applied to data series (stationwise as well as country as a whole) show that the temperatures are showing a decreasing trend in almost all the northern parts of the country (north of 23" N) and a rising trend in southern parts (south of 23"N), For the country as a whole, however, there is a small warming trend Atmospheric pressure shows a fall between second and third decades but does not indicate any significant change after 1930, Decadal analysis of seasonal (Jun-Sep) and annual rainfall indicates that the variations in rainfall are within the statistical limits.

Neural Network for Automatic Analysis of Motility Data

1994 ◽  
Vol 33 (01) ◽  
pp. 157-160 ◽  
Author(s):  
S. Kruse-Andersen ◽  
J. Kolberg ◽  
E. Jakobsen
Keyword(s):  
Neural Network ◽  
Muscular Activity ◽  
Recording System ◽  
Automatic Analysis ◽  
Biologically Relevant ◽  
System A ◽  
The Neural Network ◽  
Valuable Method ◽  
Motor Abnormalities ◽  
Trained Network

Abstract:Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neu-rocomputing has potential advantages for automatic analysis of gastrointestinal motility data.

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

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