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Dust Level Forecasting and its Interaction with Gaseous Pollutants Using Artificial Neural Network: A Case Study for Kermanshah, Iran

A.A. Zinatizadeh
2014 Iranica Journal of Energy & Environment  
Robustness of constructed ANN acknowledged and the effects of variation of input parameters were investigated. As a result, dust had a decreasing impact on the gaseous pollutants level.  ...  The prediction tests showed that the ANN models used in this study have the high potential of forecasting dust storm occurrence in the region studied by using conventional meteorological variables.  ...  The forecasting of such phenomena with up to two constructed ANN investigates and the effects of variation via historic data [5] .  ... 
doi:10.5829/idosi.ijee.2014.05.01.08 fatcat:ajm6tfqpyfgk7bo6vm4yqabjg4

Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki

Dimitris Voukantsis, Kostas Karatzas, Jaakko Kukkonen, Teemu Räsänen, Ari Karppinen, Mikko Kolehmainen
2011 Science of the Total Environment  
Compared with previous corresponding studies, these statistical parameters indicate an improved performance of air quality parameters forecasting.  ...  Then, we proceeded with the development of air quality forecasting models for both studied areas.  ...  Acknowledgements This study has been conducted in the frame of the scientific collaboration action COST ES0602 "Towards a European Network on Chemical Weather Forecasting and Information Systems" and the  ... 
doi:10.1016/j.scitotenv.2010.12.039 pmid:21276603 fatcat:dujkaqnzdbaddcq6vd53qr4dty

Machine learning and artificial intelligence to aid climate change research and preparedness

Chris Huntingford, Elizabeth S Jeffers, Michael B Bonsall, Hannah M Christensen, Thomas Lees, Hui Yang
2019 Environmental Research Letters  
Although a considerable number of isolated Earth System features have been analysed with ML techniques, more generic application to understand better the full climate system has not occurred.  ...  While ESM development is of paramount importance, we suggest a parallel emphasis on utilising ML and AI to understand and capitalise far more on existing data and simulations.  ...  Data statement The data that support the findings of this study are available from the corresponding author upon reasonable request.  ... 
doi:10.1088/1748-9326/ab4e55 fatcat:emxje2t7oje5nlogutbn5osg4a

Soft Computing Techniques in combating the complexity of the atmosphere- a review [article]

Surajit Chattopadhyay
2006 arXiv   pre-print
The purpose of the present review is to discuss the role of Soft Computing techniques in understanding the complexity associated with atmospheric phenomena and thus developing predictive models.  ...  Problems in atmospheric data analysis are discussed in brief and the relevance of Soft Computing to the atmospheric data analysis and their advantage over the conventional methods are also conversed.  ...  Wong et al (1999) constructed fuzzy rule bases with the aid of SOM and backpropagation neural networks and then with the help of the rule base developed predictive model for rainfall over Switzerland  ... 
arXiv:nlin/0608052v1 fatcat:jzyzqf7xajdava527ri3g473ii

Decision Tree Based Rainfall Prediction Model with Data Driven Model using Multiple Linear Regression

2018 Advances in Natural and Applied Sciences  
Meteorological data mining is a form of data mining concerned with finding hidden patterns inside largely available meteorological data, so that the information retrieved can be transformed into usable  ...  In this paper, here use data mining technique in forecasting monthly Rainfall of Tamil Nadu. This was carried out using traditional statistical technique -Multiple Linear Regression.  ...  These data were then quality controlled and assimilated with a data assimilation system kept unchanged over the reanalysis period.  ... 
doi:10.22587/anas.2018.12.6.3 fatcat:36u3dsmkundj7dhdtufuovwg3e

Weather forecasting using DBSCAN clustering algorithm

Aida Chefrour
2022 Annales Mathematicae et Informaticae  
The main objective of this study is the clustering of meteorological parameters and forecasting weather in the region of Annaba (Algeria) using clustering techniques.  ...  Our experiments identified five groups, each of which was associated with the area's usual weather parameters.  ...  The author would like to thank the DGRSDT (General Directorate of Scientific Research and Technological Development) -MESRS (Ministry of Higher Education and Scientific Research), ALGERIA, for the financial  ... 
doi:10.33039/ami.2022.05.001 fatcat:ibvqbxy2srgztk4dgjo552kphq

A Review of Weather Forecasting Using Data Mining Techniques

Ms.P. Shivaranjani
2016 International Journal Of Engineering And Computer Science  
The rainfall is the fragment of the agriculture and unable to understand the monsoon condition, predicating the crop yield and the soil fertility.  ...  Data mining is the techniques used to extract the knowledge from the set of data.This paper provides a survey of different data mining techniques being used in weather prediction or forecasting which helps  ...  Weather forecasting application is an art of science and technology use to the state of atmosphere for a location.  ... 
doi:10.18535/ijecs/v5i12.77 fatcat:5euhof42qjdrhidz3uvrusrcgm

Seasonal Rainfall Prediction in Lagos, Nigeria Using Artificial Neural Network

Adigun Paul Ayodele, Ebiendele Eromosele Precious
2019 Asian Journal of Research in Computer Science  
The complexity of the atmospheric processes that generate rainfall makes quantitative forecasting of rainfall an extremely, difficult task.  ...  amount indicated as follows; MSE, RMSE, and MAE were 7174, 84.7 and 18.6 respectively with a high statistical coefficient of variation of 94% when the ANN model prediction is validated with the observed  ...  The forecasting of the rainfall distribution spatially and temporally is important for water quality and quantity management [5] .  ... 
doi:10.9734/ajrcos/2019/v3i430100 fatcat:wugygnnwwrdndduxynmvhh64pi

Hybrid Based Artificial Intellegence Short –Term Load Forecasting

Kayode O. Adebunmi, Temilola M. Adepoju, Gafari A. Adepoju, Akeem O. Bisiriyu
2021 Journal of Engineering Research and Reports  
The ANFIS model outperforms the other models with least errors of RMSE and MAE of 2.2198% and 1.7932% respectively.  ...  Since the statistical method is a linear model, and the load and meteorological parameters have a nonlinear relationship, the statistical method for load forecasting involves a great calculation time for  ...  Artificial intelligence is an approach that, in an atmosphere of ambiguity and imprecision, resembles the extraordinary capacity of the human mind to think and understand.  ... 
doi:10.9734/jerr/2021/v20i617330 fatcat:etsnm4bdk5cprgfpea5vsato5y

A Critical Review on Artificial Intelligence Models in Hydrological Forecasting How Reliable are Artificial Intelligence Models

Dibie Chidubem Damian, University of Johannesburg
2019 International Journal of Engineering Research and  
a better clarity of the event in order to carry out a better simulation and subsequently forecasting.  ...  However, the description of these hydrological processes has been hampered by lack of access to data over a variety of parameters for a long period and the issues of missing data as well.  ...  Seo et al. (2016) also used the ANN model to simulate previous and current parameters of water quality in order forecast a step ahead water quality parameters of Cheongpyeong dam and the results of different  ... 
doi:10.17577/ijertv8is070123 fatcat:bxpn52m6nbamlc4tk5yicvxxqy

Systematic Approach for the Prediction of Ground-Level Air Pollution (around an Industrial Port) Using an Artificial Neural Network

Mahad S. Baawain, Aisha S. Al-Serihi
2014 Aerosol and Air Quality Research  
The results show very good agreement between the actual and predicted concentrations, as the values of the coefficient of multiple determinations (R 2 ) for all ANN models exceeded 0.70.  ...  The training of the models is based on the multi-layer perceptron (MLP) method with the Back-Propagation (BP) algorithm.  ...  of current ANN models into indices using the Air Quality Index in order to aid the general public with simple understanding of air quality information. • Explore the capability of ANN models in predicting  ... 
doi:10.4209/aaqr.2013.06.0191 fatcat:poydpvsgandd5l3igt3rqeeivu

Nonlinear Autoregressive Neural Network for Antimicrobial Waste Water Treatment

Anwer Mustafa Hilal, Mashael M. Asiri, Shaha Al-Otaibi, Faisal Mohammed Nafie, Amal Al-Rasheed, Mohammed Rizwanullah, Ishfaq Yaseen, Abdelwahed Motwakel, Jeevan Kumar Reddy Modigunta
2022 Adsorption Science & Technology  
Dirichlet design parameters and a combined combination of Neumann and Dirichlet boundary situation are applied to the system of differential equations.  ...  , an exogenous (NARX) neural network model with two activation functions was used (Log-sigmoid and hyperbolic tangent) and for both the findings of a TC and SMX absorption simulations showed the random  ...  Acknowledgments The authors extend their appreciation to the  ... 
doi:10.1155/2022/6292200 fatcat:urvwx53plreizawfxwqu6igc3i

Meteorological Research Needs for Improved Air Quality Forecasting: Report of the 11th Prospectus Development Team of the U.S. Weather Research Program*

Walter F. Dabberdt, Mary Anne Carroll, Darrel Baumgardner, Gregory Carmichael, Ronald Cohen, Tim Dye, James Ellis, Georg Grell, Sue Grimmond, Steven Hanna, John Irwin, Brian Lamb (+7 others)
2004 Bulletin of The American Meteorological Society - (BAMS)  
Air quality forecasting in the presence of clouds is difficult because of the multiple, nonlinear ways in which clouds interact with other parameters that af-fect traditional AQ.  ...  Effective emergency-response forecasting helps organizations better understand and manage the consequences of accidental or intentional releases of hazardous material into the atmosphere.  ... 
doi:10.1175/bams-85-4-563 fatcat:64wuxlfzgbdglci4uz5ulwwyvu

Support vector machine applications in the field of hydrology: A review

Sujay Raghavendra. N, Paresh Chandra Deka
2014 Applied Soft Computing  
Furthermore, this review provides a brief synopsis of the techniques of SVMs and other emerging ones (hybrid models), which have proven useful in the analysis of the various hydrological parameters.  ...  In the recent few decades there has been very significant developments in the theoretical understanding of Support vector machines (SVMs) as well as algorithmic strategies for implementing them, and applications  ...  The performance of SVM modelling was compared with that obtained from ANN models and the SVM models outperformed the ANN predictions in soil moisture forecasting. Wei et al.  ... 
doi:10.1016/j.asoc.2014.02.002 fatcat:gkcq34t4mnhibcotphbrnbusba

Artificial Neural Network Model in Prediction of Meteorological Parameters during Premonsoon Thunderstorms

A. J. Litta, Sumam Mary Idicula, U. C. Mohanty
2013 International Journal of Atmospheric Sciences  
Forecasting thunderstorm is one of the most difficult tasks in weather prediction, due to their rather small spatial and temporal extension and the inherent nonlinearity of their dynamics and physics.  ...  The developed model can be useful in decision making for meteorologists and others who work with real-time thunderstorm forecast.  ...  The authors express sincere thanks to the reviewer for providing invaluable suggestions to improve this paper.  ... 
doi:10.1155/2013/525383 fatcat:4sidg426snc2bbxk7ove4yhbru
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