Weather Data Transmission Driven by Artificial Neural Network

Nowadays, the trend of big data that can be describe as a massive volume and unstructured data have become more complicated because of its difficulty to be process using traditional database and software techniques. Due to increase in size of data, there also demand for big bandwidth for the transmission of big data. Data transmission is important in communication in providing information at different location. In this project we focus on big data transmission in the context of weather data. Weather data is important for meteorologist as it helps them to make weather prediction. Real time weather prediction is really important as it would help in making quick decision to react with the environment and planning for our daily activities. The purpose of this project is to develop a real time and low bandwidth usage for weather data transmission driven by an artificial neural network perform weather forecast using Adaptive Forecasting Model. This project seeks an application context offshore because the data transmission from offshore to onshore is very costly and requires high usage of network bandwidth. Other than that, offshore weather can change rapidly and cause offshore activity to be delayed. This research is important as it would help in making faster decision for oil and gas daily activity and avoid loss of human life in natural disaster in the fastest way. In this research we propose a neural network based prediction to control weather data transmission. This system mainly has several parts which consist of data gathering module, neural network prediction module and transmission module. The data gathering module is developed using ODroid weatherboard sensor while the prediction module is developed using an artificial neural network technique and algorithm to make prediction. The transmission module is based on TCP/IP protocol. The prediction module works by using the data captured by sensor to be processed using neural network prediction. If the neural network predict major changes in weather condition or detect bad weather, it will send the weather data and result to the server so that the data can be published and alarm meteorologist and people on the oil platform to avoid loss of human life or another contingency plan on the current oil and gas activity. But if there are no major changes in weather prediction result, the module will only update the server on a daily basis. In this project, we consider to capture atmospheric pressure, temperature, humidity, visibility, Infrared index, altitude and UV index. Real time processing and transmission weather data will show improvement in predicting weather and saving the network bandwidth transmission.

[1]  Erhan Şener,et al.  Modeling of Water Temperature Based on GIS and ANN Techniques: Case Study of Lake Egirdir (Turkey) , 2012 .

[2]  Kil To Chong,et al.  A Real-Time Multimedia Data Transmission Rate Control Using a Neural Network Prediction Algorithm , 2005, APWeb.

[3]  K. Danisman,et al.  Modelling of the hysteresis effect of target voltage in reactive magnetron sputtering process by using neural networks , 2009 .

[4]  A neural network to pulmonary embolism aided diagnosis with a feature selection approach , 2010, 2010 3rd International Conference on Biomedical Engineering and Informatics.

[5]  Kamran Pazand,et al.  Using Artificial Neural Networks in Investigations on the Effects of Ultrasonic Vibrations on the Extrusion Process , 2011 .

[6]  P. Melin,et al.  Neural Network optimization with a hybrid evolutionary method that combines Particle Swarm and Genetic Algorithms with fuzzy rules , 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.

[7]  Ahmad Zia Ul-Saufie,et al.  Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA) , 2013 .

[8]  Berndt Müller,et al.  Neural networks: an introduction , 1990 .

[9]  José A. Revilla,et al.  USE OF NEURAL NETWORKS IN DESIGN OF COASTAL SEWAGE SYSTEMS , 1998 .

[10]  Wu Qiong,et al.  Mean particle size prediction in rock blast fragmentation using neural networks , 2010 .

[11]  J. Olsson,et al.  Identification of typical synoptic patterns causing heavy rainfall in the rainy season in Japan by a Self-Organizing Map , 2007 .

[12]  Masoud Rahimi,et al.  CFD and artificial neural network modeling of two-phase flow pressure drop☆ , 2009 .

[13]  Sung Yoo,et al.  Neural network prediction model for a real-time data transmission , 2006, Neural Computing & Applications.

[14]  Moustafa Elshafei,et al.  Support Vector Regression and Functional Networks for viscosity and Gas/Oil Ratio Curves Estimation , 2011, Int. J. Comput. Intell. Appl..

[15]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[16]  Guanghui Su,et al.  Applications of ANNs in flow and heat transfer problems in nuclear engineering: A review work , 2013 .

[17]  Epaminondas Sidiropoulos,et al.  Assessment of sediment transport approaches for sand-bed rivers by means of machine learning , 2015 .

[18]  I. S. Nashawi,et al.  Using Neural Networks for Prediction of Formation Fracture Gradient , 2000 .

[19]  A. P. Clark,et al.  Principles of digital data transmission , 1976 .

[20]  M. A. Al-Marhoun,et al.  Evaluation Of Below Bubble Point Viscosity Correlations & Construction of a New Neural Network Model , 2007 .

[21]  Magdi S. Mahmoud.  Control and Estimation Methods over Communication Networks , 2014 .

[22]  Christopher J. Bean,et al.  Application of Artificial Neural Networks (ANNs) to Sub-basalt Imaging. , 2000 .

[23]  Morteza Elahi Naraghi,et al.  Adaptive Neuro Fuzzy Inference System and Artificial Neural Networks: reliable approaches for pipe stuck prediction , 2013 .

[24]  Shahab D. Mohaghegh,et al.  Neural Network: What It Can Do for Petroleum Engineers , 1995 .

[25]  S. Hoff,et al.  Development and Comparison of Backpropagation and Generalized Regression Neural Network Models to Predict Diurnal and Seasonal Gas and PM10 Concentrations and Emissions from Swine Buildings , 2008 .

[26]  Robello Samuel,et al.  Stuck Pipe Prediction and Avoidance: A Convolutional Neural Network Approach , 2006 .

[27]  Mohammed Aamir Mahmood,et al.  Evaluation of empirically derived PVT properties for Pakistani crude oils , 1996 .

[28]  Abdolnabi Hashemi,et al.  Determination of dew point pressure in gas condensate reservoirs based on a hybrid neural genetic algorithm , 2015 .

[29]  Fatih Taşpınar,et al.  Improving artificial neural network model predictions of daily average PM10 concentrations by applying principle component analysis and implementing seasonal models , 2015, Journal of the Air & Waste Management Association.