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Community Blog Project Showcase | AI Dashboard for Weather Prediction

Project Showcase | AI Dashboard for Weather Prediction

This project is from the team Clouders, which was awarded with the Innovation Award in the Global AI Innovation Challenge 2021 - Intelligent Weather Forecast for Better life.

This project is from the team Clouders, which was awarded with the Innovation Award in the Global AI Innovation Challenge 2021 - Intelligent Weather Forecast for Better life.

Background

Edge Computing IoT and 5g Future

5G and edge computing in IoT have been inextricably linked since 2019. 5G increases speed by up to ten times that of 4G, while edge computing accelerates response time by bringing data processing closer to devices and users. 5G will be able to serve all possible IoT and Smart City use cases, and provide the perfect connection depending on the individual demand for data rate, speed, and capacity. It can offer new features such as a high transmission speed of 20GB/sec, low latency of less than one millisecond means signal transmissions are possible almost in real-time ultra-low latency.

Combined with 5G, edge computing significantly reduces latency, enabling the delivery of mission-critical services that require Ultra-Reliable, Low Latency Communication (URLLC) Exponential bandwidth. 5G accelerates bandwidth, which means it can handle more connected devices that can respond in a matter of milliseconds High performance. Using 5G, it is possible to communicate with the edge quickly, and edge applications can respond rapidly to the ever-growing demand of consumers Data security.

Edge computing brings cloud computing capabilities to remote locations and provides local processing and storage when security is essential Minimized network traffic. Edge computing aggregates and processes IoT data at the edge of the network, reducing network traffic and transmission costs with less power consumption. By removing computing requirements from the device and putting it at the edge, edge computing technology reduces energy consumption

Project Introduction

Weather prediction, in general, is a complex process and challenging task. It requires various parameters to forecast the weather. Monitoring and predicting weather help in various fields like agriculture, travel, pollution dispersal, communication, disaster management, etc. Henceforth, weather forecasting plays a vital role in every day-to-day aspect, utilizing the needs of a common man to research scientists. This explains why forecasting cannot be predicted with simpler means. In the present times, there are high-definition satellite images to accurately predict the forecast of the upcoming days, but the process is neither simple nor economical. An accuracy of more than 90% is obtained, based on the dataset.

Recent studies have reflected that machine learning techniques achieved better performance than traditional statistical methods. Deep learning, a branch of artificial intelligence, has been proved to be a robust method in predicting and analyzing a given data set. Here, this module helps us predict the weather using the data and analyze it with a good accuracy rate and proves to be a simple one. The module involves the use of concepts related to artificial intelligence under Deep Learning and also predicts the air quality data and water quality data generated from the devices with speed computation. The results obtained show that it can estimate the weather conditions more precisely and accurately. Smart City brings both government and citizens closer for better engagement. The Control center of a smart city is a Single platform to view overall city condition, also able to track the location of support forces and deploy in real time, Alerts are received immediately with instantaneous, Emergencies sent to citizens via citizen app.

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Solution and Project Value

We have simulated the working of the Link IoT Edge, the Alibaba Cloud product that can be used to link your data from the IoT device the edge node/Servers present to the nearest location of the IoT node. The data from the IoT node once reaches the edge server/node. The data is processed in real-time by using Alibaba Cloud PAI DSW and it is stored in Analytical DB or PolarDB. Then the processed data that is stored is visualized on the website using Quick BI.

IoT has to manage very high volumes of data. If they share the same central server, all of them can slow down the server. For weather, Air the data needs to be processed up from time to time, Bulk data creates complexities for not just the server, but all the fragments of the IoT system. If the server slows down or fails, all the connected devices also fail. In edge computing, data is accessible locally to connected devices. Edge technology allows the data processing at each device ‘Edge’. It is closer to the data source. The proximity of the processing and data centers eliminates the need for data to travel to the centralized server, making the processing faster. And, as this reduces the data movement requirements, the response time reduces at each fragment of the IoT, contributing to high efficiency.

Another speed benefit of this system occurs because all the data is not processed together centrally. So, at each processing center, only a part of the data processing is done. Simultaneous multi-processing makes the system more productive. And, when the entire network is not exchanging data all the time, it saves a lot of unnecessary network cluttering, while maintaining data sharing between nodes only as and when required

Technology Highlights

In this project, we mainly focused on weather forecasting, air quality monitoring, and water quality monitoring with quick computation, minimize latency, and advanced technology, the monitoring station generates massive amounts of data that are analyzed in the edger nodes (Edge Computing), the data processed through deep learning algorithm technique (Data Science Workshop) and analyzed in the QuickBI and through API call it will be displayed in the dashboards.

Alibaba Cloud Products Used

Edge technology allows the data processing at each device ‘edge’ and is closer to the data source. The proximity of the processing and data centers eliminates the need for data to travel to the centralized server, making the processing faster.

PAI is an end-to-end machine learning service, including data processing, feature engineering, model training, model prediction, and model evaluation.

Simple Application Server is a new generation computing service for stand-alone application scenarios. It provides one-click application deployment and supports all-in-one services such as domain name resolution, website publishing, security, O&M, and application management.

In this project, we have used Alibaba Cloud domain to purchase the domain name “ www.aiweatherclouders.com”.

Quick BI allows you to perform data analytics, exploration and reporting on mass data with drag-and-drop features and a rich variety of visuals. Quick BI enables users to perform data analytics, exploration, and reporting and empowers enterprise users to view and explore data and make informed, data-driven decisions.

About the Developer

About GAVASKAR S (Team Leader)

Er Gavaskar S has completed M.E (Network and Internet Engineering) from Karunya University, Coimbatore in the year 2009 and B.Tech (I.T) from St.Xavier's Catholic College of Engineering affiliated to Anna University in the year 2005.

He has around 14 Years of Software experience, where he worked with various .net technologies. He has 10 years of teaching experience as an Assistant Professor at St.Xavier's Catholic College of Engineering. Presently he is working as Cloud/Software Consultant in GM Software, Nagercoil, India.

He is an MVP (Most Valuable Professional) in Alibaba Cloud with more than 175+ Clouder certifications, including seven Associate Certification in Cloud Computing, Cloud Security, Enterprise Database Cloud Transformation Architect, Cloud-Native, System Operator, Big Data and Developer, and two Professional level certifications from Alibaba Cloud. He got "DevOps Engineer Certification '' From Alibaba Cloud. He is an Oracle Academy Certified Trainer for Oracle Certification Courses. He is an active Writer of Alibaba Cloud in Quora and Reddit. He is an active writer in LinkedIn and published 4 articles in LinkedIn.

Click here to view his Linkedin Profile

About Josifha Ashmi J (Team Member)

Er. Josifha Ashmi J completed a Bachelor of Engineering in the Specialization of Computer science and engineering from St.Xavier's catholic college of Engineering, Nagercoil. She is currently working as an Assistant System Engineer at Chennai, experiencing as a software developer in the cloud, working on software design, analysis, development, implementation of web application development using Python.

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