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首页> 外文期刊>International journal of web services research >Real-Time Weather Analytics: An End-to-End Big Data Analytics Service Over Apach Spark With Kafka and Long Short-Term Memory Networks
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Real-Time Weather Analytics: An End-to-End Big Data Analytics Service Over Apach Spark With Kafka and Long Short-Term Memory Networks

机译：实时天气分析：带有Kafka和长短期内存网络的Apache Spark的端到端大数据分析服务

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摘要

Weather forecasting is one of the biggest challenges that modern science is still contending with. The advent of high-power computing, technical advancement of data storage devices, and incumbent reduction in the storage cost have accelerated data collection to turmoil. In this background, many artificial intelligence techniques have been developed and opened interesting window of opportunity in hitherto difficult areas. India is on the cusp of a major technology overhaul with millions of people's data availability who were earlier unconnected with the internet. The country needs to fast forward the innovative use of available data. The proposed model endeavors to forecast temperature, precipitation, and other vital information for usability in the agrarian sector. This project intends to develop a robust weather forecast model that learns automatically from the daily feed of weather data that is input through a third-party API source. The weather feed is sourced from openweathermap, an online service that provides weather data, and is streamed into the forecast model through Kafka components. The LSTM neural network used by the forecast model is designed to continuously learn from predictions and perform actual analysis. The model can be architected to be implemented across very large applications having the capability to process large volumes of streamed or stored data.
机译：天气预报是现代科学仍在与之竞争的最大挑战之一。高功率计算的出现，数据存储设备的技术进步以及储存成本的现任减少将数据收集加速到动荡。在这种背景下，许多人工智能技术已经开发并开启了迄今为止困难地区的有趣机会窗口。印度凭借互联网未连接的数百万人的数据可用性，是一项主要技术革新的尖端。该国需要快速前进的可用数据的创新使用。建议的模型努力预测气温，降水和其他重要信息在农业部门中的可用性。该项目打算开发一种强大的天气预报模型，可以自动学习通过第三方API源输入的天气数据的日常饲料。天气源来自OpenWeatherMap，该服务提供天气数据的在线服务，并通过Kafka组件流入预测模型。预测模型使用的LSTM神经网络旨在不断从预测中学习并执行实际分析。该模型可以在具有能够处理大量流式或存储的数据的能力的非常大的应用程序中地归属于实现。

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