Rainfall prediction system for Bangladesh using long short-term memory

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2022-01-01 DOI:10.1515/comp-2022-0254

M. Billah, Md. Nasim Adnan, Mostafijur Rahman Akhond, Romana Rahman Ema, Md. Alam Hossain, S. Galib

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引用次数: 2

Abstract

Abstract Rainfall prediction is a challenging task and has extreme significance in weather forecasting. Accurate rainfall prediction can play a great role in agricultural, aviation, natural phenomenon, flood, construction, transport, etc. Weather or climate is assumed to be one of the most complex systems. Again, chaos, also called as “butterfly effect,” limits our ability to make weather predictable. So, it is not easy to predict rainfall by conventional machine learning approaches. However, several kinds of research have been proposed to predict rainfall by using different computational methods. To accomplish chaotic rainfall prediction system for Bangladesh, in this study, historical data set-driven long short term memory (LSTM) networks method has been used, which overcomes the complexities and chaos-related problems faced by other approaches. The proposed method has three principal phases: (i) The most useful 10 features are chosen from 20 data attributes. (ii) After that, a two-layer LSTM model is designed. (iii) Both conventional machine learning approaches and recent works are compared with the LSTM model. This approach has gained 97.14% accuracy in predicting rainfall (in millimeters), which outperforms the state-of-the-art solutions. Also, this work is a pioneer work to the rainfall prediction system for Bangladesh.

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使用长短期记忆的孟加拉国降雨量预测系统

降雨预测是一项极具挑战性的任务，在天气预报中具有极其重要的意义。准确的降雨预报可以在农业、航空、自然现象、防洪、建筑、交通等方面发挥很大的作用。天气或气候被认为是最复杂的系统之一。同样，混沌，也被称为“蝴蝶效应”，限制了我们预测天气的能力。因此，通过传统的机器学习方法预测降雨并不容易。然而，已经提出了几种使用不同计算方法来预测降雨的研究。为了实现孟加拉国的混沌降雨预报系统，本研究采用了历史数据集驱动的长短期记忆(LSTM)网络方法，克服了其他方法所面临的复杂性和与混沌相关的问题。提出的方法有三个主要阶段:(i)从20个数据属性中选择最有用的10个特征。(ii)之后，设计两层LSTM模型。(iii)将传统的机器学习方法和最近的研究成果与LSTM模型进行比较。该方法在预测降雨量(以毫米为单位)方面的准确率达到97.14%，优于最先进的解决方案。此外，这项工作是孟加拉国降雨预报系统的先驱工作。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.