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Open AccessJournal ArticleDOI

Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan - Indonesia

TLDR
An Artificial Neural Network with the Backpropagation Neural Network (BPNN) algorithm has provided a good model to predict rainfall in Tenggarong, East Kalimantan - Indonesia.
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This article is published in Procedia Computer Science.The article was published on 2015-01-01 and is currently open access. It has received 113 citations till now.

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An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives

TL;DR: A thorough examination of the predictive performance of the current state-of-the-art (Markov chain extended with rainfall prediction) and six other popular machine learning algorithms, namely: Genetic Programming, Support Vector Regression, Radial Basis Neural Networks, M5 Rules, M 5 Model trees, and k-Nearest Neighbours, shows that the machine learning methods are able to outperform the current State of theart.
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Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

S. Poornima, +1 more
- 31 Oct 2019 - 
TL;DR: Intensified Long Short-Term Memory based Recurrent Neural Network (RNN) basedrecurrent neural network is trained and tested using a standard dataset of rainfall to predict rainfall.
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Development of advanced artificial intelligence models for daily rainfall prediction

TL;DR: In this paper, the authors developed and compared several advanced Artificial Intelligent (AI) models namely Adaptive Network based Fuzzy Inference System optimized with Particle Swarm Optimization (PSOANFIS), Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for the prediction of daily rainfall in Hoa Binh province, Vietnam.
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Monthly Rainfall Forecasting Using One-Dimensional Deep Convolutional Neural Network

TL;DR: This paper proposes a new forecasting method that uses a deep convolutional neural network (CNN) to predict monthly rainfall for a selected location in eastern Australia, which is the first time applying a deep CNN in predicting monthly rainfall.
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Multi-stage hybridized online sequential extreme learning machine integrated with Markov Chain Monte Carlo copula-Bat algorithm for rainfall forecasting

TL;DR: The multi-stage, hybridized MCMC-Cop-Bat-OS-ELM model is found to be a superior tool for forecasting monthly rainfall and can be explored as a pertinent decision-support tool for agricultural water resources management in arid and semi-arid regions where a statistically significant relationship with antecedent rainfall exists.
References
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Journal ArticleDOI

Time Series Analysis.

Journal ArticleDOI

Time series analysis

James D. Hamilton
- 01 Feb 1997 - 
TL;DR: A ordered sequence of events or observations having a time component is called as a time series, and some good examples are daily opening and closing stock prices, daily humidity, temperature, pressure, annual gross domestic product of a country and so on.
Journal ArticleDOI

Artificial neural networks: fundamentals, computing, design, and application

TL;DR: A bird's eye review of the various types of ANNs and the related learning rules is presented, with special emphasis on backpropagation ANNs theory and design, and a generalized methodology for developing successful ANNs projects from conceptualization, to design, to implementation is described.
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Identification of three dominant rainfall regions within indonesia and their relationship to sea surface temperature

TL;DR: In this article, the characteristics of rainfall variability in Indonesia were investigated using a double correlation method and the results were compared with empirical orthogonal function (EOF) and rotated EOF methods.
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