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.About:
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.read more
Citations
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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.
Journal ArticleDOI
Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units
S. Poornima,M. Pushpalatha +1 more
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.
Journal ArticleDOI
Development of advanced artificial intelligence models for daily rainfall prediction
Binh Thai Pham,Lu Minh Le,Tien-Thinh Le,Kien-Trinh Thi Bui,Vuong Minh Le,Hai-Bang Ly,Indra Prakash +6 more
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
Ali Haidar,Brijesh Verma +1 more
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.
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