Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia
Wanie M. Ridwan,Michelle Sapitang,Awatif Aziz,Khairul Faizal Kushiar,Ali Najah Ahmed,Ahmed El-Shafie,Ahmed El-Shafie +6 more
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TLDR
In this article, a comparative study was conducted focusing on developing and comparing several Machine Learning (ML) models, evaluating different scenarios and time horizon, and forecasting rainfall using two types of methods.About:
This article is published in Ain Shams Engineering Journal.The article was published on 2021-06-01 and is currently open access. It has received 87 citations till now. The article focuses on the topics: Bayesian linear regression.read more
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The digitization of agricultural industry – a systematic literature review on agriculture 4.0
TL;DR: In this article , a systematic literature review based on Protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses is conducted to analyse the scientific literature related to crop farming published in the last decade.
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A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem
Ali Najah Ahmed,To Van Lam,Nguyen Duy Hung,Nguyen Van Thieu,Ozgur Kisi,Ozgur Kisi,Ahmed El-Shafie,Ahmed El-Shafie +7 more
TL;DR: It can be concluded that augmenting the NRO algorithm with MLP can be a reliable tool in forecasting the monthly streamflow with a high level of precision, speed convergence, and high constancy level.
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The AI Gambit — Leveraging Artificial Intelligence to Combat Climate Change: Opportunities, Challenges, and Recommendations
TL;DR: It is argued that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based and effective governance to become a winning strategy.
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Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia.
Marwah Sattar Hanoon,Ali Najah Ahmed,Nur’atiah Zaini,Arif Razzaq,Pavitra Kumar,Mohsen Sherif,Ahmed Sefelnasr,Ahmed El-Shafie +7 more
TL;DR: In this paper, the authors proposed different machine learning algorithms: Gradient Boosting Tree (G.B.T), Random Forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of such as air temperature (T) and relative humidity (Rh).
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The AI gambit: leveraging artificial intelligence to combat climate change-opportunities, challenges, and recommendations.
Josh Cowls,Josh Cowls,Andreas Tsamados,Mariarosaria Taddeo,Mariarosaria Taddeo,Luciano Floridi,Luciano Floridi +6 more
TL;DR: In this article, the authors analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change and identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change, and it can contribute to combatting the climate crisis effectively.
References
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Another look at measures of forecast accuracy
Rob J. Hyndman,Anne B. Koehler +1 more
TL;DR: In this paper, the mean absolute scaled error (MESEME) was proposed as the standard measure for comparing forecast accuracy across multiple time series across different time series types, and was used in the M-competition as well as the M3competition.