Journal ArticleDOI
Daily water level forecasting using wavelet decomposition and artificial intelligence techniques
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TLDR
Results obtained from this study indicate that the conjunction of wavelet decomposition and artificial intelligence models can be a useful tool for accurate forecasting daily water level and can yield better efficiency than the conventional forecasting models.About:
This article is published in Journal of Hydrology.The article was published on 2015-01-01. It has received 228 citations till now. The article focuses on the topics: Wavelet & Adaptive neuro fuzzy inference system.read more
Citations
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The role of artificial intelligence in achieving the Sustainable Development Goals
Ricardo Vinuesa,Hossein Azizpour,Iolanda Leite,Madeline Balaam,Virginia Dignum,Sami Domisch,Anna Felländer,Simone D. Langhans,Max Tegmark,Francesco Fuso Nerini +9 more
TL;DR: In this paper, the authors assess the impact of artificial intelligence on the achievement of the Sustainable Development Goals (SDGs) by using Artificial Intelligence (AI) and its progressively wider impact on many sectors.
Journal ArticleDOI
The role of artificial intelligence in achieving the Sustainable Development Goals
Ricardo Vinuesa,Hossein Azizpour,Iolanda Leite,Madeline Balaam,Virginia Dignum,Sami Domisch,Anna Felländer,Simone D. Langhans,Max Tegmark,Francesco Fuso Nerini +9 more
TL;DR: Using a consensus-based expert elicitation process, it is found that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets.
Journal ArticleDOI
Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas.
TL;DR: Wang et al. as mentioned in this paper proposed a new time series model based on Long Short-Term Memory (LSTM) as an alternative to computationally expensive physical models, which is composed of an LSTM layer with another fully connected layer on top of it.
Journal ArticleDOI
Flood prediction using machine learning models: Literature review
TL;DR: In this paper, the state-of-the-art machine learning models for both long-term and short-term floods are evaluated and compared using a qualitative analysis of robustness, accuracy, effectiveness and speed.
Journal ArticleDOI
Flood Prediction Using Machine Learning Models: Literature Review
TL;DR: In this paper, the state of the art of ML models in flood prediction and to give insight into the most suitable models are presented. And the major trends in improving the quality of the flood prediction models are investigated.
References
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Journal Article
R: A language and environment for statistical computing.
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
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A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
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Fuzzy identification of systems and its applications to modeling and control
T. Takagi,Michio Sugeno +1 more
TL;DR: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented and two applications of the method to industrial processes are discussed: a water cleaning process and a converter in a steel-making process.
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Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
Journal ArticleDOI
ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.