Journal Article10.1016/J.JHYDROL.2015.08.022
Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines
Riccardo Taormina,Kwok Wing Chau +1 more
330
TL;DR: A novel approach that employs Binary-coded discrete Fully Informed Particle Swarm optimization and Extreme Learning Machines (ELM) to develop fast and accurate IVS algorithms that are particularly suited for rainfall–runoff modeling applications characterized by high nonlinearity in the catchment dynamics.
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About: This article is published in Journal of Hydrology. The article was published on 01 Oct 2015. The article focuses on the topics: Feature selection & Particle swarm optimization.
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Citations
An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction
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A systematic literature review on machine learning applications for sustainable agriculture supply chain performance
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A survey on river water quality modelling using artificial intelligence models: 2000–2020
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519
Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation
TL;DR: In this article, the authors proposed new data-driven methods for flood forecasting using Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) networks. But, the results show that the two networks are all suitable for rainfall-runoff models and better than conceptual and physical based models.
A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area
Dieu Tien Bui,Quang-Thanh Bui,Quoc-Phi Nguyen,Biswajeet Pradhan,Biswajeet Pradhan,Haleh Nampak,Phan Trong Trinh +6 more
TL;DR: The proposed PSO-NF model is a valid alternative tool that should be considered for tropical forest fire susceptibility modeling and is useful for forest planning and management in forest fire prone areas.
354
References
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Particle swarm optimization
James Kennedy,Russell C. Eberhart +1 more
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TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
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Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
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Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
- 01 Jan 2001
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.