4.7 Article

Interpretation of ensemble learning to predict water quality using explainable artificial intelligence

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Alejandro Barredo Arrieta et al.

INFORMATION FUSION (2020)

Editorial Material Robotics

XAI-Explainable artificial intelligence

David Gunning et al.

SCIENCE ROBOTICS (2019)

Article Environmental Sciences

Improved Prediction of Harmful Algal Blooms in Four Major South Korea's Rivers Using Deep Learning Models

Sangmok Lee et al.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2018)

Article Computer Science, Information Systems

A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost

Dahai Zhang et al.

IEEE ACCESS (2018)

Article Environmental Sciences

Water Quality Prediction Method Based on IGRA and LSTM

Jian Zhou et al.

Article Computer Science, Information Systems

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

Amina Adadi et al.

IEEE ACCESS (2018)

Proceedings Paper Automation & Control Systems

Water Quality Prediction Model Combining Sparse Auto-encoder and LSTM Network

Zhenbo Li et al.

IFAC PAPERSONLINE (2018)

Article Computer Science, Artificial Intelligence

LSTM: A Search Space Odyssey

Klaus Greff et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2017)

Article Environmental Sciences

Diel migration of Microcystis during an algal bloom event in the Three Gorges Reservoir, China

Yu-Jie Cui et al.

ENVIRONMENTAL EARTH SCIENCES (2016)

Article Ecology

Modeling lake trophic state: a random forest approach

Jeffrey W. Hollister et al.

ECOSPHERE (2016)

Article Computer Science, Interdisciplinary Applications

Characterising performance of environmental models

Neil D. Bennett et al.

ENVIRONMENTAL MODELLING & SOFTWARE (2013)

Article Agricultural Engineering

Model evaluation guidelines for systematic quantification of accuracy in watershed simulations

D. N. Moriasi et al.

TRANSACTIONS OF THE ASABE (2007)

Article Computer Science, Artificial Intelligence

A fast learning algorithm for deep belief nets

Geoffrey E. Hinton et al.

NEURAL COMPUTATION (2006)

Article Computer Science, Interdisciplinary Applications

Stochastic gradient boosting

JH Friedman

COMPUTATIONAL STATISTICS & DATA ANALYSIS (2002)