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

Recent Advances in Information and Communications Technology (ICT) and Sensor Technology for Monitoring Water Quality

(2020) Jungsu Park et al.   Water

Tree-Based Modeling Methods to Predict Nitrate Exceedances in the Ogallala Aquifer in Texas

(2020) Venkatesh Uddameri et al.   Water

Hybrid decision tree-based machine learning models for short-term water quality prediction

(2020) Hongfang Lu et al.   CHEMOSPHERE

Machine Learning Models of Groundwater Arsenic Spatial Distribution in Bangladesh: Influence of Holocene Sediment Depositional History

(2020) Zhen Tan et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Prediction of Chlorophyll-a Concentrations in the Nakdong River Using Machine Learning Methods

(2020) Yuna Shin et al.   Water

Ensemble Model Development for the Prediction of a Disaster Index in Water Treatment Systems

(2020) Jungsu Park et al.   Water

XAI—Explainable artificial intelligence

(2019) David Gunning et al.   Science Robotics

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

(2019) Alejandro Barredo Arrieta et al.   Information Fusion

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

(2018) Sangmok Lee et al.   International Journal of Environmental Research and Public Health

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

(2018) Dahai Zhang et al.   IEEE Access

Water Quality Prediction Method Based on IGRA and LSTM

(2018) Jian Zhou et al.   Water

Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI)

(2018) Amina Adadi et al.   IEEE Access

Application of Artificial Neural Networks to Rainfall Forecasting in the Geum River Basin, Korea

(2018) Jeongwoo Lee et al.   Water

LSTM: A Search Space Odyssey

(2017) Klaus Greff et al.   IEEE Transactions on Neural Networks and Learning Systems

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

(2016) Yu-Jie Cui et al.   Environmental Earth Sciences

Effects of eutrophication on maximum algal biomass in lake and river ecosystems

(2016) Val Smith   Inland Waters

Modeling lake trophic state: a random forest approach

(2016) Jeffrey W. Hollister et al.   Ecosphere

Thermal effects on the growth and fatty acid composition of four harmful algal bloom species: Possible implications for ichthyotoxicity

(2016) Bonggil Hyun et al.   Ocean Science Journal

Application of molecular tools for microbial source tracking and public health risk assessment of a Microcystis bloom traversing 300km of the Klamath River

(2015) Timothy G. Otten et al.   HARMFUL ALGAE

Combination of artificial neural network and clustering techniques for predicting phytoplankton biomass of Lake Poyang, China

(2015) Jiacong Huang et al.   LIMNOLOGY

Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea

(2015) Yongeun Park et al.   SCIENCE OF THE TOTAL ENVIRONMENT

Support vector machine―an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river?

(2014) Mei Liu et al.   ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH

Modeling daily chlorophyll a dynamics in a German lowland river using artificial neural networks and multiple linear regression approaches

(2013) Naicheng Wu et al.   LIMNOLOGY

Characterising performance of environmental models

(2012) Neil D. Bennett et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Enhanced algae removal by drinking water treatment of chlorination coupled with coagulation

(2011) Qiaohui Shen et al.   DESALINATION

Phytoplankton bloom status: Chlorophyll a biomass as an indicator of water quality condition in the southern estuaries of Florida, USA

(2009) Joseph N. Boyer et al.   ECOLOGICAL INDICATORS