Integrated explainable deep learning prediction of harmful algal blooms

What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research

(2021) Markus Langer et al.   ARTIFICIAL INTELLIGENCE

PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network

(2021) Sangwon Chae et al.   Scientific Reports

What Drives Bitcoin? An Approach from Continuous Local Transfer Entropy and Deep Learning Classification Models

(2021) Andrés García-Medina et al.   Entropy

A hybrid deep learning framework for urban air quality forecasting

(2021) Apeksha Aggarwal et al.   Journal of Cleaner Production

3D-CNN-based feature extraction of ground-based cloud images for direct normal irradiance prediction

(2019) Xin Zhao et al.   SOLAR ENERGY

CNNpred: CNN-based stock market prediction using a diverse set of variables

(2019) Ehsan Hoseinzade et al.   EXPERT SYSTEMS WITH APPLICATIONS

Estimating the economic costs of algal blooms in the Canadian Lake Erie Basin

(2019) Robert B. Smith et al.   HARMFUL ALGAE

Predicting residential energy consumption using CNN-LSTM neural networks

(2019) Tae-Young Kim et al.   ENERGY

Machine learning methods for better water quality prediction

(2019) Ali Najah Ahmed et al.   JOURNAL OF HYDROLOGY

Risk forecasting in the crude oil market: A multiscale Convolutional Neural Network approach

(2019) Yingchao Zou et al.   PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS

Analysis and modeling of algal blooms in the Nakdong River, Korea

(2018) Soonyim Bae et al.   ECOLOGICAL MODELLING

PM 10 concentration forecasting in the metropolitan area of Oviedo (Northern Spain) using models based on SVM, MLP, VARMA and ARIMA: A case study

(2018) P.J. García Nieto et al.   SCIENCE OF THE TOTAL ENVIRONMENT

A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities

(2018) Chiou-Jye Huang et al.   SENSORS

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

Predicting Infectious Disease Using Deep Learning and Big Data

(2018) Sangwon Chae et al.   International Journal of Environmental Research and Public Health

Long short-term memory - Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction

(2018) Jiachen Zhao et al.   CHEMOSPHERE

Estimation of daily PM10 concentrations in Italy (2006–2012) using finely resolved satellite data, land use variables and meteorology

(2017) Massimo Stafoggia et al.   ENVIRONMENT INTERNATIONAL

Can we open the black box of AI?

(2016) Davide Castelvecchi   NATURE

Deep learning

(2015) Yann LeCun et al.   NATURE

Fast Inverse Distance Weighting-Based Spatiotemporal Interpolation: A Web-Based Application of Interpolating Daily Fine Particulate Matter PM2.5 in the Contiguous U.S. Using Parallel Programming and k-d Tree

(2014) Lixin Li et al.   International Journal of Environmental Research and Public Health

Evaluation of a multiple regression model for the forecasting of the concentrations of NOx and PM10 in Athens and Helsinki

(2011) A. Vlachogianni et al.   SCIENCE OF THE TOTAL ENVIRONMENT

Explorations of the implementation of a parallel IDW interpolation algorithm in a Linux cluster-based parallel GIS

(2010) Fang Huang et al.   COMPUTERS & GEOSCIENCES

Using hexadecyl trimethyl ammonium bromide (CTAB) modified clays to clean the Microcystis aeruginosa blooms in Lake Taihu, China

(2010) Guofeng Liu et al.   HARMFUL ALGAE

Eutrophication of U.S. Freshwaters: Analysis of Potential Economic Damages

(2008) Walter K. Dodds et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY