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Integrating Mesoscale Eddies Into NARX Neural Networks for Improved Chlorophyll-A Forecasting From Remote Sensing Data in the South China Sea

Gao, Z., Liu, Q., Wu, X., Mei, H., Tang, P., Cheng, Y., Xu, W., Zhu, S., & Wang, X. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3605147 · Cited by 1 · see citing works

Evolutionary Trends in Machine Learning for Water Quality Prediction: A Visual Analysis Using CiteSpace and RStudio Bibliometrix

Li, Y., Zhang, H., Yuan, W., Qi, H., Ding, B., & Zhang, B. (2025) · JAWRA Journal of the American Water Resources Association · doi:10.1111/1752-1688.70061 · Cited by 1 · see citing works

Multi-step forecasting of chlorophyll-a concentration in coastal waters through Wavelet Dense Attention Transformer model

Qin, M., Xu, R., Xu, L., Zhao, B., Chen, X., & Du, Z. (2025) · Environmental Research · doi:10.1016/j.envres.2025.123627 · Cited by 1 · see citing works

Drone-based Estimation of Chlorophyll-a in Reservoirs: A Cubist-based Ensemble Model

Ghasemnezhad, S., Nikoo, M. R., Mashal, M., Al-Wardy, M., & Nasiri, N. (2026) · International Conference on Unmanned Vehicle Systems-Oman (UVS) · doi:10.1109/uvs66989.2026.11449628 · Cited by 1 · see citing works

Advances in artificial intelligence for climate science methods applications and evaluation challenges

Houssein, E. H., Dirar, M., Ali, A. A., Mohamed, W. M., & Hamed, K. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01262-8 · Cited by 1 · see citing works

From descriptive digital twins to closed-loop intelligent agents: Adaptive biogeochemical management in river-reservoir systems

Guo, H., Yuan, R., Wang, T., Li, R., Li, Y., Su, Y., Zhang, H., & Huang, T. (2026) · Water Research · doi:10.1016/j.watres.2026.126501 · Cited by 1 · see citing works

Controlling filamentous cyanobacterial blooms requires adaptive, weather-informed strategy

Fang, J., Su, M., Li, Y., Gui, Y., Ai, Y., Joseph, O., Cao, T., He, S., & Yang, M. (2026) · Water Research · doi:10.1016/j.watres.2026.126505 · Cited by 1 · see citing works

A conservative interpretability framework for attention-based multi-step river water-quality prediction

최인호, & Jun, B.-M. (2026) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2026.110600 · Cited by 1 · see citing works

Metamodeling of a Physically Based Pesticide Runoff Model with a Long Short-Term Memory ApproachPreprint

Métayer, G., Dagès, C., Voltz, M., & Bailly, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5159594

A Dual-Path Transformer and Deep Learning Framework with Multi-Attention Fusion for Weather PredictionPreprint

Matlani, P., & Shukla, B. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5658908

Data-Driven Vulnerability Assessment of the Water Resource System in Nansi Lake BasinPreprint

Xu, Z., Wu, L., Li, X., Diao, Y., Wang, J., Qiu, Q., Tian, J., & Liang, R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5856835

Forecasting of Chlorophyll-a Concentrations in Coastal Waters Through Gated Recurrent Unit Neural Network Based on Time–frequency Analysis and Smoothing Filtering

Ye, Q., Yi, Z., Liang, Y., Huang, X., & Li, J. (2026) · Water Conservation Science and Engineering · doi:10.1007/s41101-026-00497-x

Coupling Time-Series Reconstruction with Neural Networks for Chlorophyll-a Gap Filling in Urban RiversPreprint

Chen, Y., Qian, Y., Lou, W., Fang, C., Chen, Q., ZHANG, Y., Sun, H., Hong, H., Wu, D., Lin, H., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6487004

Predictive Modelling of Water Quality Parameters Through Machine Learning Techniques

Giri, U. K., Sarangi, D. K., Devi, L. N., Saxena, M., Mehta, G., & Agrawal, A. K. (2026) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi68868.2026.11465495

Energy-efficient water quality modeling using memristor-based neuromorphic computing

Jeung, M., Son, G., Kim, M., & Baek, S.-S. (2026) · npj Clean Water · doi:10.1038/s41545-026-00586-3

Comparative Evaluation of Machine Learning Models for Satellite Chlorophyll-a Gap Reconstruction in the Chesapeake Bay

Chidananda, R., Malarvizhi, A. S., Ahmed, S., Zhang, E., & Yang, C. P. (2026) · Remote Sensing · doi:10.3390/rs18111736

Comparative evaluation of deterministic and Bayesian neural networks for chlorophyll-a time series forecasting in eutrophic lakes: A case study of Dianchi Lake, China

Yang, J., Huang, Y., Zhu, Z., Li, Y., Liang, Z., & Liu, Y. (2026) · Environmental Research · doi:10.1016/j.envres.2026.125082

Data-driven vulnerability assessment of the Nansi Lake Basin water resource system

Xu, Z., Wu, L., Li, X., Diao, Y., Wang, J., Qiu, Q., Tian, J., & Liang, R. (2026) · Journal of Hydrology Regional Studies · doi:10.1016/j.ejrh.2026.103706

Physics-Informed Transformer for Genus-Level Phytoplankton Forecasting at Drinking-Water Intake Points

Lee, J., Jung, E.-Y., Lee, J., Son, H., Kim, H. G., & Cho, K. H. (2026) · Water Research · doi:10.1016/j.watres.2026.126599

Trends in water quality and geochemistry research

Guo, H. (2026) · Journal of Hydrology · doi:10.1016/j.jhydrol.2026.136194

Prediction of lake chlorophyll-a concentration driven by historical big data

Qiu, Z., Wu, T., Zhou, W., Fei, Y., Xi, H., & Qiu, Y. (2026) · Information Geography · doi:10.1016/j.infgeo.2026.100059

A coupled spatiotemporal graph model for phytoplankton blooms prediction using incomplete data from buoy-based monitoring systems

Wang, L., Shan, K., Wu, X., Xie, M., & Shang, M. (2026) · Ecological Informatics · doi:10.1016/j.ecoinf.2026.104037

Improved prediction of chlorophyll-a concentrations using advancing graph neural network variants

Yoon, S.-H., & Ahn, K.-H. (2025) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2025.179481 · Cited by 7 · see citing works

DEEP LEARNING APPLICATION TO TIME-SERIES PREDICTION OF DAILY CHLOROPHYLL-A CONCENTRATION

Cho, H., Choi, U.-J., & Park, H. (2018) · WIT transactions on ecology and the environment · doi:10.2495/eid180141 · Cited by 55 · see citing works

A Hybrid Approach for the Prediction of Chlorophyll-a Concentration at the Non-monitoring Area in the Geum River, Korea

Jang, M., Choi, J., Kim, J., Seo, D., & Kim, J. (2020) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc49870.2020.9289191 · Cited by 4 · see citing works

Prediction of chlorophyll-a as an indicator of harmful algal blooms using deep learning with Bayesian approximation for uncertainty assessment

Busari, I., Sahoo, D., & Jana, R. B. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.130627 · Cited by 38 · see citing works

Long-term prediction of sea surface chlorophyll-a concentration based on the combination of spatio-temporal features

Na, L., Chen, S., Zhenyan, C., Wang, X., Xiao, Y., Li, X., Gong, Y., Wang, T., Xuefeng, Z., & Liu, S. (2022) · Water Research · doi:10.1016/j.watres.2022.118040 · Cited by 85 · see citing works