Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduou
Deep Learning for Water Quality Classification in Water Distribution NetworksMaintaining high water quality is the main goal for water management planning and iterative evaluation of operating policies. For effective water monitoring, it is crucial to test a vast number of......
Scarcity of water or scarcity of management? Scarcity as a means of governing: Challenging neoliberal hydromentality in the context of the South ... An integrated approach for water scarcity evaluation—a case study of Yunnan, China Exploring the framings of water scarcity in Palestinian textbooks...
Deep learning (DL) has been progressively used in water quality retrieval because it efficiently captures the potential relationship between target variables and imagery. In this study, the multimodal deep learning (MDL) models were developed and rigorously validated using atmospherically corrected Landsat...
A deep learning model coupling with datafeature extraction and enhancementmethods was developedESWT module was applied to extract the self characteristics of dataEnhancementmodule was adopted to enhance the global and local feature of dataThe established model improves the accuracy of water quality predict...
Preliminary detection for abnormal water quality after UF is vital for cost-efficient operations,but current predictive models lack accuracy. This study investigated the predictive models using deep learningalgorithms, specifically convolutional neural network (CNN) and long short-term memory (LSTM) ...
Deep learning for water quality Wei Zhi Alison P. Appling Li Li Nature Water (2024) Deep learning with autoencoders and LSTM for ENSO forecasting Chibuike Chiedozie Ibebuchi Michael B. Richman Climate Dynamics (2024) Improved monthly streamflow prediction using integrated multivariate adaptive ...
Deep learning for water quality Zhi, Wei;Appling, Alison P.;Golden, Heather E.;Podgorski, Joel;Li, Li 2024Nature Water doi:10.1038/s44221-024-00202-zpmid:38846520 Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes...
In this study we investigate how climate change will directly influence the groundwater resources in Germany during the 21st century. We apply a machine learning groundwater level prediction approach based on convolutional neural networks to 118 sites well distributed over Germany to assess the ground...
China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framework.