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
The Manufacture of Popular Perceptions of Scarcity: Dams and Water-Related Narratives in Gujarat, In... 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 ev...
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 model Water resource management Water quality index Hybrid models 1. Introduction A water quality index (WQI) determines overall water quality based on a combination of physical, chemical, and biological properties (Asadollah et al., 2021). It simplifies water quality assessment as a ...
Deep learning for prediction of water quality index classification: tropical catchment environmental assessment Natural Resources Research., 30 (6) (2021), pp. 4235-4254 Google Scholar Vaswani et al., 2017 Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A.N., Kaiser Ł...
a novel water quality prediction method based on the deep LSTM (long short-term memory) learning network is proposed to predict pH and water temperature... Hu, ZhuhuaZhang, YiranZhao, YaochiXie, MingshanZhong, JiezhuoTu, ZhigangLiu, Juntao - 《Nature Reviews Cancer》 被引量: 0发表: 2019...
The deep residual shrinkage network is a variant of deep residual networks (ResNets), and aims to improve the feature learning ability from highly noise signals or complex backgrounds. Although the method is originally developed for vibration-based fault diagnosis, it can be applied to image recogn...
In this study, we applied two novel deep learning algorithms, the recurrent neural network (RNN) and convolutional neural network (CNN), for national-scale landslide susceptibility mapping of Iran. We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors (...
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 we
During animal development, embryos undergo complex morphological changes over time. Differences in developmental tempo between species are emerging as principal drivers of evolutionary novelty, but accurate description of these processes is very challeng