Interpretable Framework of Physics‐Guided Neural Network With Attention Mechanism: Simulating Paddy Field Water Temperature Variations
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Interpretable Framework of Physics‐Guided Neural Network With Attention Mechanism: Simulating Paddy Field Water Temperature Variations
Authors
Keywords
-
Journal
WATER RESOURCES RESEARCH
Volume 58, Issue 5, Pages -
Publisher
American Geophysical Union (AGU)
Online
2022-05-02
DOI
10.1029/2021wr030493
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- The development of a hybrid model to forecast paddy water temperature as an alert system for high‐temperature damage
- (2022) Wenpeng Xie et al. IRRIGATION AND DRAINAGE
- River water temperature forecasting using a deep learning method
- (2021) Rujian Qiu et al. JOURNAL OF HYDROLOGY
- From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling
- (2021) Wen-Ping Tsai et al. Nature Communications
- Extract interpretability-accuracy balanced rules from artificial neural networks: A review
- (2020) Congjie He et al. NEUROCOMPUTING
- Simulation of water temperature in paddy fields by a heat balance model using plant growth status parameter with interpolated weather data from weather stations
- (2020) Wenpeng Xie et al. Paddy and Water Environment
- Unmasking Clever Hans predictors and assessing what machines really learn
- (2019) Sebastian Lapuschkin et al. Nature Communications
- Deep learning and process understanding for data-driven Earth system science
- (2019) Markus Reichstein et al. NATURE
- Responses of indica rice yield and quality to extreme high and low temperatures during the reproductive period
- (2019) Mohammad Abubakar Siddik et al. EUROPEAN JOURNAL OF AGRONOMY
- Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks
- (2019) Nicholas Geneva et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Process‐Guided Deep Learning Predictions of Lake Water Temperature
- (2019) Jordan S. Read et al. WATER RESOURCES RESEARCH
- Theoretical analysis of the effects of irrigation rate and paddy water depth on water and leaf temperatures in a paddy field continuously irrigated with running water
- (2018) Kazuhiro Nishida et al. AGRICULTURAL WATER MANAGEMENT
- Methods for interpreting and understanding deep neural networks
- (2018) Grégoire Montavon et al. DIGITAL SIGNAL PROCESSING
- Source localization using deep neural networks in a shallow water environment
- (2018) Zhaoqiong Huang et al. JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
- Complexity and stability of ecological networks: a review of the theory
- (2018) Pietro Landi et al. POPULATION ECOLOGY
- The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
- (2018) Kun Yao et al. Chemical Science
- A Survey of Methods for Explaining Black Box Models
- (2018) Riccardo Guidotti et al. ACM COMPUTING SURVEYS
- A Water Temperature Simulation Model for Rice Paddies With Variable Water Depths
- (2017) Atsushi Maruyama et al. WATER RESOURCES RESEARCH
- Hybrid modeling and prediction of dynamical systems
- (2017) Franz Hamilton et al. PLoS Computational Biology
- Rice grain yield and quality responses to free-air CO2 enrichment combined with soil and water warming
- (2016) Yasuhiro Usui et al. GLOBAL CHANGE BIOLOGY
- An overview of current applications, challenges, and future trends in distributed process-based models in hydrology
- (2016) Simone Fatichi et al. JOURNAL OF HYDROLOGY
- Interpretable deep neural networks for single-trial EEG classification
- (2016) Irene Sturm et al. JOURNAL OF NEUROSCIENCE METHODS
- A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund–Potsdam–Jena (LPJ) model as an example
- (2016) Guodong Sun et al. THEORETICAL AND APPLIED CLIMATOLOGY
- A mechanistic energy balance model for predicting water temperature in surface flow wetlands
- (2014) Jason K. Smesrud et al. ECOLOGICAL ENGINEERING
- The Parable of Google Flu: Traps in Big Data Analysis
- (2014) D. Lazer et al. SCIENCE
- Rainfall-runoff modeling using conceptual, data driven, and wavelet based computing approach
- (2013) P.C. Nayak et al. JOURNAL OF HYDROLOGY
- Using sensitivity analysis and visualization techniques to open black box data mining models
- (2012) Paulo Cortez et al. INFORMATION SCIENCES
- A decade of weather extremes
- (2012) Dim Coumou et al. Nature Climate Change
- Coupling land surface and crop growth models to estimate the effects of changes in the growing season on energy balance and water use of rice paddies
- (2010) Atsushi Maruyama et al. AGRICULTURAL AND FOREST METEOROLOGY
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now