Interpretable Framework of Physics‐Guided Neural Network With Attention Mechanism: Simulating Paddy Field Water Temperature Variations

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