A review on interpretable and explainable artificial intelligence in hydroclimatic applications
This review focuses on the use of Interpretable Artificial Intelligence (IAI) and eXplainable
Artificial Intelligence (XAI) models for data imputations and numerical or categorical …
Artificial Intelligence (XAI) models for data imputations and numerical or categorical …
[HTML][HTML] A 1 km daily soil moisture dataset over China using in situ measurement and machine learning
High-quality gridded soil moisture products are essential for many Earth system science
applications, while the recent reanalysis and remote sensing soil moisture data are often …
applications, while the recent reanalysis and remote sensing soil moisture data are often …
[HTML][HTML] An attention-aware LSTM model for soil moisture and soil temperature prediction
Accurate prediction of soil moisture (SM) and soil temperature (ST) plays an important role in
Earth system science, helping to forecast and understand ecosystem changes. They present …
Earth system science, helping to forecast and understand ecosystem changes. They present …
Modeling of energy consumption factors for an industrial cement vertical roller mill by SHAP-XGBoost: a" conscious lab" approach
Cement production is one of the most energy-intensive manufacturing industries, and the
milling circuit of cement plants consumes around 4% of a year's global electrical energy …
milling circuit of cement plants consumes around 4% of a year's global electrical energy …
Quantification of COVID-19 impacts on NO2 and O3: Systematic model selection and hyperparameter optimization on AI-based meteorological-normalization methods
Since the unprecedented outbreak of the COVID-19, numerous meteorological-
normalization techniques have been developed in lockdown-imposed regions to decouple …
normalization techniques have been developed in lockdown-imposed regions to decouple …
A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm
A high-resolution soil moisture prediction method has recently gained its importance in
various fields such as forestry, agricultural and land management. However, accurate …
various fields such as forestry, agricultural and land management. However, accurate …
Environmental assessment based surface water quality prediction using hyper-parameter optimized machine learning models based on consistent big data
Prediction of dissolved oxygen (DO) and total dissolved solids (TDS) are of paramount
importance for water environmental protection and analysis of the ecosystem. The traditional …
importance for water environmental protection and analysis of the ecosystem. The traditional …
Hourly streamflow forecasting using a Bayesian additive regression tree model hybridized with a genetic algorithm
Urban flooding is a global metropolitan problem; therefore, establishing reliable streamflow
forecasting models is critical for flood control and planning in urban areas. Furthermore …
forecasting models is critical for flood control and planning in urban areas. Furthermore …
Spatial–temporal modeling of root zone soil moisture dynamics in a vineyard using machine learning and remote sensing
High-resolution spatial–temporal root zone soil moisture (RZSM) information collected at
different scales is useful for a variety of agricultural, hydrologic, and climate applications …
different scales is useful for a variety of agricultural, hydrologic, and climate applications …
A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing
M Li, H Sun, R Zhao - Remote Sensing, 2023 - mdpi.com
Root zone soil moisture (RZSM) controls vegetation transpiration and hydraulic distribution
processes and plays a key role in energy and water exchange between land surface and …
processes and plays a key role in energy and water exchange between land surface and …