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Temperature

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Machine Learning approach to Predict net radiation over crop surfaces from global solar radiation and canopy temperature data.

International journal of biometeorology
As the ground-based instruments for measuring net radiation are costly and need to be handled skillfully, the net radiation data at spatial and temporal scales over Indian subcontinent are scanty. Sometimes, it is necessary to use other meteorologica...

Predicting Modified Fournier Index by Using Artificial Neural Network in Central Europe.

International journal of environmental research and public health
The Modified Fournier Index () is one of the indices that can assess the erosivity of rainfall. However, the implementation of the artificial neural network (ANN) for the prediction of the is still rare. In this research, climate data (monthly and y...

A Multifunctional Light-Driven Swimming Soft Robot for Various Application Scenarios.

International journal of molecular sciences
The locomotor behavior of creatures in nature can bring a lot of inspiration for the fabrication of soft actuators. In this paper, we fabricated a bionic light-driven swimming soft robot that can perform grasping of tiny objects and achieve the task ...

Application of Quantum Chemical Topology Force Field FFLUX to Condensed Matter Simulations: Liquid Water.

Journal of chemical theory and computation
We present here the first application of the quantum chemical topology force field FFLUX to condensed matter simulations. FFLUX offers many-body potential energy surfaces learnt exclusively from data using Gaussian process regression. FFLUX also inc...

From data to noise to data for mixing physics across temperatures with generative artificial intelligence.

Proceedings of the National Academy of Sciences of the United States of America
Using simulations or experiments performed at some set of temperatures to learn about the physics or chemistry at some other arbitrary temperature is a problem of immense practical and theoretical relevance. Here we develop a framework based on stati...

Analysis of environmental factors using AI and ML methods.

Scientific reports
The main goal of this research paper is to apply a deep neural network model for time series forecasting of environmental variables. Accurate forecasting of snow cover and NDVI are important issues for the reliable and efficient hydrological models a...

Temperature Prediction of Seasonal Frozen Subgrades Based on CEEMDAN-LSTM Hybrid Model.

Sensors (Basel, Switzerland)
Improving the temperature prediction accuracy for subgrades in seasonally frozen regions will greatly help improve the understanding of subgrades' thermal states. Due to the nonlinearity and non-stationarity of the temperature time series of subgrade...

Machine learning and features for the prediction of thermal sensation and comfort using data from field surveys in Cyprus.

International journal of biometeorology
Perception can influence individuals' behaviour and attitude affecting responses and compliance to precautionary measures. This study aims to investigate the performance of methods for thermal sensation and comfort prediction. Four machine learning a...

LS-NTP: Unifying long- and short-range spatial correlations for near-surface temperature prediction.

Neural networks : the official journal of the International Neural Network Society
The near-surface temperature prediction (NTP) is an important spatial-temporal forecast problem, which can be used to prevent temperature crises. Most of the previous approaches fail to explicitly model the long- and short-range spatial correlations ...

Deep Learning Approach for Detection of Underground Natural Gas Micro-Leakage Using Infrared Thermal Images.

Sensors (Basel, Switzerland)
The leakage of underground natural gas has a negative impact on the environment and safety. Trace amounts of gas leak concentration cannot reach the threshold for direct detection. The low concentration of natural gas can cause changes in surface veg...