Machine learning
Research conducted by the Chinese Academy of Sciences in Changchun, People's Republic of China, has highlighted the critical need for improved water quality monitoring and prediction as the world grapples with the intensifying challenges of climate change and human activities. The study emphasizes the importance of integrating remote
Machine learning
Coffee demand continues to rise globally, and producing countries face challenges due to climate change, resulting in yield losses. Researchers from the State University of Campinas (UNICAMP) have developed a novel approach to identify coffee cultivation using machine learning. This innovative method proposes to identify coffee cultivation considering four phenological
Machine learning
A recent study published in the Journal of Hydrology and Hydromechanics has revealed that land-use change is a significant driver of environmental degradation and increasing drought risk. The research, conducted by investigators from Slovak University of Technology, aimed to assess drought dynamics in the South Malang Plateau, East Java, by
Machine learning
Research conducted by scientists at the Chinese Academy of Sciences has utilized machine learning to compute the lattice thermal conductivity of hcp Fe-Si alloys at room temperature and high pressure. The study, which has been peer-reviewed, found that the lattice thermal conductivity of Fe-Si alloys is significant at room temperature,
Machine learning
Researchers at the University of Groningen have developed a novel recursive Bayesian estimation framework using B-splines for continuous-time 6-DoF dynamic motion estimation. This framework, known as RESPLE, improves upon existing systems by achieving comparable or superior estimation accuracy and robustness while attaining real-time efficiency. The study presents extensive real-world evaluations
Machine learning
Researchers from Firat University have conducted a critical study to assess seismic risk in Denizli Province, a region in Turkey known for its industrial and touristic significance. The team utilized a combination of Analytic Hierarchy Process, Random Forest, and Frequency Ratio methods to create detailed susceptibility maps and classify risk
Machine learning
Researchers at Charles Sturt University have made a breakthrough in artificial intelligence by developing a hybrid machine-learning framework that can aid decision-making in laboratory experiments. The novel approach combines Ordinary Least Squares (OLS) for global surface estimation, Gaussian Process (GP) regression for uncertainty modeling, expected improvement (EI) for active learning,
Machine learning
A new study has developed two novel hyperspectral indices, Machine Learning-Based Vegetation Index (MLVI) and Hyperspectral Vegetation Stress Index (H_VSI), which leverage critical spectral bands to detect crop stress effectively. The proposed framework is optimized using Recursive Feature Elimination (RFE) and serves as an input to a Convolutional Neural
Machine learning
Researchers at the Indian Institute of Technology (IIT) Kanpur have successfully utilized a combination of air pollutant data, meteorological parameters, urbanization indicators, spectral indices, and elevation data to predict land surface temperature (LST) in Bengaluru over a five-year period. The study, published in the journal Natural Hazards, employed advanced machine
Machine learning
Current research has demonstrated the effectiveness of Deep Learning (DL) models in image classification, a crucial application of DL techniques. A recent study published in the Proceedings on Engineering Sciences journal has compared the efficiency and accuracy of different DL models and activation functions in image classification tasks. The research,
Machine learning
Research findings on artificial intelligence, conducted by researchers at Lawrence Berkeley National Laboratory, highlight the importance of using ensemble strategies for hydrologic predictions. According to the study, machine learning (ML) models have made significant progress in predicting stream flows and other hydrologic quantities over the past decade. However, deterministic models
Machine learning
Researchers from Beijing Normal University have developed a new method to identify the source of cropland runoff in surface waters using machine learning and microbial fingerprints. The study, published in the journal Water Research, utilized 386 samples from various sources, including aquaculture wastewater, domestic sewage, and cropland runoff. The team
