[HTML][HTML] Explainable AI for earth observation: A review including societal and regulatory perspectives
CM Gevaert - International Journal of Applied Earth Observation and …, 2022 - Elsevier
Artificial intelligence and machine learning are ubiquitous in the domain of Earth
Observation (EO) and Remote Sensing. Congruent to their success in the domain of …
Observation (EO) and Remote Sensing. Congruent to their success in the domain of …
Sustainable use of chemically modified tyre rubber in concrete: Machine learning based novel predictive model
To encourage the consumption of crumb rubber (CR), gene expression programming (GEP)
has been exercised in this paper to establish empirical models for estimation of mechanical …
has been exercised in this paper to establish empirical models for estimation of mechanical …
Forecasting of SPI and meteorological drought based on the artificial neural network and M5P model tree
Climate change has caused droughts to increase in frequency and severity worldwide,
which has attracted scientists to create drought prediction models to mitigate the impacts of …
which has attracted scientists to create drought prediction models to mitigate the impacts of …
[HTML][HTML] A comparative analysis of data mining techniques for agricultural and hydrological drought prediction in the eastern Mediterranean
Drought is a natural hazard which affects ecosystems in the eastern Mediterranean.
However, limited historical data for drought monitoring and forecasting are available in the …
However, limited historical data for drought monitoring and forecasting are available in the …
Machine learning-based prediction of sand and dust storm sources in arid Central Asia
With the emergence of multisource data and the development of cloud computing platforms,
accurate prediction of event-scale dust source regions based on machine learning (ML) …
accurate prediction of event-scale dust source regions based on machine learning (ML) …
Knowledge discovery of Middle East dust sources using Apriori spatial data mining algorithm
Identifying the areas susceptible to dust storm formation is one effective way of dealing with
this destructive environmental phenomenon. This study is the first attempt to employ the …
this destructive environmental phenomenon. This study is the first attempt to employ the …
Dust source susceptibility mapping based on remote sensing and machine learning techniques
Dust source susceptibility modeling and mapping is the first step in managing the impacts of
dust on environmental systems and human health. In this study, satellite products and …
dust on environmental systems and human health. In this study, satellite products and …
[HTML][HTML] Identification of aeolian dust hotspots in the lower reaches of Zhuoshui river in Taiwan using environmental indicators
SW Wu, H Huang, SF Tsai, CY Lin - Ecological Indicators, 2023 - Elsevier
This study introduces a risk model that utilizes the ventilation index (VI), Temperature
Vegetation Dryness Index (TVDI), and coefficient of variation (CV) of soil moisture content to …
Vegetation Dryness Index (TVDI), and coefficient of variation (CV) of soil moisture content to …
A game theory-based prioritization of drought affected demo vineyards using soil main properties in the northern apennines, italy
SH Sadeghi, MZ Silabi, M Bordoni, TNA Nguyen… - Catena, 2024 - Elsevier
The selection of appropriate strategies is required to mitigate drought effects in farmlands.
Prioritizing the severity of the water deficit in vineyards is further essential to take early …
Prioritizing the severity of the water deficit in vineyards is further essential to take early …
Hessian regularization of deep neural networks: A novel approach based on stochastic estimators of Hessian trace
In this paper, we develop a novel regularization method for deep neural networks by
penalizing the trace of Hessian. This regularizer is motivated by a recent guarantee bound of …
penalizing the trace of Hessian. This regularizer is motivated by a recent guarantee bound of …