Related papers: A comparative study of non-deep learning, deep lea…
This article implements a Convolutional Neural Network (CNN)-based deep learning model for solar-wind prediction. Images from the Atmospheric Imaging Assembly (AIA) at 193\.A wavelength are used for training. Solar-wind speed is taken from…
Sea surface temperature (SST) variability plays a key role in the global weather and climate system, with phenomena such as El Ni\~{n}o-Southern Oscillation regarded as a major source of interannual climate variability at the global scale.…
In this paper, we introduce a novel methodology for leveraging shape-based characteristics of magnetograms of active region (AR) patches and provide a novel capability for predicting solar flares covering the entirety of the solar disk (AR…
The Beijing-Arizona Sky Survey (BASS) Data Release 3 (DR3) catalogue was released in 2019, which contains the data from all BASS and the Mosaic z-band Legacy Survey (MzLS) observations during 2015 January and 2019 March, about 200 million…
As the peak of the solar cycle approaches in 2025 and the ability of a single geomagnetic storm to significantly alter the orbit of Resident Space Objects (RSOs), techniques for atmospheric density forecasting are vital for space…
Accurate prediction of crop yield supported by scientific and domain-relevant insights, can help improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop…
Flash points of organic molecules play an important role in preventing flammability hazards and large databases of measured values exist, although millions of compounds remain unmeasured. To rapidly extend existing data to new compounds…
The ability to identify stock market trends has obvious advantages for investors. Buying stock on an upward trend (as well as selling it in case of downward movement) results in profit. Accordingly, the start and end-points of the trend are…
Predictions in the form of probability distributions are crucial for effective decision-making. Quantile regression enables such predictions within spatial prediction settings that aim to create improved precipitation datasets by merging…
Climate change affects ocean temperature, salinity and sea level, impacting monsoons and ocean productivity. Future projections by Global Climate Models based on shared socioeconomic pathways from the Coupled Model Intercomparison Project…
Developing an accurate prediction model for housing prices is always needed for socio-economic development and well-being of citizens. In this paper, a diverse set of machine learning algorithms such as XGBoost, CatBoost, Random Forest,…
We discuss recent papers very critical of our Group Sunspot Number Series (Svalgaard & Schatten [2016]). Unfortunately, we cannot support any of the concerns they raise. We first show that almost always there is simple proportionality…
Active Learning aims to optimize performance while minimizing annotation costs by selecting the most informative samples from an unlabelled pool. Traditional uncertainty sampling often leads to sampling bias by choosing similar uncertain…
The increased amount of information provided by ongoing missions such as the Solar Dynamics Observatory (SDO) represents a great challenge for the understanding of basic questions such as the internal structure of sunspots and how they…
This paper combines a techno-economic energy system model with an econometric model to maximise electricity price forecasting accuracy. The proposed combination model is tested on the German day-ahead wholesale electricity market. Our paper…
Unsupervised ensemble learning emerged to address the challenge of combining multiple learners' predictions without access to ground truth labels or additional data. This paradigm is crucial in scenarios where evaluating individual…
Global leaders and policymakers are unified in their unequivocal commitment to decarbonization efforts in support of Net-Zero agreements. District Heating Systems (DHS), while contributing to carbon emissions due to the continued reliance…
Dangerous large wave put the coastal communities and vessels operating under threats and wave predictions are strongly needed for early warnings. While numerical wave models, such as WAVEWATCH III (WW3), are useful to provide spatially…
We study the sunspot activity in relation to spotless days (SLDs) during the descending phase of solar cycle $11$--$24$ to predict the amplitude of sunspot cycle $25$. For this purpose, in addition to SLD, we also use the geomagnetic…
The advancement of distributed generation technologies in modern power systems has led to a widespread integration of renewable power generation at customer side. However, the intermittent nature of renewable energy poses new challenges to…