This study presents a novel, integrated modeling framework that combines machine learning (ML) techniques with physics-based approaches to forecast both CO2 emissions and global temperature anomalies. Unlike prior research that typically addresses these components in isolation, this work concurrently applies and compares five advanced ML models—Long Short-Term Memory (LSTM), Extreme Gradient Boosting

Large-scale solar promises a low-carbon energy alternative. However, solar production in North America given anticipated climate change has been studied only seasonally in terms of solar irradiance. This work integrates more of the predictive potential of climate-change models by exploring other environmental variables, such as humidity and temperature. Here, a Continental US (CONUS) model is produced

Social media usage surges during natural disasters, offering critical insights into public sentiment and needs. This study leverages artificial intelligence (AI) and advanced natural language processing (NLP) techniques to analyze Twitter (X) data from the 2018 California Camp Fire. By combining sentiment analysis, emotion classification, and humanitarian topic classification, we provide a nuanced

Machine-learning algorithms have been employed in river ice research for flood estimation. This study aimed to introduce a machine learning-based model for predicting ice jam floods. An ice-jam dataset was created using a stochastic modelling approach in which thousands of possible scenarios were simulated. This approach integrated a hydrodynamic model, RIVICE, into a Monte Carlo Analysis (MOCA) framework

This systematic review explores the need for explainability in computational intelligence methods for optimization, such as metaheuristic optimizers, including evolutionary algorithms and swarm intelligence. The work focuses on four aspects: (1) the contribution of Explainable AI (XAI) methods to interpreting metaheuristic performance; (2) the influence of problem features on search behavior and explainability;

Recent studies have shown that LSTM performs well in runoff prediction in large sample regional modeling and can estimate hydrological concepts based on its internal information. However, compared to process-based models, it still produces erroneous predictions that violate the physical laws. To explore the reasons for the above phenomenon, this study analyzes the evolution of LSTM's performance in

Accurately simulating soil organic carbon (SOC) dynamics is essential for carbon-related assessments. Process-oriented SOC models employ temperature (f(T)) and soil moisture (f(W)) response functions derived from specific conditions to simulate SOC responses to climate change, yet are widely applied in regional and global-scale studies. How these functions affect regional SOC simulations remains unclear

Abstractive text summarization addresses information overload by generating paraphrased content that mimics human expression, yet it faces significant computational and linguistic challenges. This paper presents a detailed functional taxonomy of abstractive summarization, structured along four dimensions: techniques (including structure-based, semantic, and deep learning approaches, including large

Farming is increasingly data-driven, leveraging high-frequency and precision data from IoT devices, sensors, and remote tools. Effective data collection, organization, and management are essential to link datasets with agronomic details, forming the foundation for predictive models. These models, using AI and machine learning, optimize decision-making, forecast crop yields, predict pest outbreaks,

Urban flood forecasting is crucial for timely public warnings and effective flood management. Traditional mechanistic models face challenges such as high computational costs and limited real-time capabilities. Recent advancements in Artificial Intelligence (AI), including machine learning (ML), deep learning (DL), and large language models (LLMs), address these limitations by improving data handling

The rainfall-runoff relationship frequently undergoes changes and exhibits a non-stationary state due to the impacts of climate and human activities. This non-stationarity often results in performance degradation of most existing runoff prediction models, which were designed and applied under the assumption of a stationary rainfall-runoff relationship. This study proposes an adaptive rainfall-runoff

The efficacy of a deep learning error-updating model in predicting the hydrological model-simulated errors influenced by reservoir regulation is assessed. Two daily discharge forecasting model frameworks without (Case I) and with (Case II) error-updating of the discharge forecasts at a downstream location are developed. The best discharge forecasts are forced as inputs to a hydrodynamic model to simulate

Prescribed fires are planned to achieve conservation and fuel reduction objectives while minimizing smoke ground concentration to limit health impacts and road visibility impairment. Prescribed burns cannot indeed be conducted if those hazards are not within predefined limits. This paper proposes a new framework to evaluate road visibility that overcomes the limitation of the state of the art model

A stochastic generator for rainfall is built from a Hawkes process, which is modeling the occurrence and serial correlation of non-zero rainfall values. Hawkes processes are suited to model intermittent signals, which is the case of rainfall at a fine enough observation frequency. This Hawkes process has a two-scale intensity function accounting for two orders of clustering in rainfall time series

Natural environmental systems and human activities are deeply interconnected, especially in agriculture. Despite advancements in agricultural techniques, weather remains a critical factor influencing crop yields and livestock health. Precision agriculture relies on weather predictions to mitigate environmental risks caused by weather. However, numerical weather predictions are generated by global or

Identifying rainfall-runoff events is routinely performed in many hydrologic applications. Absence of a ground-based truth makes rainfall-runoff event identification largely subjective. As a result, current algorithms often disagree on the start and end of events, leading to events within a given set of rainfall and runoff time-series with inconsistent properties – referred to hereafter as ‘uncertainty

The unique freeze‒thaw cycle in cold regions complicates irrigation. Field monitoring and experiments simulated the water cycle during thawing and growing periods, analyzing hydraulic connections. This led to coupling a hydrological balance model, the Environmental Policy Integrated Climate (EPIC) model, and a carbon emission model into a multi-objective optimization framework for rice irrigation,

The number of web portals and online tools to support or inform decision-making on environmental and climate issues has grown steadily in recent decades. This paper explores the benefits and challenges of applying user-centred design (UCD) in environmental tool development, drawing on three case studies at the science-policy interface. We examine the roles and perspectives of scientists, funders, software

Hazard maps for shallow landslides at the basin or regional scale often provide information solely about past events and/or potential source areas. Despite the availability of several propagation assessment software tools, runout maps for potential shallow landslides at the basin scale remain scarce.

As network science emerges as a transformative tool in the ‘Big Science’ era, this study harnesses this tool to model ground-level ozone distribution dynamics across US states under different regulatory frameworks from 1980 to 2017. The evolution of these regulations provides a unique natural experiment to analyze how network-driven models evolve amidst varied environmental policies. By constructing

Assessing agroecological similarity is crucial for shaping sustainable agricultural practices and resource allocation, especially in regions undergoing rapid environmental changes. Current evaluation methods face challenges such as managing large datasets, adjusting for temporal variations across locations, and the need for accessible, comprehensive analytical tools. Addressing these challenges, this

The prediction of water quality parameters is vital for sustainable aquaculture. Dissolved oxygen (DO), a key factor influencing the health and growth of aquatic organisms, is challenging to predict due to its non-linearity and significant time lag. This study proposed a DO time-series prediction model based on Transformer architecture. A dynamic interpretable time-series decomposition strategy was

Reverse engineering CAD models from raw geometry is a classic but challenging research problem. In particular, reconstructing the CAD modeling sequence from point clouds provides great interpretability and convenience for editing. Analyzing previous work, we observed that a CAD modeling sequence represented by tokens and processed by a generative model does not have an immediate geometric interpretation

Incremental Potential Contact (IPC) is a widely used, robust, and accurate method for simulating complex frictional contact behaviors. However, achieving high efficiency remains a major challenge, particularly as material stiffness increases, which leads to slower Preconditioned Conjugate Gradient (PCG) convergence, even with the state-of-the-art preconditioners. In this paper, we propose a fully GPU-optimized

Quantum image processing uses quantum hardware to revolutionize the storage, recovery, processing, and security of quantum images across diverse applications. Although researchers have explored various facets of quantum image processing, a comprehensive systematic literature review encompassing all domains is essential for theoretical and experimental progress. This article aims to bridge this gap

Simulating stiff materials has long posed formidable challenges for traditional physics-based solvers. Explicit time integration schemes demand prohibitively small time steps, while implicit methods necessitate an excessive number of iterations to converge, often yielding visually objectionable transient configurations in the early iterations, severely limiting their real-time applicability. Position-based

Visual coverage estimation of seagrass for ground truth verification is one of the most critical aspects of marine ecosystem monitoring programs worldwide. It has traditionally been an arduous and tedious task. Commonly used tools like a scuba diver and underwater video transects require manual investigation by domain experts to assess seagrass status. Supervised machine learning methods have had a

With the development of technology, drones, and satellites play an important role in human life. Related research problems receive great attention, especially in the computer vision community. Notably, the object detection models on aerial imagery take part in many applications in both civil and military domains. Although it has great potential and has achieved many achievements, it cannot be denied

Designing a freeform surface to reflect or refract light to achieve a target distribution is a challenging inverse problem. In this paper, we propose an end-to-end optimization strategy for an optical surface mesh. Our formulation leverages a novel differentiable rendering model, and is directly driven by the difference between the resulting light distribution and the target distribution. We also enforce

Multi-objective optimization (MOO) is becoming increasingly important in environmental decision making, but interpreting highly-dimensional Pareto optimal data often constitutes a cognitive overload for both scientists and stakeholders. To address this challenge, we present PyretoClustR, a modular framework for post-processing Pareto optimal solutions. This tool aims to increase accessibility and applicability

This article compares novel and existing uncertainty quantification approaches for semantic segmentation used in remote sensing applications. We compare the probability estimates produced by a neural network with Monte Carlo dropout-based approaches, including predictive entropy and mutual information, and conformal prediction-based approaches, including feature conformal prediction (FCP) and a novel

This work presents a tool that enhances the hydrological flood modeling process at the event scale by integrating geospatial analysis capabilities, hydrological algorithms, and data. The main purpose is to overcome some of the main simplifications made in many modeling flood hydrographs, contributing to better simulate peak flow hydrographs for fixed return period (i.e., design flood). By leveraging

PyDDC is a particle tracking reservoir simulator capable of solving non-linear density driven convection of single phase carbon-dioxide (CO2)–brine fluid mixture in saturated porous media at the continuum scale. In contrast to the sate-of-the-art Eulerian models, PyDDC uses a Lagrangian approach to simulate the Fickian transport of single phase solute mixtures. This introduces additional flexibility

Holographic displays hold the promise of providing authentic depth cues, resulting in enhanced immersive visual experiences for near-eye applications. However, current holographic displays are hindered by speckle noise, which limits accurate reproduction of color and texture in displayed images. We present HoloChrome, a polychromatic holographic display framework designed to mitigate these limitations

Man-made 3D shapes are naturally organized in parts and hierarchies; such structures provide important constraints for shape reconstruction and generation. Modeling shape structures is difficult, because there can be multiple hierarchies for a given shape, causing ambiguity, and across different categories the shape structures are correlated with semantics, limiting generalization. We present StructRe

Modelling urban floods is essential for disaster prevention, yet it faces limitations in accuracy due to technical, operational, and functional constraints. The study employs a primary market research analysis to explore the perspectives of both academic and non-academic experts in urban flood modelling (UFM). Identified issues include inadequate spatial and temporal model resolution, high data requirements