Climate & Environment

122 verified cases, highest-rated first.

Industries: weather forecasting 27, environmental monitoring 5, grid forecasting 4, flood forecasting 4, wildfire management 3, agriculture 3, precision agriculture 3, building energy 3, renewable energy 3, remote sensing 3, environmental health 2, emissions monitoring 2

• Global high-resolution ultrafine particle number concentrations through data fusion with machine learning

  No global high-resolution datasets of ultrafine particle (UFP) number concentrations existed, limiting assessment of a critical but under-measured health risk.

  Climate & Environment · 2025 · solved · 8/10 importance

• Machine Learning in Carbon Capture, Utilization, Storage, and Transportation: A Review of Applications in Greenhouse Gas Emissions Reduction

  CCUS processes face technical and economic challenges in optimization and scalability that hinder reducing greenhouse gas emissions.

  Climate & Environment · 2025 · ai assisted · 8/10 importance

• Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer

  Detecting methane point-source emissions from satellites was limited by trade-offs between coverage, resolution and detection accuracy.

  Climate & Environment · 2024 · solved · 8/10 importance

• Learning skillful medium-range global weather forecasting

  Producing accurate 10-day global weather forecasts, traditionally requiring heavy compute rather than learning from historical weather data.

  Climate & Environment · 2023 · solved · 8/10 importance

• Accurate medium-range global weather forecasting with 3D neural networks

  Numerical weather prediction is accurate but computationally expensive, while prior AI forecasting methods were much faster but significantly less accurate. The paper aims to make AI-based medium-range global weather forecasting accurate.

  Climate & Environment · 2023 · partially solved · 8/10 importance

• FuXi: a cascade machine learning forecasting system for 15-day global weather forecast

  Medium-range weather forecasts accumulate errors beyond 10 days, so ML models still lag the ECMWF ensemble for 15-day forecasts.

  Climate & Environment · 2023 · partially solved · 8/10 importance

• Skilful nowcasting of extreme precipitation with NowcastNet

  Extreme precipitation is a considerable contributor to meteorological disasters, and existing nowcasting methods suffer blur, dissipation, intensity or location errors; physics-based methods miss chaotic dynamics while data-driven methods violate physical laws…

  Climate & Environment · 2023 · partially solved · 8/10 importance

• Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

  Renewable energy generation is variable and uncertain, making accurate forecasting difficult, which complicates efficient grid operation and energy management.

  Climate & Environment · 2023 · ai assisted · 8/10 importance

• An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and Deep Learning Algorithms with Advanced Alert System

  Floods cause casualties worldwide, and accurate precipitation and flood forecasting is needed to reduce damage and save lives.

  Climate & Environment · 2023 · ai assisted · 8/10 importance

• Predicting Rapid Intensification in North Atlantic and Eastern North Pacific Tropical Cyclones Using a Convolutional Neural Network

  Predicting rapid intensification of tropical cyclones in the North Atlantic and eastern North Pacific, where storms suddenly strengthen faster than traditional guidance anticipates.

  Climate & Environment · 2022 · partially solved · 8/10 importance

• Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning

  Arsenic contamination affects over 200 million people, but current detection methods are laborious, expensive, and unscalable.

  Climate & Environment · 2021 · partially solved · 8/10 importance

• Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion

  Achieving high methanol yields and stable catalysts for CO2 thermoreduction remains a significant challenge for closing the carbon cycle.

  Climate & Environment · 2025 · ai assisted · 7/10 importance

• Aboveground biomass estimation in a grassland ecosystem using Sentinel-2 satellite imagery and machine learning algorithms

  Aboveground biomass of grasslands must be estimated accurately to track carbon stocks and the carbon cycle.

  Climate & Environment · 2025 · ai assisted · 7/10 importance

• Deep learning of flood forecasting by considering interpretability and physical constraints

  Deep learning flood forecasting models often lack interpretability and physical realism, limiting trust in predictions.

  Climate & Environment · 2025 · ai assisted · 7/10 importance

• Enhancing PET Degrading Enzymes: A Combinatory Approach

  Plastic PET waste accumulates as a major environmental issue, and natural PET-degrading enzymes need improved efficiency for enzymatic recycling.

  Climate & Environment · 2024 · ai assisted · 7/10 importance

• U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers

  Detecting atmospheric methane plumes and quantifying point-source rates from growing high-resolution satellite imagery is labor-intensive and not scalable.

  Climate & Environment · 2024 · partially solved · 7/10 importance

• Review of Methods for Automatic Plastic Detection in Water Areas Using Satellite Images and Machine Learning

  Ocean plastic pollution forms growing 'rubbish islands' that damage marine ecosystems; sources, accumulation sites and movement need to be identified and tracked.

  Climate & Environment · 2024 · ai assisted · 7/10 importance

• METEO-DLNet: Quantitative Precipitation Nowcasting Net Based on Meteorological Features and Deep Learning

  Short-term precipitation forecasting via radar echo extrapolation struggles to account for inherent meteorological characteristics and local images that deviate from the overall trend.

  Climate & Environment · 2024 · ai assisted · 7/10 importance

• Predicting Short-Term Intensity Change in Tropical Cyclones Using a Convolutional Neural Network

  Predicting current intensity and short-term intensity change of tropical cyclones more skillfully than existing operational methods.

  Climate & Environment · 2024 · partially solved · 7/10 importance

• Weed Detection Using Deep Learning: A Systematic Literature Review

  Weeds are harmful agricultural pests that raise production costs and damage the global agricultural economy, motivating early detection technology for farmers.

  Climate & Environment · 2023 · partially solved · 7/10 importance

• An Efficient Wildfire Detection System for AI-Embedded Applications Using Satellite Imagery

  Detecting wildfires quickly from satellite imagery so that response can limit damage to humans and wildlife.

  Climate & Environment · 2023 · ai assisted · 7/10 importance

• Air pollution particulate matter (PM2.5) prediction in South African cities using machine learning techniques

  Predicting PM2.5 particulate pollution in South African cities, where reliable air quality sensors are scarce.

  Climate & Environment · 2023 · ai assisted · 7/10 importance

• Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management

  Estimating 10-hour dead fuel moisture content across the contiguous US is needed to manage and mitigate wildland fires.

  Climate & Environment · 2023 · partially solved · 7/10 importance

• A review of the application of machine learning in water quality evaluation

  Evaluating and managing water quality across surface water, groundwater, drinking water, sewage, and seawater using growing environmental data.

  Climate & Environment · 2022 · ai assisted · 7/10 importance

• The Prediction of Wheat Yield in the North China Plain by Coupling Crop Model with Machine Learning Algorithms

  Accurately predicting wheat yield in the North China Plain to support regional food security.

  Climate & Environment · 2022 · solved · 7/10 importance

• Forecasting Amazon Rain-Forest Deforestation Using a Hybrid Machine Learning Model

  Predicting annual Amazon rain-forest deforestation area across 760 Brazilian municipalities from static geographic and 20-year time-series data.

  Climate & Environment · 2022 · partially solved · 7/10 importance

• Large-scale demonstration of machine learning for the detection of volcanic deformation in Sentinel-1 satellite imagery

  Radar satellites systematically image over 1000 volcanoes, but detecting deformation signals requires manual inspection of huge datasets, delaying timely eruption information.

  Climate & Environment · 2022 · partially solved · 7/10 importance

• Tropical Cyclone Track Prediction with an Encoding-to-Forecasting Deep Learning Model

  Predicting the tracks of tropical cyclones in the western North Pacific, where traditional dynamic and statistical methods struggle with efficiency and accuracy on large remote-sensing datasets.

  Climate & Environment · 2022 · ai assisted · 7/10 importance

• Hybrid Surrogate Model for Timely Prediction of Flash Flood Inundation Maps Caused by Rapid River Overflow

  Physically based hydraulic models are too computationally slow to generate flash flood inundation maps in real time for urban early warning systems.

  Climate & Environment · 2022 · partially solved · 7/10 importance

• Sub‐Seasonal Forecasting With a Large Ensemble of Deep‐Learning Weather Prediction Models

  Producing sub-seasonal (six-week) global weather forecasts with large ensembles is computationally prohibitive with traditional numerical models.

  Climate & Environment · 2021 · partially solved · 7/10 importance

• Improved Weather Forecasting Using Neural Network Emulation for Radiation Parameterization

  Radiation parameterization is computationally expensive in operational weather forecasting models, slowing forecasts.

  Climate & Environment · 2021 · solved · 7/10 importance

• Global Wildfire Outlook Forecast with Neural Networks

  Predicting monthly wildfire burned area up to one year in advance across major global fire regions to support planning and preparedness.

  Climate & Environment · 2020 · partially solved · 7/10 importance

• Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning

  Non-destructive, efficient crop monitoring over large areas is needed for precision agriculture, phenotyping, and food-security decisions.

  Climate & Environment · 2020 · ai assisted · 7/10 importance

• Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning

  Surface mines and mining tailings dams across Brazil, including unregistered ones, needed automatic identification from satellite imagery.

  Climate & Environment · 2020 · solved · 7/10 importance

• Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events

  Deep learning precipitation nowcasting models underperform on extreme rainfall rates due to prediction smoothing, weakening short-term flash-flood and thunderstorm forecasts.

  Climate & Environment · 2020 · solved · 7/10 importance

• Wildfire Susceptibility Mapping Using Deep Learning and Machine Learning Models Based on Multi-Sensor Satellite Data Fusion: A Case Study of Serbia

  Identifying wildfire-susceptible locations and key fire-driving factors across Serbia to support prevention and mitigation.

  Climate & Environment · 2025 · ai assisted · 6/10 importance

• The Rise of Data-Driven Weather Forecasting: A First Statistical Assessment of Machine Learning–Based Weather Forecasts in an Operational-Like Context

  Traditional numerical weather prediction is so computationally costly that improvements via higher resolution or larger ensembles are hindered.

  Climate & Environment · 2024 · partially solved · 6/10 importance

• Weed Detection from Unmanned Aerial Vehicle Imagery Using Deep Learning—A Comparison between High-End and Low-Cost Multispectral Sensors

  Reliable, affordable weed detection from drone imagery is needed so herbicides can be targeted rather than applied broadly across maize fields.

  Climate & Environment · 2024 · partially solved · 6/10 importance

• Deep learning for prediction of energy consumption: an applied use case in an office building

  Building operators need accurate estimates of office building energy consumption to identify inefficiencies and optimize energy management.

  Climate & Environment · 2024 · ai assisted · 6/10 importance

• The limitation of machine learning methods for water supply and demand forecasting: A case study for Greater Melbourne, Australia

  Cities facing water scarcity need accurate supply and demand forecasts for sustainable urban water management; the study tests ML efficacy for Greater Melbourne.

  Climate & Environment · 2024 · partially solved · 6/10 importance

• Reconstruction of tropical cyclone boundary layer wind field using physics-informed machine learning

  Reconstructing high-fidelity 3D boundary layer wind fields of tropical cyclones from limited observations is difficult.

  Climate & Environment · 2024 · ai assisted · 6/10 importance

• Deep learning model based on multi-scale feature fusion for precipitation nowcasting

  Deep learning models struggle to accurately forecast heavy precipitation, especially at longer lead times.

  Climate & Environment · 2024 · partially solved · 6/10 importance

• Temporal and spatial satellite data augmentation for deep learning-based rainfall nowcasting

  Improving short-term rainfall prediction is needed because climate-change-induced flash floods and severe flooding are increasing.

  Climate & Environment · 2024 · ai assisted · 6/10 importance

• Improving Precipitation Forecasts with Convolutional Neural Networks

  Dynamical model (West-WRF) daily precipitation forecasts over the US west coast contain systematic biases.

  Climate & Environment · 2023 · partially solved · 6/10 importance

• Machine learning in biohydrogen production: a review

  Operating parameters for biohydrogen production must be optimized to enable large-scale commercial uptake.

  Climate & Environment · 2023 · ai assisted · 6/10 importance

• Land Use and Land Cover Classification Meets Deep Learning: A Review

  Classifying land use and land cover from remote sensing images is difficult because the data is high-dimensional and labeled samples are limited.

  Climate & Environment · 2023 · ai assisted · 6/10 importance

• A spatial–temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features

  Predicting near-future urban flood conditions as an extreme weather event unfolds, to improve situational awareness.

  Climate & Environment · 2023 · ai assisted · 6/10 importance

• Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis

  Wind power output is hard to predict because wind speeds fluctuate, complicating grid operation, unit commitment, and turbine maintenance planning.

  Climate & Environment · 2023 · ai assisted · 6/10 importance

• Thunderstorm Nowcasting With Deep Learning: A Multi‐Hazard Data Fusion Model

  Short-term prediction of thunderstorm hazards (lightning, hail, heavy precipitation) is needed by first responders, infrastructure managers and aviation.

  Climate & Environment · 2023 · ai assisted · 6/10 importance

• 3D-UNet-LSTM: A Deep Learning-Based Radar Echo Extrapolation Model for Convective Nowcasting

  Predicting the very short-term evolution of convective storm systems by extrapolating radar reflectivity images.

  Climate & Environment · 2023 · ai assisted · 6/10 importance

• A Customized Multi‐Scale Deep Learning Framework for Storm Nowcasting

  Deep learning models perform poorly when nowcasting high-impact storms due to complex multi-scale spatiotemporal variations.

  Climate & Environment · 2023 · partially solved · 6/10 importance

• Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region

  Delineating lake area extent over time in semi-arid regions and assessing how hydro-climatological factors drive lake changes.

  Climate & Environment · 2023 · ai assisted · 6/10 importance

• Systematic Review of Deep Learning and Machine Learning for Building Energy

  Building energy management needs accurate, data-driven analysis and forecasting of energy consumption and demand for smarter, sustainable cities.

  Climate & Environment · 2022 · ai assisted · 6/10 importance

• Intelligent Forecasting of Air Quality and Pollution Prediction Using Machine Learning

  Predicting PM2.5 pollutant concentrations in polluted cities to support air quality monitoring.

  Climate & Environment · 2022 · ai assisted · 6/10 importance

• ProbSevere LightningCast: A Deep-Learning Model for Satellite-Based Lightning Nowcasting

  Lightning strikes threaten life and property but are difficult to forecast in a timely, location-specific manner.

  Climate & Environment · 2022 · ai assisted · 6/10 importance

• Do Machine Learning Approaches Offer Skill Improvement for Short-Term Forecasting of Wind Gust Occurrence and Magnitude?

  Short-term forecasting of wind gust occurrence and magnitude, relevant to high passenger volume US airports, is extremely challenging.

  Climate & Environment · 2022 · ai assisted · 6/10 importance

• Deep learning for post-processing ensemble weather forecasts

  Ensemble weather forecasts are computationally expensive and their raw predictions need improvement, especially for extreme weather events.

  Climate & Environment · 2021 · partially solved · 6/10 importance

• Use of Deep Learning for Weather Radar Nowcasting

  Operationally deployed weather radar nowcasting systems did not use deep learning; the study explored deep learning alternatives for predicting future radar echoes.

  Climate & Environment · 2021 · partially solved · 6/10 importance

• Ensemble Methods for Neural Network‐Based Weather Forecasts

  Deterministic neural network weather forecasts provide no measure of uncertainty, and generating ensembles with a good spread-error relationship is non-trivial.

  Climate & Environment · 2020 · partially solved · 6/10 importance

• Probabilistic Forecasting of El Niño Using Neural Network Models

  Statistical models predicting the El Niño–Southern Oscillation lacked direct estimation of predictive uncertainty for each forecast.

  Climate & Environment · 2020 · partially solved · 6/10 importance

• Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment

  Distinguishing oil spills from lookalikes and accurately modeling spill trajectories remain difficult for emergency response.

  Climate & Environment · 2020 · partially solved · 6/10 importance

• Landslides Information Extraction Using Object-Oriented Image Analysis Paradigm Based on Deep Learning and Transfer Learning

  Quickly and accurately extracting landslide disaster information from UAV remote sensing images is hard because spectral information is deficient and manual interpretation is too slow for emergencies.

  Climate & Environment · 2020 · ai assisted · 6/10 importance

• Monitoring hydropower reliability in Malawi with satellite data and machine learning

  Assessing hydropower reliability under hydro-climatic extremes in Malawi, where ground observations are scarce.

  Climate & Environment · 2020 · ai assisted · 6/10 importance

• Convolutional Neural Network for Convective Storm Nowcasting Using 3-D Doppler Weather Radar Data

  Short-term forecasting (nowcasting) of convective storms is difficult because they are small-scale and short-lived, and extracting information from 3-D radar data was previously problematic.

  Climate & Environment · 2019 · partially solved · 6/10 importance

• Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks

  GRACE-derived terrestrial water storage needed to be forecast to predict drought events across 10 major African watersheds.

  Climate & Environment · 2019 · partially solved · 6/10 importance

• Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model

  Physics-based planetary boundary layer (PBL) parameterizations in numerical weather forecast models are computationally expensive.

  Climate & Environment · 2019 · ai assisted · 6/10 importance

• Benchmark analysis of day-ahead solar power forecasting techniques using weather predictions

  Day-ahead prediction of power output of individual and aggregated PV systems is needed to keep the electricity grid reliable as solar penetration grows.

  Climate & Environment · 2019 · ai assisted · 6/10 importance

• Predicting day-ahead solar irradiance through gated recurrent unit using weather forecasting data

  Solar power operators need accurate day-ahead irradiance forecasts for planning, scheduling and managing photovoltaic plants and grid-connected systems.

  Climate & Environment · 2019 · solved · 6/10 importance

• Neural Networks for Postprocessing Ensemble Weather Forecasts

  Ensemble weather forecasts contain systematic errors and need statistical postprocessing to produce reliable probabilistic forecasts, traditionally done with distributional regression and prespecified link functions.

  Climate & Environment · 2018 · solved · 6/10 importance

• Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches

  Aircraft icing remains a critical aviation threat, and no robust model existed to detect potential icing areas over East Asia from geostationary satellite data.

  Climate & Environment · 2018 · partially solved · 6/10 importance

• A machine learning approach for wind turbine power forecasting for maintenance planning

  Wind turbine power output is hard to predict, making it difficult to schedule maintenance without losing energy production.

  Climate & Environment · 2025 · ai assisted · 5/10 importance

• Research on building energy consumption prediction algorithm based on customized deep learning model

  Building energy consumption is hard to forecast accurately due to uncertainty and noise, hindering energy-saving measures.

  Climate & Environment · 2025 · ai assisted · 5/10 importance

• Comparative Analysis of Machine-Learning Models for Soil Moisture Estimation Using High-Resolution Remote-Sensing Data

  Estimating soil moisture from high-resolution remote-sensing data (SAR and optical) is needed for agriculture, climate and disaster monitoring, but comprehensive comparisons of machine-learning techniques were limited.

  Climate & Environment · 2024 · ai assisted · 5/10 importance

• Optimizing Crop Yield Prediction: Data-Driven Analysis and Machine Learning Modeling Using USDA Datasets

  Predicting crop yields and identifying the underlying factors that cause differences in agricultural output, using USDA data from 2003 to 2013.

  Climate & Environment · 2024 · ai assisted · 5/10 importance

• Embedding the weather prediction errors (WPE) into the photovoltaic (PV) forecasting method using deep learning

  Photovoltaic power forecasts for on-grid, hybrid and off-grid systems are degraded by weather prediction errors, hurting renewable energy use and market bids.

  Climate & Environment · 2024 · partially solved · 5/10 importance

• A Deep Learning Model for Precipitation Nowcasting Using Multiple Optical Flow Algorithms

  Traditional optical-flow precipitation nowcasting has limited performance and forecast time scale.

  Climate & Environment · 2024 · ai assisted · 5/10 importance

• War and Deforestation: Using Remote Sensing and Machine Learning to Identify the War-Induced Deforestation in Syria 2010–2019

  Measuring and attributing forest cover loss in Syria during the 2011–2019 armed conflict.

  Climate & Environment · 2023 · solved · 5/10 importance

• Air Quality Index (AQI) Prediction in Holy Makkah Based on Machine Learning Methods

  Predicting air quality index levels in Makkah is hard due to variable spatiotemporal pollution, especially with millions of Hajj and Ramadan visitors.

  Climate & Environment · 2023 · ai assisted · 5/10 importance

• Precipitation Nowcasting Based on Deep Learning over Guizhou, China

  Accurate 0-2 hour precipitation nowcasting at high spatiotemporal resolution is needed for weather-dependent social and operational activities.

  Climate & Environment · 2023 · ai assisted · 5/10 importance

• Correcting Systematic and State‐Dependent Errors in the NOAA FV3‐GFS Using Neural Networks

  Imperfect weather models contain systematic, state-dependent errors that degrade forecasts and are only partially corrected by data assimilation.

  Climate & Environment · 2022 · partially solved · 5/10 importance

• Low Cloud Detection in Multilayer Scenes Using Satellite Imagery with Machine Learning Methods

  Detecting low clouds hidden beneath higher cloud layers in satellite imagery, which is hard for passive sensors and matters for aviation nowcasting and weather forecasting.

  Climate & Environment · 2022 · ai assisted · 5/10 importance

• A stacking-based short-term wind power forecasting method by CBLSTM and ensemble learning

  Traditional short-term wind power forecasting struggles with large, highly volatile time-series data and limited processing capacity.

  Climate & Environment · 2022 · ai assisted · 5/10 importance

• Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study

  Classifying land use and land cover from high-resolution remote sensing imagery is difficult and time-consuming, and training deep networks from scratch is costly.

  Climate & Environment · 2021 · ai assisted · 5/10 importance

• Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error

  Wind speed forecasts suffer errors from atmospheric non-linearities, and it was unclear which atmospheric variables help predict that error.

  Climate & Environment · 2021 · ai assisted · 5/10 importance

• Hybrid Forecasting Methodology for Wind Power-Photovoltaic-Concentrating Solar Power Generation Clustered Renewable Energy Systems

  Short-term forecasting of clustered hybrid renewable energy systems (wind, photovoltaic, concentrating solar power) is difficult due to spatial and temporal uncertainties.

  Climate & Environment · 2021 · ai assisted · 5/10 importance

• Using Artificial Neural Networks for Generating Probabilistic Subseasonal Precipitation Forecasts over California

  NWP models have limited skill for subseasonal 7-day precipitation accumulations over California, and low signal-to-noise ratio makes reliable probabilistic forecasts hard.

  Climate & Environment · 2020 · ai assisted · 5/10 importance

• Flood Prediction and Uncertainty Estimation Using Deep Learning

  Floods are non-linear and hard to predict, and existing physical/statistical models lack accuracy and only give six-hour updates.

  Climate & Environment · 2020 · partially solved · 5/10 importance

• Demystifying the use of ERA5‐land and machine learning for wind power forecasting

  Short-term wind power forecasting to mitigate wind inconsistency effects on the electric grid.

  Climate & Environment · 2020 · ai assisted · 5/10 importance

• Air Quality Index and Air Pollutant Concentration Prediction Based on Machine Learning Algorithms

  Air pollution forecasts are needed to warn people and control pollution; accurate prediction of AQI and pollutant concentrations was addressed.

  Climate & Environment · 2019 · ai assisted · 5/10 importance

• Predicting weather forecast uncertainty with machine learning

  Estimating confidence in weather forecasts currently requires expensive ensemble simulations, so a cheaper alternative was sought.

  Climate & Environment · 2018 · partially solved · 5/10 importance

• An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning

  Underground coal mines need air quality monitoring that goes beyond passive sensing to assess hazards and predict pollutant levels to protect miners.

  Climate & Environment · 2018 · ai assisted · 5/10 importance

• Cloud Detection for High-Resolution Satellite Imagery Using Machine Learning and Multi-Feature Fusion

  Accurately locating clouds in high-resolution satellite imagery, a prerequisite for atmospheric correction, land cover classification and target recognition.

  Climate & Environment · 2016 · solved · 5/10 importance

• LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting

  Traditional methods struggle to accurately forecast renewable energy demand when processing complex data, limiting sustainable planning.

  Climate & Environment · 2025 · ai assisted · 4/10 importance

• Reliable and efficient integration of AI into camera traps for smart wildlife monitoring based on continual learning

  Camera traps produce large image volumes that normally require off-site manual or desktop processing, hindering reliable on-site wildlife monitoring.

  Climate & Environment · 2024 · solved · 4/10 importance

• Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery

  Traditional remote-sensing methods cannot precisely classify dynamic and complex urban land areas.

  Climate & Environment · 2024 · ai assisted · 4/10 importance

• Transformer-based land use and land cover classification with explainability using satellite imagery

  Transformer models for land use and land cover (LULC) analysis from satellite imagery are too computationally expensive for practical use.

  Climate & Environment · 2024 · ai assisted · 4/10 importance

• Forest Disturbance Monitoring Using Cloud-Based Sentinel-2 Satellite Imagery and Machine Learning

  Forest damage has become more frequent in Hungary, and rapid, cost-effective monitoring of disturbances was needed.

  Climate & Environment · 2024 · ai assisted · 4/10 importance

• Automated visitor and wildlife monitoring with camera traps and machine learning

  Manually classifying hundreds of thousands of camera trap images to study human–wildlife interactions is slow and labor-intensive.

  Climate & Environment · 2023 · ai assisted · 4/10 importance

• Multi-Branch Deep Learning Framework for Land Scene Classification in Satellite Imagery

  Automatically classifying land scenes in high-resolution satellite imagery is difficult due to complex textures, cluttered backgrounds, small objects and large scale variations.

  Climate & Environment · 2023 · ai assisted · 4/10 importance

• Sweet corn yield prediction using machine learning models and field-level data

  Predicting field-level sweet corn yield from crop genetics, management, weather, and soil data.

  Climate & Environment · 2023 · partially solved · 4/10 importance

• Efficient Deep Semantic Segmentation for Land Cover Classification Using Sentinel Imagery

  Land cover classification is hindered by time-consuming annotation, imagery download, and pre-processing tasks that discourage policy makers from creating and using new land cover maps.

  Climate & Environment · 2023 · ai assisted · 4/10 importance

• Machine Learning-Based Land Use and Land Cover Mapping Using Multi-Spectral Satellite Imagery: A Case Study in Egypt

  Mapping land use and land cover (LULC) from satellite imagery is needed for environmental monitoring but is difficult to do accurately. The study built a prediction model to classify 8 LULC classes in Egypt from 3 m multi-spectral PlanetScope images.

  Climate & Environment · 2023 · solved · 4/10 importance

• Machine Learning-Based Cost-Effective Smart Home Data Analysis and Forecasting for Energy Saving

  Smart homes collect redundant sensor data, and homeowners lack tools to predict HVAC power use and save energy.

  Climate & Environment · 2023 · ai assisted · 4/10 importance

• Rain Discrimination with Machine Learning Classifiers for Opportunistic Rain Detection System Using Satellite Micro-Wave Links

  Oblique earth-space satellite links can detect rain in real time, but classifying rainy versus non-rainy periods from the indirect signal is difficult.

  Climate & Environment · 2023 · ai assisted · 4/10 importance

• Evaluation of Machine Learning Algorithms for Air Quality Index (AQI) Prediction

  Air quality in Uttarakhand, India has deteriorated with industrial and vehicle growth, so the study aimed to predict pollutant status and AQI for monitored cities.

  Climate & Environment · 2023 · ai assisted · 4/10 importance

• Machine learning for surrogate process models of bioproduction pathways

  Detailed process simulation models for biofuel and biochemical production are costly and computationally intensive, limiting sharing and reproduction.

  Climate & Environment · 2022 · ai assisted · 4/10 importance

• Deep Learning-Based Weed Detection in Turf: A Review

  Autonomous precision herbicide spraying in turf requires accurate and reliable detection of weeds.

  Climate & Environment · 2022 · partially solved · 4/10 importance

• A comparative evaluation of convolutional neural networks, training image sizes, and deep learning optimizers for weed detection in alfalfa

  Farmers need automated ways to detect weeds growing among alfalfa crops so control measures can be targeted.

  Climate & Environment · 2022 · ai assisted · 4/10 importance

• Peer-to-peer energy trading optimization in energy communities using multi-agent deep reinforcement learning

  Prosumers in energy communities need to optimize how much energy to trade and at what price in peer-to-peer markets.

  Climate & Environment · 2022 · ai assisted · 4/10 importance

• Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network

  Predicting the short-term evolution and long-term statistics of a multiscale chaotic Lorenz 96 system when only the large-scale variables are observable and the smaller-scale processes are never known.

  Climate & Environment · 2020 · partially solved · 4/10 importance

• ARPEGE Cloud Cover Forecast Postprocessing with Convolutional Neural Network

  Numerical weather prediction models have significant biases in cloud cover forecasts, a hard-to-forecast variable needed for planning land observation missions from space.

  Climate & Environment · 2020 · partially solved · 4/10 importance

• Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station

  Farmers need detailed short-to-medium-range, high-resolution local forecasts, but major numerical weather prediction models only provide 6-hourly forecasts at 10-20 km grid length.

  Climate & Environment · 2020 · ai assisted · 4/10 importance

• Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

  Extracting fine-grained features and objects from very high resolution Earth observation data is needed to better understand land-surface dynamics.

  Climate & Environment · 2020 · ai assisted · 4/10 importance

• Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning

  Reliably recognizing and mapping windthrow areas in forests from very-high-resolution satellite imagery.

  Climate & Environment · 2020 · ai assisted · 4/10 importance

• Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

  Temporary inundations in flat agricultural areas of southern Hungary disrupt farming and cause large financial loss; mapping land use/land cover, especially inundated areas, from medium-resolution satellite imagery is needed.

  Climate & Environment · 2020 · ai assisted · 4/10 importance

• Machine Learning Weather Soft-Sensor for Advanced Control of Wastewater Treatment Plants

  Weather conditions strongly affect inflow rates and substance concentrations in wastewater treatment plants, but this weather signal cannot be directly measured with traditional physical sensors, making plant control difficult.

  Climate & Environment · 2019 · ai assisted · 4/10 importance

• Toward Data‐Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning

  Whether deep learning can emulate the dynamics of weather and climate general circulation models to enable data-driven forecasting.

  Climate & Environment · 2018 · partially solved · 4/10 importance

• Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

  Improving urban land-cover classification accuracy using multi-sensor satellite imagery and determining which features contribute most.

  Climate & Environment · 2018 · ai assisted · 4/10 importance

• Evaluation of Thunderstorm Predictors for Finland Using Reanalyses and Neural Networks

  Identifying thunderstorm occurrences over Finland from reanalysis data using multiple environmental predictors.

  Climate & Environment · 2017 · partially solved · 4/10 importance

• Maize yield forecasting by linear regression and artificial neural networks in Jilin, China

  Forecasting maize yields in Jilin, China from 1962-2004 using climate conditions and fertilizer data, which is hard to obtain.

  Climate & Environment · 2014 · partially solved · 4/10 importance

• Land Cover Mapping from Colorized CORONA Archived Greyscale Satellite Data and Feature Extraction Classification

  Land cover mapping typically relies on multispectral data; extracting land cover information from archived greyscale CORONA satellite imagery is difficult.

  Climate & Environment · 2021 · ai assisted · 3/10 importance

• Visualization of Neural Network Predictions for Weather Forecasting

  A single loss value hides variations in recurrent neural network weather forecasts, making it hard to tune hyperparameters and understand model behavior on large datasets.

  Climate & Environment · 2018 · ai assisted · 3/10 importance

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