Self-Organizing Maps provide quick flood predictions for real-time decision making.
problem Computational demand of physically-based overland flow models limits their use in real-time applications.
method Developed a flood-simulation specific SOM using cellular automata flood model results and synthetic DEM.
result SOMs can generate accurate water depth and flood extent results in a short time.
CNN model predicts fluvial floods quickly and accurately.
problem Real-time flood prediction is computationally demanding.
method Deep Convolutional Neural Network (CNN) trained on 2D hydraulic model outputs.
result CNN model outperforms SVR in predicting flood inundation.
Machine learning and IoT improve steam flood oil production.
problem Optimizing oil production from heavy-oil wells using steam floods.
method Cutting-edge machine learning techniques applied to time-series IoT data.
result 3% improvement in oil production with optimized steam allocation.
Hybrid deep learning model predicts urban floods with high accuracy.
problem Urban flood prediction and situation awareness using channel network sensors data.
method FastGRNN-FCN hybrid deep learning model trained on Harris County, Texas flood data.
result Test accuracy and F-measure reach 97.8% and 0.792, respectively.
Machine learning predicts flood risk across river basins.
problem Costly physics-based models are not generalizable.
method Supervised machine learning using remote sensing data.
result Machine learning models predict flood susceptibility.
Deep learning framework predicts streamflow and flood probabilities in Australian catchments.
problem Large-scale flooding prediction challenges due to model calibration and missing data.
method Ensemble quantile-based deep learning framework using quantile regression and CAMELS dataset.
result Notable efficacy and uncertainties in streamflow forecasts with varied catchment properties.
This paper reviews ML models for flood prediction, highlighting their benefits and effectiveness.
problem Complex modeling of floods to reduce risk and damage.
method Machine learning models for flood prediction.
result Hybridization, data decomposition, algorithm ensemble, and model optimization are effective strategies.
Study uses deep neural networks for flood forecasting.
problem Accurate flood predictions everywhere.
method Artificial deep neural networks for time-series forecasting.
result Neural networks improve flood predictions.
A new model predicts spatially varying inland flooding from time-varying inputs.
problem Ignoring time series and spatial correlations in flood models leads to inaccurate predictions.
method Introduced a multioutput Gaussian process model with separable kernels for functional inputs and spatial locations.
result The model provides accurate predictions of spatially varying inland flooding with minimal computational time.
Study improves flood loss risk models using historical data and rainfall data.
problem Predicting financial losses from flooding events.
method Used neural networks, decision trees, and kernel-based regressors on NFIP dataset, incorporating rainfall data.
result Extreme Gradient Boosting provided the best results, and bias correction improved model performance.
Machine learning predicts dam-break flood wave behavior accurately.
problem Predicting long-term wave behavior in dam-break floods.
method Solved Saint-Venant equations using Lax-Wendroff scheme, trained RC-ESN with flow depth data.
result RC-ESN model predicts 286 time-steps ahead with RMSE < 0.01, outperforming LSTM.
ML improves flood forecasting by leveraging local data.
problem Human calibration, limited data, and computational difficulty.
method Transfer learning and ML for high-dimensional scenarios.
result ML systems achieve timely and accurate flood prediction.
Real-time event detection using human sensor data and adaptive machine learning.
problem Lack of effective real-time event detection using human sensor data.
method Combination of corroborative and probabilistic sources with drift adaptive machine learning.
result Automated continuous learning maintains high performance in the face of concept drift.
LSTM model predicts climate impacts on floods and droughts.
problem Predicting climate impacts on individual watersheds is challenging.
method Large-scale LSTM training on extensive data sets.
result LSTM model outperforms state-of-the-art models in predicting extreme flows.
New method provides reliable high-confidence prediction intervals for high-impact events.
problem High-impact events require very high confidence prediction intervals, but classical methods provide uninformative intervals.
method Bridge extreme value statistics and conformal prediction to provide reliable and informative prediction intervals.
result Provides reliable and informative prediction intervals with high-confidence coverage.
Neural network model forecasts extreme flood risk.
problem Accurately estimating high quantiles of extreme events.
method EQRN model combining neural networks and extreme value theory.
result Forecasting flood risk with improved adaptability.
Data scientists guide to streamflow prediction and flood forecasting.
problem Forecasting floods and predicting streamflow volume.
method Explains hydrologic concepts and machine learning applications.
result Helps data scientists understand streamflow prediction.
Paper develops flood forecasting system for data scarce regions.
problem Flood forecasting in developing countries with data scarcity.
method Operational system for flood extent forecasts in India.
result Scalable and cost-efficient flood forecasting system.
Framework improves ML flood mapping generalization.
problem Improving machine learning models' ability to generalize to new conditions.
method Dimensionless, multi-scale features constrained by the Buckingham Π theorem.
result Model outperformed dimensional features, improving AUC in unmapped areas.
Elevating houses to flood risk increases uncertainty, leading to higher optimal elevations.
problem Deciding how high to elevate houses to manage riverine flood risks is complex due to uncertainties.
method Used a multi-objective robust decision-making framework to analyze uncertainties.
result Optimal house elevation can be significantly higher than FEMA's recommendation due to deep uncertainties.
Forecast dam inflow using sea surface feature weights.
problem Accurate dam inflow forecasting for flood mitigation.
method Extracted sea surface features, applied L2-norm ensemble weighting, used PCA and t-SNE for dimensionality reduction, and calibrated regression models.
result The proposed method improves predictor stability and accuracy in dam inflow forecasting.
This paper compares unstructured and structured EM-based semi-supervised learning methods.
problem Semi-supervised learning with EM algorithm for structured prediction.
method Comparative study between unstructured and structured EM-based semi-supervised learning methods.
result Structured EM is more robust to class confusion in flood mapping datasets.
This paper analyzes extreme flooding risks and proposes insurance and bond solutions.
problem Severe rise in magnitude and frequency of floods causing catastrophic losses.
method Extremes analysis using Peaks-Over-Threshold method and Point Process model; Value-at-Risk (VaR) and Conditional VaR (CVaR) estimation; Flood zoning insurance and catastrophic bond design.
result Developed flood risk vulnerability and threat analysis considering geography and economic factors; Proposed flood zoning insurance and catastrophic bond design.
Compact satellite tech detects flooded areas quickly.
problem Rapid access to satellite data and automatic flood detection.
method Automatically identifies cloud, water, and land in satellite images onboard small satellites.
result Reduced downlink bandwidth by 2 orders of magnitude.
Study finds more flood risk strategies can improve outcomes in NYC.
problem Managing future flood risks with complex models.
method Used an intermediate complexity model to analyze flood risk strategies.
result More combinations of risk mitigation strategies expand the solution set and improve outcomes.
Paper proposes a novel approach to improve spatiotemporal precipitation forecasts.
problem Improving accuracy of spatiotemporal precipitation forecasts for flood damage mitigation.
method Introduces a rain-code fusion approach using ConvLSTM and multi-frame fusion for spatiotemporal precipitation code-to-code forecasting.
result Demonstrates enhanced accuracy in precipitation forecasts beyond 3 timesteps using the rain-code fusion.
Flooding prevents deep networks from achieving zero training loss, improving test performance.
problem Deep networks can achieve zero training error but often have zero training loss, leading to overconfidence and poor test performance.
method Flooding prevents training loss from reaching zero by applying gradient ascent when loss is below a preset flood level.
result Flooding improves test performance and induces a double descent curve of the test loss.
SoftAD improves classification accuracy with less fine-tuning and fewer computational costs.
problem Improving classification accuracy with less fine-tuning and fewer computational costs.
method SoftAD is a softened, pointwise mechanism that downweights borderline points and limits the effects of outliers.
result SoftAD achieves classification accuracy competitive with flooding and SAM, with a smaller loss generalization gap and model norm.
New protocol reduces communication costs for heterogeneous bandits over complex networks.
problem Minimizing group regret in a multi-agent, heterogeneous bandit setting over complex networks.
method Flooding with Absorption (FwA) protocol for heterogeneous bandits over complex networks.
result FwA protocol significantly reduces communication costs compared to flooding while maintaining similar regret performance.
LSTM model predicts rainfall runoff with high temporal resolution.
problem Accurate and efficient rainfall runoff simulations for flood risk management.
method Data-driven rainfall runoff model using Long-short-Term-Memory (LSTM) networks.
result LSTM model achieves high-resolution discharge predictions with improved performance.
HydroNets use river structure to improve hydrologic predictions.
problem Scalable and accurate hydrologic models are needed for climate change impacts.
method HydroNets are deep neural networks that incorporate river network structure.
result HydroNets improve predictions with fewer data, especially at longer horizons.
Flood extent mapping plays a crucial role in disaster management and national water forecasting. Unfortunately, traditional classification methods are often hampered by the existence of noise, obstacles and heterogeneity in spectral features as well as implicit anisotropic spatial dependency across class labels. In thi…
Predicts student outcomes in real-time using domain adaptation.
problem Real-time student performance prediction in online courses.
method GritNet architecture with unsupervised domain adaptation.
result GritNet enhances real-time predictions, especially in early weeks.
Low-cost water-level tracking using LTE power metrics and wavelet analysis.
problem Real-time water-level monitoring across many locations with fixed instruments.
method Extracts per-antenna RSRP, RSSI, and RSRQ, applies CWT to RSRP, and uses a neural network to track water-level changes.
result Achieves root-mean-square and mean-absolute errors of 0.8 cm and 0.5 cm, respectively, under line-of-sight conditions.
Neural process model improves real-time condition monitoring signal prediction.
problem Real-time adaptation for complex condition monitoring signals.
method Label-aware neural processes encoding and reconstruction.
result Advantages in real-time adaptation, enhanced signal prediction with uncertainty quantification, and joint prediction for labels and signals.
Study uses RNN for real-time crypto price prediction and trading optimization.
problem High volatility in cryptocurrency markets makes traditional forecasting models unreliable.
method Data collection, preprocessing, model refinement, and backtesting.
result Improved accuracy in real-time crypto price prediction and optimized trading strategies.
Deep model predicts robot trajectories in real time.
problem Real-time robot trajectory prediction with low latency.
method Deep conditional generative model trained with SGDVB.
result More accurate long-term predictions with lower latency.
BusTr predicts bus travel times from real-time traffic forecasts.
problem Improving accuracy of bus travel time predictions.
method Neural sequence model trained on real-time traffic forecasts.
result BusTr outperforms DeepTTE by 30% in Mean Absolute Percentage Error (MAPE).
KryptoOracle predicts cryptocurrency prices using Twitter sentiments.
problem Real-time price prediction for high-volatility cryptocurrencies.
method Spark-based architecture, sentiment analysis, online learning.
result Real-time adaptation of learning algorithms to new data.
Dividing local Gaussian processes improve real-time prediction efficiency.
problem Efficient online prediction for large data sets.
method Iterative data-driven division of input space for sublinear computational complexity.
result Sublinear computational complexity in real-time prediction.
Model predicts parking area states up to 60 mins ahead.
problem Predicting parking area states for urban traffic management.
method Neural Network-based model using real-time and historic data.
result Model outperforms naive models by over 150% at 60 mins prediction.
Model predicts real-time job applicant numbers for regional economic analysis.
problem Real-time economic analysis using alternative data.
method Mixed-Frequency Aggregate Learning (MF-AGL) model.
result Model accurately predicts regional labor market conditions and economic status changes.
The study models insurance dependence using Bernstein copulas.
problem Modeling dependence structures in nonlife insurance data.
method Review and suggest fitting Bernstein copulas to empirical data.
result Monte Carlo simulation and PML estimation for aggregate losses.
Study optimizes climate adaptation strategies for NYC.
problem Catastrophic damages from extreme weather in NYC.
method Real options analysis and extreme value theory.
result Optimal adaptation pathways identified for NYC.
Predict real-time crash risks during hurricane evacuations using connected vehicle data.
problem Mitigate crash risks during hurricane evacuations by predicting high-risk locations.
method Used connected vehicle data to predict crash risks in real-time, considering weather and traffic features.
result Gaussian Process Boosting and Extreme Gradient Boosting models performed best, with recall of 0.91.
Increasingly fast development and update cycle of online course contents, and diverse demographics of students in each online classroom, make student performance prediction in real-time (before the course finishes) and/or on curriculum without specific historical performance data available interesting topics for both i…
Engine predicts real-time air quality with high resolution.
problem Real-time prediction of air pollutants for health monitoring.
method Combines official data, models, land cover, traffic data for high-resolution predictions.
result Engine produces predictions with resolution of a few dozen meters.
Meta-analysis and systematic review can help distill machine learning research.
problem Difficulties in distilling a flood of machine learning papers into useful principles.
method Use meta-analysis and systematic review to aggregate and analyze machine learning research.
result Meta-analysis and systematic review can help in distilling machine learning research.