A Resilience Evaluation Framework for Electric Distribution Systems: Historical Weather Conditioning, Sensitivity Analysis, and a Flooding-Aware Extension

Amir Shahin Kamjou; Caisheng Wang; Carol Miller; John Norton; Xuesong Wang

arxiv: 2605.16811 · v1 · pith:ZR4X3IEDnew · submitted 2026-05-16 · 📡 eess.SY · cs.SY

A Resilience Evaluation Framework for Electric Distribution Systems: Historical Weather Conditioning, Sensitivity Analysis, and a Flooding-Aware Extension

Xuesong Wang , Caisheng Wang , Carol Miller , Amir Shahin Kamjou , John Norton This is my paper

Pith reviewed 2026-05-19 21:07 UTC · model grok-4.3

classification 📡 eess.SY cs.SY

keywords resilience evaluationelectric distribution systemshistorical weather conditioningsensitivity analysispower-flooding couplingMonte Carlo simulationfragility modelingoutage metrics

0 comments

The pith

An extended graph-based framework evaluates electric distribution resilience using historical wind events, sensitivity tests, and coupled flooding simulations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper extends an existing graph-based model to test how power distribution networks perform under severe weather when real historical events drive the simulations. It adds sensitivity checks on modeling choices like fragility curves and repair rules, plus a joint simulation that tracks how outages affect sewage backups. The work shows that key resilience numbers level off after roughly 256 Monte Carlo runs and that flooding risk rises with outage severity. If the underlying assumptions hold, the approach gives planners a workable way to compare scenarios and explore infrastructure links even with only public data available.

Core claim

The authors drive Monte Carlo simulations with historical wind events and real outage records, treating the resulting trajectories as samples for comparison. Resilience metrics stabilize at approximately 256 episodes. Outage peak, duration, and intensity shift systematically when fragility parameters, network topology, restoration assumptions, and repair strategies are varied. In a separate 1000-episode joint power-flooding run, episodes containing at least one flooded customer appear in 1.9 percent of cases overall, with both flood occurrence and intensity increasing as outage intensity grows.

What carries the argument

The graph-based resilience evaluation framework extended with historical weather conditioning and a coupled power-flooding module, which connects network topology, hazard simulation, fragility modeling, restoration rules, and downstream flood consequences through Monte Carlo sampling.

If this is right

Resilience metrics stabilize at approximately 256 episodes for wind-event simulations.
Outage peak, duration, and intensity change systematically with fragility parameters, network topology, restoration assumptions, and repair strategies.
Flooded-customer episodes occur in 1.9 percent of joint simulations, with both occurrence and intensity rising alongside outage intensity.
The framework supplies a practical basis for resilience assessment and comparative scenario analysis in limited public-data settings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Utilities facing similar data constraints could adapt the same historical-conditioning approach to evaluate their own past events without proprietary records.
The selective power-to-flood pathway points to possible value in hardening distribution lines in areas where outages could trigger sewage backups.
Incorporating utility-specific repair logs or more granular flood models could narrow the uncertainty ranges seen in the sensitivity tests.

Load-bearing premise

The fragility functions, restoration assumptions, and repair strategies used in the Monte Carlo simulations accurately capture real-world component failure and recovery behavior under wind and flooding hazards.

What would settle it

A direct side-by-side comparison of the simulated outage trajectories and flooded-customer counts against actual recorded outages and flooding incidents for the same historical wind events in the studied region.

Figures

Figures reproduced from arXiv: 2605.16811 by Amir Shahin Kamjou, Caisheng Wang, Carol Miller, John Norton, Xuesong Wang.

**Figure 2.** Figure 2: Patch-level historical HRRR gust field for a representative curated wind event [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Figure 3: Wind-event simulated distributions normalized by the observed event, shown as [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗

**Figure 4.** Figure 4: Wind-fragility family used in the sensitivity study, with higher fragility factors [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗

**Figure 5.** Figure 5: Five-event wind sensitivity sweep showing monotone increases in mean [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗

**Figure 6.** Figure 6: Representative flooded-area footprint from the coupled wind run generated by [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗

**Figure 7.** Figure 7: Flood impacts in the selected coupled wind run grouped by power-outage [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗

read the original abstract

Evaluating resilience in electric distribution systems under severe weather requires models that can connect network topology, hazard simulation, fragility modeling, restoration assumptions, repair strategy, and downstream consequences. This paper extends our prior graph-based resilience evaluation framework for power distribution systems in three ways: it adds analysis conditioned on historical events with real outage and weather data, introduces sensitivity studies for key modeling assumptions, and includes a coupled power-flooding extension for sewage-backup assessment. Historical wind events drive Monte Carlo simulations conditioned on real weather, and the observed outage trajectories are treated as realized historical samples for comparison. Wind-event resilience metrics stabilize at approximately 256 episodes, and outage peak, duration, and outage intensity change systematically with fragility parameters, network topology, restoration assumptions, and repair strategies. In a separate 1000-episode joint power-flooding simulation, episodes with at least one flooded customer occur in 1.9% of episodes overall, and both flood occurrence and flood intensity increase with outage intensity, showing a selective power-to-flood consequence pathway. Overall, the framework provides a practical basis for resilience assessment, comparative scenario analysis, and coupled power-flooding studies in a limited public-data setting, while also suggesting that more detailed utility data could further improve simulation realism.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This extends the authors' prior graph-based framework with historical weather runs, sensitivity sweeps, and a power-flooding link, but the reported stabilization at 256 episodes and 1.9% flood rate rest on uncalibrated fragility and restoration choices.

read the letter

The paper extends the authors' earlier graph-based resilience evaluation framework in three concrete ways. It conditions the Monte Carlo runs on actual historical wind events using real weather and outage data, adds sensitivity analysis on parameters like fragility functions and repair strategies, and introduces a coupled simulation for power outages leading to flooding and sewage backup. This setup allows for scenario comparisons and shows how outage metrics vary with different modeling choices. The reported stabilization of metrics at around 256 episodes and the finding that 1.9% of episodes involve flooded customers, with increasing flood intensity as outages worsen, come from these runs. The framework is presented as useful for resilience assessment in limited public-data environments. What the paper does well is provide a usable simulation approach that connects network topology, hazards, and consequences in a way that can support planning. The sensitivity studies highlight which assumptions matter most for the outcomes. The soft spots are around validation. The fragility parameters, restoration assumptions, and repair strategies drive the results, yet the abstract does not include quantitative comparisons showing how closely the simulated outage trajectories match the recorded ones for the specific historical events. Without error bars or direct matches on metrics like peak load or duration, it's possible that the stabilization point and the 1.9% figure are influenced by the particular choices made. The stress-test concern about parameter artifacts seems relevant based on what's described. This paper is for engineers and planners at utilities or in infrastructure resilience research who need tools to evaluate distribution systems under weather risks. Readers looking for applied methods rather than new fundamental theory would find value in the extensions and the coupled power-flooding analysis. It deserves a serious referee because the work is constructive and builds directly on prior results with new elements that can be tested. I'd recommend sending it to peer review, where reviewers can ask for more on the calibration to real data.

Referee Report

3 major / 2 minor

Summary. The manuscript extends a prior graph-based resilience framework for electric distribution systems by conditioning Monte Carlo simulations on historical wind events and real outage/weather data, adding sensitivity analysis over fragility parameters, restoration assumptions, and repair strategies, and introducing a coupled power-flooding module to evaluate sewage-backup risk. It reports that wind-event resilience metrics stabilize at approximately 256 episodes, that outage peak/duration/intensity vary systematically with modeling choices and topology, and that in 1000-episode joint simulations 1.9% of episodes contain at least one flooded customer with positive correlation between outage and flood intensity. The central claim is that the framework supplies a practical basis for resilience assessment and coupled hazard studies under limited public data.

Significance. If the fragility functions, restoration distributions, and repair heuristics are accepted as reasonable, the work supplies a reproducible, data-conditioned simulation platform that supports comparative scenario analysis and selective power-to-flood pathway identification. The addition of historical conditioning and explicit sensitivity studies strengthens the framework relative to purely synthetic studies; the flooding extension is a timely contribution for multi-hazard resilience. These elements would be of clear interest to the systems-and-control community working on infrastructure resilience.

major comments (3)

[Historical event conditioning and results] The abstract and methods sections state that 'observed outage trajectories are treated as realized historical samples for comparison,' yet no quantitative validation metrics (RMSE, duration error, peak-load error, or customer-outage curve match) are reported between the Monte Carlo trajectories and the actual recorded outages for the same historical wind events. This comparison is load-bearing for the claim of practical utility in a limited-public-data regime.
[Monte Carlo results and flooding extension] Stabilization of resilience metrics at ~256 episodes and the 1.9% flooded-customer rate are presented without error bars, convergence diagnostics, or sensitivity to random-seed variation. The Monte Carlo results therefore lack the statistical characterization needed to support the headline quantitative claims.
[Sensitivity analysis] The sensitivity studies vary fragility parameters and repair strategies but do not include a direct calibration step against real outage records for the same events; the weakest assumption (fragility curves and restoration times accurately capture real behavior) therefore remains untested within the manuscript.

minor comments (2)

[Methods] Parameter tables for the fragility functions, restoration distributions, and repair heuristics should be provided in full (including any values taken from the authors' prior work) so that the sensitivity results can be reproduced.
[Results figures] Figure captions and axis labels for the convergence and sensitivity plots should explicitly state the number of Monte Carlo episodes and the confidence intervals shown (or note their absence).

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments, which help improve the clarity and rigor of our manuscript. We address each of the major comments below.

read point-by-point responses

Referee: [Historical event conditioning and results] The abstract and methods sections state that 'observed outage trajectories are treated as realized historical samples for comparison,' yet no quantitative validation metrics (RMSE, duration error, peak-load error, or customer-outage curve match) are reported between the Monte Carlo trajectories and the actual recorded outages for the same historical wind events. This comparison is load-bearing for the claim of practical utility in a limited-public-data regime.

Authors: We acknowledge that quantitative validation metrics such as RMSE or curve matching are not explicitly reported in the current manuscript. The framework is intended for scenarios with limited public data, where full per-event detailed outage records for direct comparison may not be accessible. The historical events are used to condition the simulations with real weather data, and the observed trajectories provide a basis for qualitative comparison. To strengthen the presentation, we will add a discussion of this limitation and include any available aggregate statistics or qualitative assessments in the revised manuscript. revision: partial
Referee: [Monte Carlo results and flooding extension] Stabilization of resilience metrics at ~256 episodes and the 1.9% flooded-customer rate are presented without error bars, convergence diagnostics, or sensitivity to random-seed variation. The Monte Carlo results therefore lack the statistical characterization needed to support the headline quantitative claims.

Authors: We agree that providing error bars, convergence diagnostics, and analysis of random seed variation would enhance the statistical robustness of the Monte Carlo results. The stabilization at approximately 256 episodes was identified by monitoring when the metrics ceased to change significantly with additional episodes. In the revision, we will incorporate error bars on the reported metrics, include convergence plots, and report results from multiple independent runs to demonstrate consistency. revision: yes
Referee: [Sensitivity analysis] The sensitivity studies vary fragility parameters and repair strategies but do not include a direct calibration step against real outage records for the same events; the weakest assumption (fragility curves and restoration times accurately capture real behavior) therefore remains untested within the manuscript.

Authors: The sensitivity analysis is designed to illustrate how resilience metrics respond to variations in key modeling parameters, which is particularly useful in data-limited settings where exact calibration may not be feasible. We recognize that direct calibration against real outage records would be ideal but is constrained by the availability of detailed utility data. We will revise the manuscript to explicitly state this assumption and its implications, and suggest that future work could incorporate calibration when more granular data is accessible. revision: partial

Circularity Check

0 steps flagged

No circularity: Monte Carlo outputs independent of definitional inputs

full rationale

The paper's quantitative results, including metric stabilization at approximately 256 episodes, systematic changes in outage metrics with fragility and repair parameters, and the 1.9% rate of episodes with flooded customers, are generated via Monte Carlo simulations conditioned on external historical weather and outage data. These outputs are not algebraically or definitionally equivalent to the input fragility functions, restoration assumptions, or prior framework by construction; the simulations introduce stochastic variation and allow sensitivity studies that vary modeling choices independently. The extension of prior graph-based work is acknowledged but does not reduce the new historical-conditioning or coupled power-flooding analyses to self-citation chains, as the claims rest on fresh runs and comparisons to realized historical samples rather than fitted inputs renamed as predictions. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central claims rest on standard power-system fragility and restoration modeling assumptions plus several adjustable parameters whose values are explored via sensitivity analysis rather than derived from first principles.

free parameters (2)

fragility parameters
Control component failure probabilities under wind; varied systematically in sensitivity studies.
restoration assumptions and repair strategies
Define recovery timing and sequencing; shown to affect outage peak, duration, and intensity.

axioms (1)

domain assumption Historical wind events and observed outages serve as valid conditioning data for Monte Carlo simulation of future resilience metrics.
Invoked when real weather drives the simulations and when observed trajectories are treated as realized samples for comparison.

pith-pipeline@v0.9.0 · 5775 in / 1132 out tokens · 52494 ms · 2026-05-19T21:07:03.849886+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Historical wind events drive Monte Carlo simulations conditioned on real weather, and the observed outage trajectories are treated as realized historical samples for comparison. Wind-event resilience metrics stabilize at approximately 256 episodes...
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

fragility factor that scales the hazard-location thresholds used by the damage model... restoration assumptions and repair strategy

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages

[1]

Younesi, H

A. Younesi, H. Shayeghi, Z. Wang, P. Siano, A. Mehrizi-Sani, A. Safari, Trends in modern power systems resilience: State-of-the-art review, Re- newable and Sustainable Energy Reviews 162 (2022) 112397

work page 2022
[2]

Potts, H

J. Potts, H. R. Tiedmann, K. K. Stephens, K. M. Faust, S. Castellanos, Enhancing power system resilience to extreme weather events: A quali- tative assessment of winter storm uri, International Journal of Disaster Risk Reduction 103 (2024) 104309

work page 2024
[3]

Coletti, J

C.Brelsford, S.Tennille, A.Myers, S.Chinthavali, V.Tansakul, M.Den- man, M. Coletti, J. Grant, S. Lee, K. Allen, et al., A dataset of recorded electricity outages by united states county 2014–2022, Scientific Data 11 (1) (2024) 271

work page 2014
[4]

Oikonomou, K

K. Oikonomou, K. Mongird, J. S. Rice, J. S. Homer, Resilience of inter- dependent water and power systems: A literature review and conceptual modeling framework, Water 13 (20) (2021) 2846

work page 2021
[5]

D. K. Mishra, M. J. Ghadi, A. Azizivahed, L. Li, J. Zhang, A review on resilience studies in active distribution systems, Renewable and Sus- tainable Energy Reviews 135 (2021) 110201. 18

work page 2021
[6]

S. M. Lee, S. Chinthavali, N. Bhusal, N. Stenvig, A. Tabassum, T. Ku- ruganti, Quantifying the power system resilience of the us power grid through weather and power outage data mapping, IEEE Access 12 (2023) 5237–5255

work page 2023
[7]

Cresta, F

M. Cresta, F. M. Gatta, A. Geri, M. Maccioni, M. Paulucci, Resilience assessment in distribution grids: A complete simulation model, Energies 14 (14) (2021) 4303

work page 2021
[8]

Zhang, S

L. Zhang, S. Yu, B. Zhang, G. Li, Y. Cai, W. Tang, Outage management of hybrid ac/dc distribution systems: Co-optimize service restoration with repair crew and mobile energy storage system dispatch, Applied Energy 335 (2023) 120422

work page 2023
[9]

Prieto-Godino, C

L. Prieto-Godino, C. Peláez-Rodríguez, J. Pérez-Aracil, J. Pastor- Soriano, S. Salcedo-Sanz, Predicting weather-related power outages in large scale distribution grids with deep learning ensembles, International Journal of Electrical Power & Energy Systems 170 (2025) 110811

work page 2025
[10]

X. Wang, S. Yuan, S. K. Magableh, O. Dawaghreh, C. Wang, L. Y. Wang, Graph-based simulation framework for power resilience estima- tion and enhancement, in: 2025 IEEE Power & Energy Society General Meeting (PESGM), IEEE, 2025, pp. 1–5

work page 2025
[11]

Rouholamini, C

M. Rouholamini, C. Wang, S. Magableh, X. Wang, Resiliency of elec- tric power distribution networks: a review, Journal of Infrastruc- ture Preservation and Resilience 6 (1) (2025) 39.doi:10.1186/ s43065-025-00154-y. URLhttps://doi.org/10.1186/s43065-025-00154-y

work page doi:10.1186/s43065-025-00154-y 2025
[12]

L. Tang, Y. Han, A. S. Zalhaf, S. Zhou, P. Yang, C. Wang, T. Huang, Resilience enhancement of active distribution networks under extreme disaster scenarios: A comprehensive overview of fault location strate- gies, Renewable and Sustainable Energy Reviews 189 (2024) 113898. doi:https://doi.org/10.1016/j.rser.2023.113898. URLhttps://www.sciencedirect.com/sc...

work page doi:10.1016/j.rser.2023.113898 2024
[13]

X. Wang, N. Fatehi, C. Wang, M. H. Nazari, Deep learning-based weather-related power outage prediction with socio-economic and power 19 infrastructure data, in: 2024 IEEE Power & Energy Society General Meeting (PESGM), 2024, pp. 1–5.doi:10.1109/PESGM51994.2024. 10688596

work page doi:10.1109/pesgm51994.2024 2024
[14]

Raman, G

G. Raman, G. Raman, J. C.-H. Peng, Resilience of urban public elec- tric vehicle charging infrastructure to flooding, Nature Communications 13 (1) (2022) 3213

work page 2022
[15]

D. C. Dowell, C. R. Alexander, E. P. James, S. S. Weygandt, S. G. Ben- jamin, G. S. Manikin, B. T. Blake, J. M. Brown, J. B. Olson, M. Hu, et al., The high-resolution rapid refresh (hrrr): An hourly updating convection-allowing forecast model. part i: Motivation and system de- scription, Weather and Forecasting 37 (8) (2022) 1371–1395

work page 2022
[16]

E. P. James, C. R. Alexander, D. C. Dowell, S. S. Weygandt, S. G. Benjamin, G. S. Manikin, J. M. Brown, J. B. Olson, M. Hu, T. G. Smirnova, et al., The high-resolution rapid refresh (hrrr): an hourly updating convection-allowing forecast model. part ii: Forecast perfor- mance, Weather and Forecasting 37 (8) (2022) 1397–1417

work page 2022
[17]

T. P. Tran, R. J. Mahu, C. Miller, P. S. Larson, L. Thompson, Is there differential engagement in urban flooding prevention in detroit?, Journal of Urban Affairs (2026).doi:10.1080/07352166.2026.2642133. URLhttps://doi.org/10.1080/07352166.2026.2642133

work page doi:10.1080/07352166.2026.2642133 2026
[18]

P. S. Larson, C. Gronlund, L. Thompson, N. Sampson, R. Washington, J. Steis Thorsby, N. Lyon, C. Miller, Recurrent home flooding in detroit, mi 2012–2020: results of a household survey, International journal of environmental research and public health 18 (14) (2021) 7659. 20

work page 2012

[1] [1]

Younesi, H

A. Younesi, H. Shayeghi, Z. Wang, P. Siano, A. Mehrizi-Sani, A. Safari, Trends in modern power systems resilience: State-of-the-art review, Re- newable and Sustainable Energy Reviews 162 (2022) 112397

work page 2022

[2] [2]

Potts, H

J. Potts, H. R. Tiedmann, K. K. Stephens, K. M. Faust, S. Castellanos, Enhancing power system resilience to extreme weather events: A quali- tative assessment of winter storm uri, International Journal of Disaster Risk Reduction 103 (2024) 104309

work page 2024

[3] [3]

Coletti, J

C.Brelsford, S.Tennille, A.Myers, S.Chinthavali, V.Tansakul, M.Den- man, M. Coletti, J. Grant, S. Lee, K. Allen, et al., A dataset of recorded electricity outages by united states county 2014–2022, Scientific Data 11 (1) (2024) 271

work page 2014

[4] [4]

Oikonomou, K

K. Oikonomou, K. Mongird, J. S. Rice, J. S. Homer, Resilience of inter- dependent water and power systems: A literature review and conceptual modeling framework, Water 13 (20) (2021) 2846

work page 2021

[5] [5]

D. K. Mishra, M. J. Ghadi, A. Azizivahed, L. Li, J. Zhang, A review on resilience studies in active distribution systems, Renewable and Sus- tainable Energy Reviews 135 (2021) 110201. 18

work page 2021

[6] [6]

S. M. Lee, S. Chinthavali, N. Bhusal, N. Stenvig, A. Tabassum, T. Ku- ruganti, Quantifying the power system resilience of the us power grid through weather and power outage data mapping, IEEE Access 12 (2023) 5237–5255

work page 2023

[7] [7]

Cresta, F

M. Cresta, F. M. Gatta, A. Geri, M. Maccioni, M. Paulucci, Resilience assessment in distribution grids: A complete simulation model, Energies 14 (14) (2021) 4303

work page 2021

[8] [8]

Zhang, S

L. Zhang, S. Yu, B. Zhang, G. Li, Y. Cai, W. Tang, Outage management of hybrid ac/dc distribution systems: Co-optimize service restoration with repair crew and mobile energy storage system dispatch, Applied Energy 335 (2023) 120422

work page 2023

[9] [9]

Prieto-Godino, C

L. Prieto-Godino, C. Peláez-Rodríguez, J. Pérez-Aracil, J. Pastor- Soriano, S. Salcedo-Sanz, Predicting weather-related power outages in large scale distribution grids with deep learning ensembles, International Journal of Electrical Power & Energy Systems 170 (2025) 110811

work page 2025

[10] [10]

X. Wang, S. Yuan, S. K. Magableh, O. Dawaghreh, C. Wang, L. Y. Wang, Graph-based simulation framework for power resilience estima- tion and enhancement, in: 2025 IEEE Power & Energy Society General Meeting (PESGM), IEEE, 2025, pp. 1–5

work page 2025

[11] [11]

Rouholamini, C

M. Rouholamini, C. Wang, S. Magableh, X. Wang, Resiliency of elec- tric power distribution networks: a review, Journal of Infrastruc- ture Preservation and Resilience 6 (1) (2025) 39.doi:10.1186/ s43065-025-00154-y. URLhttps://doi.org/10.1186/s43065-025-00154-y

work page doi:10.1186/s43065-025-00154-y 2025

[12] [12]

L. Tang, Y. Han, A. S. Zalhaf, S. Zhou, P. Yang, C. Wang, T. Huang, Resilience enhancement of active distribution networks under extreme disaster scenarios: A comprehensive overview of fault location strate- gies, Renewable and Sustainable Energy Reviews 189 (2024) 113898. doi:https://doi.org/10.1016/j.rser.2023.113898. URLhttps://www.sciencedirect.com/sc...

work page doi:10.1016/j.rser.2023.113898 2024

[13] [13]

X. Wang, N. Fatehi, C. Wang, M. H. Nazari, Deep learning-based weather-related power outage prediction with socio-economic and power 19 infrastructure data, in: 2024 IEEE Power & Energy Society General Meeting (PESGM), 2024, pp. 1–5.doi:10.1109/PESGM51994.2024. 10688596

work page doi:10.1109/pesgm51994.2024 2024

[14] [14]

Raman, G

G. Raman, G. Raman, J. C.-H. Peng, Resilience of urban public elec- tric vehicle charging infrastructure to flooding, Nature Communications 13 (1) (2022) 3213

work page 2022

[15] [15]

D. C. Dowell, C. R. Alexander, E. P. James, S. S. Weygandt, S. G. Ben- jamin, G. S. Manikin, B. T. Blake, J. M. Brown, J. B. Olson, M. Hu, et al., The high-resolution rapid refresh (hrrr): An hourly updating convection-allowing forecast model. part i: Motivation and system de- scription, Weather and Forecasting 37 (8) (2022) 1371–1395

work page 2022

[16] [16]

E. P. James, C. R. Alexander, D. C. Dowell, S. S. Weygandt, S. G. Benjamin, G. S. Manikin, J. M. Brown, J. B. Olson, M. Hu, T. G. Smirnova, et al., The high-resolution rapid refresh (hrrr): an hourly updating convection-allowing forecast model. part ii: Forecast perfor- mance, Weather and Forecasting 37 (8) (2022) 1397–1417

work page 2022

[17] [17]

T. P. Tran, R. J. Mahu, C. Miller, P. S. Larson, L. Thompson, Is there differential engagement in urban flooding prevention in detroit?, Journal of Urban Affairs (2026).doi:10.1080/07352166.2026.2642133. URLhttps://doi.org/10.1080/07352166.2026.2642133

work page doi:10.1080/07352166.2026.2642133 2026

[18] [18]

P. S. Larson, C. Gronlund, L. Thompson, N. Sampson, R. Washington, J. Steis Thorsby, N. Lyon, C. Miller, Recurrent home flooding in detroit, mi 2012–2020: results of a household survey, International journal of environmental research and public health 18 (14) (2021) 7659. 20

work page 2012