ARXIV:2603.06278 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport

arXiv

A decision-support framework using reinforcement learning for long-term flood adaptation in urban transport systems.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain A decision-support framework using reinforcement learning for long-term flood adaptation in urban transport systems.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A decision-support framework using reinforcement learning for long-term flood adaptation in urban transport systems. Designing effective adaptation strategies is challenging due to the long-term, sequential nature of infrastructure investments, deep climate uncertainty, and the…

METHOD

Full abstract

Climate change is expected to intensify rainfall and, consequently, pluvial flooding, leading to increased disruptions in urban transportation systems over the coming decades. Designing effective adaptation strategies is challenging due to the long-term, sequential nature of infrastructure investments, deep climate uncertainty, and the complex interactions between flooding, infrastructure, and mobility impacts. In this work, we propose a novel decision-support framework using reinforcement learning (RL) for long-term flood adaptation planning. Formulated as an integrated assessment model (IAM), the framework combines rainfall projection and flood modeling, transport simulation, and quantification of direct and indirect impacts on infrastructure and mobility. Our RL-based approach learns adaptive strategies that balance investment and maintenance costs against avoided impacts. We evaluate the framework through a case study of Copenhagen's inner city over the 2024-2100 period, testing multiple adaptation options, and different belief and realized climate scenarios. Results show that the framework outperforms traditional optimization approaches by discovering coordinated spatial and temporal adaptation pathways and learning trade-offs between impact reduction and adaptation investment, yielding more resilient strategies. Overall, our results showcase the potential of reinforcement learning as a flexible decision-support tool for adaptive infrastructure planning under climate uncertainty.

RESULT

ScienceToStartup currently rates this 3.0/10 on the public viability pass. In this work, we propose a novel decision-support framework using reinforcement learning (RL) for long-term flood adaptation planning.

WHY NOW

Agents moved forward this cycle; last verified April 2026. Public score 3.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score3.0

PainA decision-support framework using reinforcement learning for long-term flood adaptation in urban transport systems.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A decision-support framework using reinforcement learning for long-term flood adaptation in urban transport systems.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.06278 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport

arXiv

A decision-support framework using reinforcement learning for long-term flood adaptation in urban transport systems.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain A decision-support framework using reinforcement learning for long-term flood adaptation in urban transport systems.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Agents moved forward this cycle; last verified April 2026. Public score 3.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score3.0

PainA decision-support framework using reinforcement learning for long-term flood adaptation in urban transport systems.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A decision-support framework using reinforcement learning for long-term flood adaptation in urban transport systems.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.06278

Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport

A decision-support framework using reinforcement learning for long-term flood adaptation in urban transport systems.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback49 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

3.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Agents moved forward this cycle; last verified April 2026. Public score 3.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
A decision-support framework using reinforcement learning for long-term flood adaptation in urban transport systems. Designing effective adaptation strategies is challenging due to the long-term, sequential nature of infrastructure investments, deep climate uncertainty, and the complex interactions between flooding, infrastructure, and mobility impacts.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Climate change is expected to intensify rainfall and, consequently, pluvial flooding, leading to increased disruptions in urban transportation systems over the coming decades. Designing effective adaptation strategies is challenging due to the long-term, sequential nature of infrastructure investments, deep climate uncertainty, and the complex interactions between flooding, infrastructure, and mobility impacts.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. In this work, we propose a novel decision-support framework using reinforcement learning (RL) for long-term flood adaptation planning.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Agents moved forward this cycle; last verified April 2026. Public score 3.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Agents

Competitors

not indexed

Competitive landscape

A decision-support framework using reinforcement learning for long-term flood adaptation in urban transport systems.

Segment

Agents

Adoption evidence

No public code link in the paper record yet

Commercial read

3.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.06278 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(49)

Modeling and Analyzing Urban Networks and Amenities with OSMnx

2025Geoff Boeing

Reinforcement learning–based adaptive strategies for climate change adaptation: An application for coastal flood risk management

2025Kairui Feng, Ning Lin et al.

Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization

2024Zhitong Xu, Shandian Zhe

Changes in intra-city transport accessibility accompanying the occurrence of an urban flood

2023M. Borowska-Stefańska, Adam Bartnik et al.

Regional variation of climatological cloudburst frequency estimated from historical observations of daily precipitation sums

2023T. Schmith, P. Thejll et al.

Bayesian Optimisation Against Climate Change: Applications and Benchmarks

2023Sigrid Passano Hellan, Christopher G. Lucas et al.

Flooding mitigation through safe & trustworthy reinforcement learning

2023Wenchong Tian, K. Xin et al.

Interregional economic impacts of an extreme storm flood scenario considering transportation interruption: A case study of Shanghai, China

2023Wei Ding, Jidong Wu

An overview of flood-induced transport disruptions on urban streets and roads in Chinese megacities: Lessons and future agendas.

2022Xiaohui Lu, Faith Ka Shun Chan et al.

Deep learning, reinforcement learning, and world models

2022Yu Matsuo, Yann LeCun et al.

Assessing Flood Indirect Impacts on Road Transport Networks Applying Mesoscopic Traffic Modelling: The Case Study of Santarém, Portugal

2022Fereshteh Jafari Shahdani, Mónica Santamaria-Ariza et al.

Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems

2022T. Gilbert, Sarah Dean et al.

Evaluating crisis perturbations on urban mobility using adaptive reinforcement learning

2021Chao Fan, Xiangqi Jiang et al.

Flood impacts on urban transit and accessibility—A case study of Kinshasa

2021Yiyi He, S. Thies et al.

Flood risk assessment of the European road network

2021Kees C. H. van Ginkel, F. Dottori et al.

Appendix to: BOTORCH: A Framework for Efficient Monte-Carlo Bayesian Optimization

2021

Stable-Baselines3: Reliable Reinforcement Learning Implementations

2021A. Raffin, Ashley Hill et al.

Proposal of a flash flood impact severity scale for the classification and mapping of flash flood impacts

2020M. Diakakis, G. Deligiannakis et al.

Climate change research on transportation systems: Climate risks, adaptation and planning

2020Tianni Wang, Zhuohua Qu et al.

Flood mitigation in coastal urban catchments using real-time stormwater infrastructure control and reinforcement learning

2020Benjamin D. Bowes, A. Tavakoli et al.

Showing 20 of 49 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkTransferable Reinforcement Learning via Probabilistic Latent Embeddings and Dynamic Policy Adaptation for Sim-to-Real Deployment

3.0

Extension

none indexed

Commercially relevant

Higher ViabilityLearning long term climate-resilient transport adaptation pathways under direct and indirect flood impacts using reinforcement learning

6.0

Higher ViabilityEvaluating the Robustness of Reinforcement Learning based Adaptive Traffic Signal Control

6.0

Higher ViabilityBi-level RL-Heuristic Optimization for Real-world Winter Road Maintenance

7.0

Higher ViabilityIntelli-Planner: Towards Customized Urban Planning via Large Language Model Empowered Reinforcement Learning

7.0

Higher ViabilityIRL-DAL: Safe and Adaptive Trajectory Planning for Autonomous Driving via Energy-Guided Diffusion Models

7.0

Higher ViabilityAdapting the Behavior of Reinforcement Learning Agents to Changing Action Spaces and Reward Functions

6.0

Higher ViabilityA Generative AI-Driven Reliability Layer for Action-Oriented Disaster Resilience

6.0

Higher ViabilityInterpretable Deep Reinforcement Learning for Element-level Bridge Life-cycle Optimization

5.0

Conflicting

Competing ApproachTowards Autonomous Railway Operations: A Semi-Hierarchical Deep Reinforcement Learning Approach to the Vehicle Rescheduling Problem

3.0

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Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.06278
Route: /paper/artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport
Active tab: read
Artifact: artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport/paper-pack
REST build passport API/api/v1/paper/artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
  "contract_version": "paper-r2",
  "paper_id": "caf48fce-8499-4137-82e3-1368eb9fc572",
  "arxiv_id": "2603.06278",
  "canonical_route": "/paper/artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport",
  "endpoints": {
    "paper_pack": "/api/v1/paper/artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport/paper-pack",
    "build_passport": "/api/v1/paper/artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport

stale

Proof freshness: stale
Proof status: unverified
Display score: 3/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport

Canonical ID artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport | Route /paper/artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.06278"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport",
  "normalized_query": "2603.06278",
  "route": "/paper/artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport",
  "paper_ref": "artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Not build-ready: Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport

/buildability/artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport

Ignoreblocked

Subject: Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport

Paper ref

artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport

arXiv id

2603.06278

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

f2a02585abecae113535d31b757e4b6c63d4673c2504888be7e06e3e220d7c78

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

{
  "@context": "https://schema.org",
  "@graph": [
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      "headline": "Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport",
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      "url": "https://sciencetostartup.com/paper/artificial-intelligence-for-climate-adaptation-reinforcement-learning-for-climate-change-resilient-transport",
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Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport

Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport

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References(49)

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Brief

Experiment plan

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Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline