Research output

Publications

Peer-reviewed articles and working papers.

Journal articles

  1. 01

    Low impact development technologies for mitigating climate change: Summary and prospects

    Zhang, Z., & Valeo, C. (2024)

    National Science Open, 3(1), 20230025

    A perspective on LID technologies, synthesizing evidence for climate mitigation and future research directions.

    PDF DOI
  2. 02

    Verification of PCSWMM’s LID processes and their scalability over time and space

    Zhang, Z., & Valeo, C. (2022)

    Frontiers in Water, 4, 1058883

    Validates LID modules in a widely used hydrologic model and tests their transferability across scales and periods.

    PDF DOI

Preprints / Working papers

  1. 01

    Quantifying uncertainty from spatial data inputs using hydrological processes and fuzzy entropy

    Zhang, Z., & Valeo, C. (2025)

    SSRN

    Process‑aware uncertainty quantification from spatial inputs using fuzzy entropy for hydrologic decision support.

    PDF DOI

Under review / In revision

  1. 01

    Quantifying flowrate uncertainty using spatially defined fuzzy entropy based on hydrological processes in a catchment

    Zhang, Z., & Valeo, C. (in revision)

    Journal of Hydrology

    Introduces a process‑aware fuzzy entropy framework to quantify flow uncertainty driven by spatial input variability.

  2. 02

    Fuzzy-based input methods for uncertainty quantification in a deterministic model comparison with ChatGPT for peak flow prediction

    Zhang, Z., & Valeo, C. (in revision)

    Journal of Hydrology X

    Compares fuzzy membership input strategies with LLM‑assisted baselines for predicting peak flows in urban catchments.

Conference papers

  1. 01

    Quantifying Scaling-Up Uncertainty in Soil Data Using Fuzzy C-Means Clustering: A Framework for Application to Hydrological Modeling

    Zhang, Z., & Valeo, C. (2025)

    Environmental Science and Technology: Sustainable Development III (pp. 17–29). Springer Nature Switzerland

    Proposes a soil data scale‑up workflow using fuzzy C‑means to preserve hydrologic information through model resolutions.

    PDF DOI
  2. 02

    Quantifying Spatial Data Uncertainty with Fuzzy Entropy

    Zhang, Z., & Valeo, C. (2024)

    Conference Proceedings (IEEE Pacific Rim Conference on Communications, Computers, and Signal Processing), 1–6

    Uses fuzzy entropy to summarize spatial uncertainty in environmental datasets for engineering analysis and decisions.

    PDF DOI
  3. 03

    Quantifying scaling-up uncertainty in soil data using fuzzy C-means clustering: A framework for application to hydrological modeling

    Zhang, Z., & Valeo, C. (2024)

    In Proceedings of the International Conference on Environmental Science and Technology (ICEST) (pp. 17–29). Xiamen, China.

    ICEST paper detailing a fuzzy C‑means approach for soil data aggregation that preserves hydrologic relevance.

  4. 04

    Assessing Optimal LID Areas for Flood Mitigation: A Case Study on Vancouver Island, Canada

    Zhang, Z., & Valeo, C. (2023)

    Environmental Science and Technology: Sustainable Development (pp. 89–100). Springer International Publishing AG

    Field‑informed case study quantifying how LID placement reduces flooding and peak flows in a Canadian municipality.

    PDF DOI
  5. 05

    Potential for nature-based solutions to mitigate impacts of climate change

    Zhang, Z., & Valeo, C. (2022, April 25–28)

    In Proceedings of the 3rd International Conference on New Horizons in Green Civil Engineering (NHICE-03). Victoria, BC, Canada.

    Outlines opportunities and constraints of nature‑based solutions for climate‑change resilience in urban drainage.

Conference posters

  1. 01

    Potential in nature-based solutions for mitigating impacts of climate change [Conference poster]

    Zhang, Z., & Valeo, C. (2022, June 5–8)

    CWRA 2022 National Conference: Valuing Shared Waters, Canmore, AB, Canada.

    Poster synthesizing early findings on linking fuzzy metrics with climate‑ready LID design in cities.

Conference presentations (talks/abstracts)

  1. 01

    Shannon entropy in urban drainage systems under changing climate: A case study of small-scale urban stormwater management system

    Zhang, Z., & Valeo, C. (2024)

    In Proceedings of the 4th International Conference on New Horizons in Green Civil Engineering (NHICE 04).

    Explores information loss and scale effects in LID modelling and paths to retain decision‑useful signals.

  2. 02

    Scale-up challenges and information loss in low impact development modeling for urban stormwater management in changing climate

    Zhang, Z., & Valeo, C. (2023)

    Presentation at the SUDS Conference, Jiaxing, China.

    Talk on preserving hydrologic fidelity when aggregating inputs, with practical guidance for planners.

  3. 03

    Scale-up challenges and information loss in low impact development modeling for urban stormwater management in changing climate

    Zhang, Z., & Valeo, C. (2023)

    Presentation at the International Conference on Environmental Science and Technology (ICEST), Qingdao, China.

    ICEST talk emphasizing uncertainty pathways and mitigation strategies for climate‑aware LID models.

  4. 04

    Determining LID performance for mitigating flooding under a changing climate [Conference abstract]

    Zhang, Z., & Valeo, C. (2023, June 18–21)

    CWRA 2023 National Conference: Conferencing with the Tide: Working Together to Address Water Resource Challenges, Halifax, Canada.

    Abstract presenting early results on flood mitigation effectiveness of LID under climate‑change scenarios.