Research output

Publications

Peer-reviewed articles and working papers.

Peer-reviewed journal articles

  1. 01

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

    Zhang, Z. (2025)

    Journal of Hydrology, 2025, 134447

    Establishes a hydrologically informed fuzzy entropy formulation to quantify catchment-scale flowrate modelling uncertainty from spatial inputs.

    DOI
  2. 02

    Fuzzy-based input method for uncertainty quantification in a deterministic model: Comparison with ChatGPT for peak flow prediction

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

    Journal of Hydrology X, 28–29, 100208

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

    DOI
  3. 03

    Low Impact Development Technologies for Mitigating Climate Change: Review and Direction

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

    National Science Open, 3(1), 20230025

    A comprehensive review of LID technologies, synthesizing evidence for climate mitigation and future research directions.

    DOI
  4. 04

    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.

    DOI

Book chapters (Peer-reviewed conference proceedings)

  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)

    In Y. Zeng (ed.), Environmental Science and Technology: Sustainable Development III, Environmental Science and Engineering, pp. 17–29. Springer, Cham

    Proposes a soil data scale-up workflow using fuzzy C-means to preserve hydrologic information through model resolutions. Oral presentation delivered at ICEST 2024 conference.

    DOI
  2. 02

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

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

    In Z. Yang (ed.), Environmental Science and Technology: Sustainable Development, ICEST 2022. Environmental Science and Engineering, pp. 89–100. Springer, Cham

    Field-informed case study quantifying how LID placement reduces flooding and peak flows in a Canadian municipality. Oral presentation delivered at ICEST 2022 conference.

    DOI

Conference papers

  1. 01

    Quantifying Spatial Data Uncertainty with Fuzzy Entropy

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

    IEEE Pacific Rim Conference on Communications, Computers, and Signal Processing (PACRIM 2024), Victoria, BC, Canada, August 21–23, 6 pp

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

    IEEE Xplore
  2. 02

    Potential for Nature-based Solutions to Mitigate Impacts of Climate Change

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

    3rd International Conference on New Horizons in Green Civil Engineering (NHICE-03), Victoria, BC, Canada, April 25–28

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

Conference presentations (Reviewed by abstract/Invited)

  1. 01

    Quantifying Uncertainty in Urban Hydrological Models and Exploring AI-based Prediction: A Case Study in the Saanich Area

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

    PEOPLE 2025 International Conference - Challenges and Opportunities in Environmental Sustainability under Climate Change, jointly with the 2025 Atlantic Symposium of the Canadian Association on Water Quality (CAWQ), St. John's, NL, Canada, July 21–25

  2. 02

    Shannon Entropy in Urban Drainage Systems under Changing Climate: A Case Study of Small-Scale Urban Stormwater Management System

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

    4th International Conference on New Horizons in Green Civil Engineering (NHICE-04), Victoria, BC, Canada, August 26–28

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

  3. 03

    Enhancing Low-Impact Development Modelling for Future Urban Stormwater Management: A Case Study in Vancouver Island

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

    14th International Conference on Environmental Science and Technology (ICEST 2023), Shandong University, Qingdao, China, November 23–25

  4. 04

    Scale-up Challenges and Information Loss in Low Impact Development Modeling for Urban Stormwater Management in Changing Climate

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

    The 3rd International Symposium on Sustainable Urban Drainage (SUDS 2023), Hangzhou, Zhejiang, China, October 20–22

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

  5. 05

    Determining LID Performance for Mitigating Flooding under a Changing Climate

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

    Canadian Water Resources Association (CWRA) National Conference 2023 — "Rising with the Tide: Working Together to Address Water Resource Challenges," Halifax, NS, Canada, June 18–21

  6. 06

    Potential in Nature-based Solutions for Mitigating Impacts of Climate Change [Conference poster]

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

    Canadian Water Resources Association (CWRA) National Conference 2022 Valuing Shared Waters, Canmore, AB, Canada, June 5–8

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

Papers in submission

  1. 01

    Quantifying Uncertainty from Spatial Data Inputs Using Hydrological Processes and Fuzzy Entropy

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

    Manuscript in revision with Journal of Hydrology (major revision completed)

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

    SSRN Preprint
  2. 02

    Information Loss over Hillslope Flowpaths and Scaling with Runoff Models

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

    Manuscript submitted to Hydrology and Earth System Science (EGU), October 30, 2025

Technology transfer

  1. 01

    Fuzzy-HydroGPT-RTC v2.0

    Zhang, Z. (2025)

    GitHub Release

    PyQt6 GUI with ≈10 s real-time weather/flow scraping, GPT-based prediction, fuzzy UQ, and model evaluation.

    GitHub Release
  2. 02

    HydroGPT v1.1

    Zhang, Z. (2025)

    GitHub Release

    End-to-end workflow for hydrology: data scraping, cleaning, GPT conversion, fuzzy UQ, metrics, and export.

    GitHub Release