Skip to main content

Advertisement

SpringerLink
Log in

Development of Multi-Hazard Risk Assessment Model for Agricultural Water Supply and Distribution Systems Using Bayesian Network

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

To identify and assess the impact of various hazards threaten agriculture water supply systems, the development of a risk analysis framework is inevitable in promoting sustainable agricultural development. This study aims to develop a novel multi-hazard risk assessment model by applying a Hybrid Bayesian Network for agricultural water supply and distribution systems. This model consists of discrete and continuous nodes and their probable interactions. The structure of this model is designed to assess the risk associated with the agricultural water system's for supply and distribution sections separately considering various factors such as river discharge, the inflow of water distribution system and its fluctuation, and the demand of water. The developed model is applied to the Roodasht Irrigation district located in the center of Iran to demonestrate its capability. This Irrigation district is under the threat of drought, improper performance of the ditch-riders, and operational losses. The results showed that the model in both training and test datasets has proper accuracy and performance with the root mean square error of 0.069 and 0.076, coefficient of determinations equal to 0.717 and 0.690, and the overall index of model performance equal to 0.787 and 0.671, respectively. The results of this research and the proposed model will help stakeholders and decision-makers to be aware of the probable causes and the extent of system failure and its components due to the threatening hazards. Also, it will assist in planning the allocation of irrigation water based on predictable risk associated with various hazards.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Abbassi R, Bhandari J, Khan F, Garaniya V, Chai S (2016) Developing a quantitative risk-based methodology for maintenance scheduling using Bayesian network. Chem Eng Trans 48:235–240

    Google Scholar 

  • Abedzadeh S, Roozbahani A, Heidar A (2020) Risk Assessment of Water Resources Development Plans Using Fuzzy Fault Tree Analysis. Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 1–21

  • Anbari MJ, Tabesh M, Roozbahani A (2017) Risk assessment model to prioritize sewer pipes inspection in wastewater collection networks. J Environ Manage 190:91–101

    Article  Google Scholar 

  • Arzaghi E, Abaei MM, Abbassi R, Garaniya V, Chin C, Khan F (2017) Risk-based maintenance planning of subsea pipelines through fatigue crack growth monitoring. Eng Fail Anal 79:928–939

    Article  Google Scholar 

  • Babaei M, Roozbahani A, Shahdany SMH (2018) Risk Assessment of Agricultural Water Conveyance and Delivery Systems by Fuzzy Fault Tree Analysis Method. Water Resour Manage 32(12):4079–4101

    Article  Google Scholar 

  • Bergmeir C, Hyndman RJ, Koo B (2018) A note on the validity of cross-validation for evaluating autoregressive time series prediction. Comput Stat Data Anal 120:70–83

    Article  Google Scholar 

  • Carriger JF, Parker RA (2021) “Conceptual Bayesian networks for contaminated site ecological risk assessment and remediation support.” J Environ Manag 278, 111478

  • FAO (2008) Sensitivity analysis of irrigation structures, from http://www.fao.org/3/a-bc045e.pdf

  • FAO (2017) Water for sustainable food and agriculture a report produced for the G20 presidency of germany.

  • Hashimoto T, Stedinger JR, Loucks DP (1982) Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation. Water Resour Res 18(1):14–20

    Article  Google Scholar 

  • Imran U, Khan M, Jamal R, Sahulka SQ, Goel R, Mahar R, Weidhaas J (2020) “Probabilistic risk assessment of water distribution system in Hyderabad, Pakistan reveals unacceptable health hazards and areas for rehabilitation.” Ecotoxicol Environ Safety 191 110233

  • Kaghazchi A, Shahdany SMH, Roozbahani A (2021) “Simulation and evaluation of agricultural water distribution and delivery systems with a Hybrid Bayesian network model.” Agric Water Manag 245, 106578

  • Kamrani K, Roozbahani A, Shahdany SMH (2020) “Using Bayesian networks to evaluate how agricultural water distribution systems handle the water-food-energy nexus.” Agric Water Manag 239 106265

  • Lee M, McBean EA, Ghazali M, Schuster CJ, Huang JJ (2009) Fuzzy-logic modeling of risk assessment for a small drinking-water supply system. J Water Resour Plan Manag 135(6):547–552

    Article  Google Scholar 

  • Lerne U (2002) Hybrid Bayesian networks for reasoning about complex systems (Doctoral dissertation, stanford university).

  • Liu B, Huang JJ, McBean E, Li Y.(2020) “Risk assessment of hybrid rain harvesting system and other small drinking water supply systems by game theory and fuzzy logic modeling.” Sci Total Environ 708 134436

  • Liu B, Siu YL, Mitchell G (2017) A quantitative model for estimating risk from multiple interacting natural hazards: an application to northeast Zhejiang, China. Stoch Env Res Risk Assess 31(6):1319–1340

    Article  Google Scholar 

  • Molden DJ, Gates TK (1990) Performance measures for evaluation of irrigation-water-delivery systems. J Irrig Drain Eng 116(6):804–823

    Article  Google Scholar 

  • Nalbantis I, Tsakiris G (2009) Assessment of hydrological drought revisited. Water Resour Manage 23(5):881–897

    Article  Google Scholar 

  • Nielsen TD, Jensen FV (2009) “Bayesian networks and decision graphs.” Springer Sci Bus Media.

  • Nieto-Morote A, Ruz-Vila F (2011) A fuzzy approach to construction project risk assessment. Int J Project Manage 29(2):220–231

    Article  Google Scholar 

  • Noorbeh P, Roozbahani A, Moghaddam HK (2020) “Annual and Monthly Dam Inflow Prediction Using Bayesian Networks.” Water Resource Manag 1–19

  • Orojloo M, Shahdany SMH, Roozbahani A (2018) Developing an integrated risk management framework for agricultural water conveyance and distribution systems within fuzzy decision making approaches. Sci Total Environ 627:1363–1376

    Article  Google Scholar 

  • Ronco P, Zennaro F, Torresan S, Critto A, Santini M, Trabucco A, Marcomini A (2017) A risk assessment framework for irrigated agriculture under climate change. Adv Water Resour 110:562–578

    Article  Google Scholar 

  • Roozbahani A, Ebrahimi E, Banihabib ME (2018) A framework for ground water management based on bayesian network and MCDM techniques. Water Resour Manage 32(15):4985–5005

    Article  Google Scholar 

  • Roozbahani A, Zahraie B, Tabesh M (2013) Integrated risk assessment of urban water supply systems from source to tap. Stoch Env Res Risk Assess 27(4):923–944

    Article  Google Scholar 

  • Sarzaeim P, Bozorg-Haddad O, Bozorgi A, Loáiciga HA (2017) Runoff projection under climate change conditions with data-mining methods. J Irrig Drain Eng 143(8):04017026

    Article  Google Scholar 

  • Shahdany SH, Majd EA, Firoozfar A, Maestre JM (2016) Improving operation of a main irrigation canal suffering from inflow fluctuation within a centralized model predictive control system: case study of Roodasht Canal, Iran. J Irrig Drain Eng 142(11):05016007

    Article  Google Scholar 

  • Sherafatpour Z, Roozbahani A, Hasani Y (2019) Agricultural water allocation by integration of hydro-economic modeling with Bayesian networks and random forest approaches. Water Resour Manage 33(7):2277–2299

    Article  Google Scholar 

  • Torres JM, Brumbelow K, Guikema SD (2009) Risk classification and uncertainty propagation for virtual water distribution systems. Reliab Eng Syst Saf 94(8):1259–1273

    Article  Google Scholar 

  • Tsakiris G, Vangelis HJEW (2005) Establishing a drought index incorporating evapotranspiration. European Water 9(10):3–11

    Google Scholar 

  • Wong TT (2015) Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recogn 48(9):2839–2846

    Article  Google Scholar 

  • Xiong Z, Cui Y, Liu Z, Zhao Y, Hu M, Hu J (2020 “Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation.” Comput Mater Sci 171 109203

Download references

Funding

Financial support fron Iran Water Resources Management Company under grant number 99/S/007.

Author information

Authors and Affiliations

  1. Department of Water Engineering, College of Aburaihan, University of Tehran, Tehran, Iran

    Atiyeh Bozorgi, Abbas Roozbahani & Seied Mehdy Hashemy Shahdany

  2. School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia

    Rouzbeh Abbassi

Authors
  1. Atiyeh Bozorgi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abbas Roozbahani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seied Mehdy Hashemy Shahdany
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rouzbeh Abbassi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Atiyeh Bozorgi: Investigation, Methodology, Software, Formal analysis, Writing -original draft, Abbas Roozbahani: Conceptualization, Supervision, Validation, Writing—Review & Editing, Seied Mehdy Hashemy Shahdany: Supervision, Validation, Writing—Review & Editing, Rouzbeh Abbassi: Validation, Writing—Review & Editing.

Corresponding authors

Correspondence to Abbas Roozbahani or Seied Mehdy Hashemy Shahdany.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bozorgi, A., Roozbahani, A., Hashemy Shahdany, S.M. et al. Development of Multi-Hazard Risk Assessment Model for Agricultural Water Supply and Distribution Systems Using Bayesian Network. Water Resour Manage 35, 3139–3159 (2021). https://doi.org/10.1007/s11269-021-02865-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-021-02865-9

Keywords

Search

Navigation