Application of deep convolutional networks for improved risk assessments of post-wildfire drinking water contamination

Schmidt, A.; Ellsworth, Lisa M.; Tilt, Jenna H.; Gough, Mike

doi:10.1016/j.mlwa.2023.100454

Cited by 2 publications

(1 citation statement)

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“…Significant progress has been made by global scholars in the field of water environment risk assessment. However, the majority of these works primarily focus on aspects such as indicator selection and quantitative demonstration, with fewer applications and empirical verifications in specific cases [1][2][3][4][5][6][7][8][9][10]. The concept of risk assessment as a management and decision-making tool can be traced back to the 20th century.…”

Section: Introductionmentioning

confidence: 99%

Improving Analytic Hierarchy Process inside the Analytic Group Decision-Making Approach Method with Two-Dimensional Cloud Model for Water Resource Pollution Risk Warning Evaluation: A Case Study in Shandong Province, China

Zhou,

Li,

Gao

et al. 2024

Water

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This study proposes a water resource pollution risk warning evaluation method. Firstly, an evaluation system is constructed, consisting of 15 secondary indicators in four aspects: water quality, ecology, utilization protection, and water disasters. Then, an improved AGA-AHP method and coefficient of variation method are used to determine the weights of each indicator. Cloud models are employed to describe the characteristics of standard clouds and evaluation clouds, establishing a two-dimensional cloud model with risk probability and hazard level as variables. Taking a certain region in Shandong Province, China, as an example, the quantitative analysis results indicate that the water pollution risk level in the area is classified as Level IV, with particular attention needed for water quality and management indicators. Simultaneously, a series of measures such as source control, monitoring and early warning, emergency response, and public participation are proposed to further reduce the risk. The research findings demonstrate the following: (1) The establishment of a comprehensive indicator system for multidimensional assessment; (2) The combination of the AGA-AHP method and cloud model for quantitative analysis; (3) The practicality of the method validated through the case study; (4) Providing a basis for subsequent decision-making. This study provides new insights for water environmental risk management, but a further optimization of the model to enhance predictive capability is required when applied in practical scenarios. Nevertheless, the preliminary validation of this method’s application prospects in water resource risk monitoring has been achieved.

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