The overall approach of the proposal is based on advanced risk assessment of multi-hazards and multi-risk. It will done based on 4 operational workpackages.
The supportive tools and methods are:
- Mining hazard assessments
- Developping specific tool (DSS and GIS) for integrating multi-hazard in mining risk management and decisions.
- Applying and validating the tools on abandoned coal mines in Europe.
In order to find the interactions between hazards, PoMHaZ will answer the following questions:
- Interactions conditions: are there specific conditions to be fulfilled for an interaction to take place? What are these conditions? How to evaluate their likelihood? Or is the interaction systematic?
- Intensity: To what extent a specific source phenomenon will modify target phenomenon intensity? What are the parameters that explain target phenomenon intensity?
- Probability of occurrence: what are the parameters that will alter target phenomenon probability of occurrence?
- Temporality: will source and target phenomena occur simultaneously, or is there a buffer time between their occurrence? What are the parameters influencing buffer time?
In this project, the proposed DSS will support planning and decision-making processes by providing relevant and scientifically sound information to a broad range of stakeholders as it acts as a useful tool leading to an effective management by providing guidance and support, alternatives, options and comparisons of technologies within a reasonable time frame. The developed DSS tool will deal with multiple objectives, criteria, uncertainties, and non-linearities associated with post-mining hazard management and mitigation. It will also act as a portal for disseminating information across all interested parties and for knowledge transfer among all practitioners involved.
The data management component will include a database containing the relevant data and information for the decision situation. The model management component will contain any model(s) used by the DSS (e.g. numerical models). The knowledge management component will include a knowledge-base and an inference engine and will provide intelligence to supplement the operations of the other components. The knowledge base encapsulates domain expertise and is an essential component of intelligent DSS (explained in the sequel). Finally, the decisions that the took provided to the user could be: (i) strategic for high-level managerial support; (ii) tactical for allocating and controlling organisational resources; (iii) operational for supporting day to day activities; (iv) mitigative for providing remediation measures, or (v) have other functionalities according to the discipline or decision situation. A particular novelty in developing the DSS tool for post-mining hazard assessment and risk management will be the use of artificial intelligence (AI) and machine learning (ML) techniques. AI and ML will provide the DSS with the ability to automatically learn and improve from experience from vast amount and diverse data, without being explicitly programmed. The resulting intelligent DSS will be a versatile tool capable of learning, complex analysis and advanced decision support.