Ph.D. in Information Technology: Final Dissertation
DEIB - PT1 Room
June 7th, 2016
2.00 pm
June 7th, 2016
2.00 pm
Sommario
On June 7th, 2016 the final dissertation of the candidate of the Ph.D. in Information Technology will be held at DEIB PT1 Room and will start at 2.00 pm:
Yu Li – XXVIII Cycle
"Advancing Coupled Human-Water Systems Analysis by Agent-Based Modeling"
Advisor: Prof. Andrea Castelletti
Abstract:
Most of the hydrological models developed in the last decades, and ordinarily used for decision support in water system, focus on the natural component in the water cycle whilst human activities are predominantly regarded as external forces that marginally impact the hydrological system. This is, however, contradict to the presence of human signature in many river basins worldwide, and whose increasing impacts are rivaling with the natural forces themselves in conditioning the natural processes and transforming the hydrosphere. The feedback between the human system and the natural ones also imply such process a complex co-evolutionary one, with the projected non-stationary climate change further exacerbating our ability to predict the future conditions of these systems. To this end, there is a growing interest in shifting from traditional human-excluded modeling practices to a more integrated approach, where the human related entities have to be included in the modeling framework to better describe such Coupled Human-Natural Systems (CHNS).
In this work, we developed a decision-analytic framework, namely the DistriLake framework, to analyze and characterize the co-evolution of coupled human water systems (descriptive component), and to design and assess alternative management strategies (prescriptive component). The objective is to build a mathematical model of the co-evolving CHNS under changing climate and socio-economic conditions. The proposed modeling framework was applied on Lake Como water system located in north Italy as a pilot study area, and also as a representative CHNS. In the first application, the framework was shown to successfully capture current situation, and the system may, however, be exposed under a considerable risk under projected drought scenarios. Results show that timely co-adaptation to such changing conditions is necessary to mitigate the negative impact from climate changes. In the second study, we applied the DistriLake framework to investigate the robustness of current system facing deep uncertain scenarios. Results highlight the potential multi-stakeholders' conflicts implicit to traditional robustness based assessments, often with presumption of social-planner's view. The last study adopted the proposed modeling framework to assess the optional value of long-term climate forecast products in supporting farmers' crop planning decisions. Results show that integrating the end-users' decision making process into the assessment framework may provide more insightful thoughts on the poor adoption of the long-term forecast, given the current status of forecast quality.
Yu Li – XXVIII Cycle
"Advancing Coupled Human-Water Systems Analysis by Agent-Based Modeling"
Advisor: Prof. Andrea Castelletti
Abstract:
Most of the hydrological models developed in the last decades, and ordinarily used for decision support in water system, focus on the natural component in the water cycle whilst human activities are predominantly regarded as external forces that marginally impact the hydrological system. This is, however, contradict to the presence of human signature in many river basins worldwide, and whose increasing impacts are rivaling with the natural forces themselves in conditioning the natural processes and transforming the hydrosphere. The feedback between the human system and the natural ones also imply such process a complex co-evolutionary one, with the projected non-stationary climate change further exacerbating our ability to predict the future conditions of these systems. To this end, there is a growing interest in shifting from traditional human-excluded modeling practices to a more integrated approach, where the human related entities have to be included in the modeling framework to better describe such Coupled Human-Natural Systems (CHNS).
In this work, we developed a decision-analytic framework, namely the DistriLake framework, to analyze and characterize the co-evolution of coupled human water systems (descriptive component), and to design and assess alternative management strategies (prescriptive component). The objective is to build a mathematical model of the co-evolving CHNS under changing climate and socio-economic conditions. The proposed modeling framework was applied on Lake Como water system located in north Italy as a pilot study area, and also as a representative CHNS. In the first application, the framework was shown to successfully capture current situation, and the system may, however, be exposed under a considerable risk under projected drought scenarios. Results show that timely co-adaptation to such changing conditions is necessary to mitigate the negative impact from climate changes. In the second study, we applied the DistriLake framework to investigate the robustness of current system facing deep uncertain scenarios. Results highlight the potential multi-stakeholders' conflicts implicit to traditional robustness based assessments, often with presumption of social-planner's view. The last study adopted the proposed modeling framework to assess the optional value of long-term climate forecast products in supporting farmers' crop planning decisions. Results show that integrating the end-users' decision making process into the assessment framework may provide more insightful thoughts on the poor adoption of the long-term forecast, given the current status of forecast quality.