Python

ABSESpy is an innovative agent-based modeling framework designed to improve the fidelity of socio-ecological systems research by integrating complex decision-making, scaling, and data integration through its Branch-Leaf architecture. Its capabilities in modeling human behavior and accommodating diverse temporal scales make it a vital tool for addressing existing gaps in SES research and enhancing the applicability of ABMs to real-world issues.

An agent-based model was developed around the Yellow River’s most far-reaching water quota institution during the past half century, considering how factors such as human behaviour and environmental change have combined with the institutional shifts to lead to changes in the Yellow River’s water use.

ABSESpy makes it easier to build artificial Social-ecological systems with real GeoSpatial datasets and fully incorporate human behavior.

I have five years of experience in Python Programming. I solve daily from small projects for fun to full projects for papers. I master

Collective memory plays a controlling role in adaptation to potential flood risks, by learning from past disasters. Based on survey data, we suggest that using the Universal Decay Model (UDM) proposed by previous researchers provides better fitting results for the decay of flooding memory.

The Yellow River, once the world’s most sediment-rich river, has experienced dramatic regime shifts. We reanalyzed previous datasets to clarify the first historical sediment transport regime shift in the Yellow River. Our results suggest a regime shift occurred only under increased forcing from anthropogenic stresses (after 1900 AD).