Combining Human and Machine Intelligence to Derive Agents’ Behavioral Rules for Groundwater Irrigation

Abstract:

One major challenge of agent-based modeling is to derive agents’ behavioral rules due to behavioral uncertainty and data scarcity. This study proposes a new approach to combine a data-driven modeling based on the directed information (i.e., machine intelligence) with expert domain knowledge (i.e., human intelligence) to derive the behavioral rules of agents considering behavioral uncertainty. A directed information graph algorithm is applied to identifying the causal relationships between agents’ decisions (i.e., groundwater irrigation depth) and time-series of environmental, socio-economical and institutional factors. A case study is conducted for the High Plains aquifer hydrological observatory (HO) area, U.S. Preliminary results show that four factors, corn price (CP), underlying groundwater level (GWL), monthly mean temperature (T) and precipitation (P) have causal influences on agents’ decisions on groundwater irrigation depth (GWID) to various extents. Based on the similarity of the directed information graph for each agent, five clusters of graphs are further identified to represent all the agents’ behaviors in the study area as shown in Figure 1. Using these five representative graphs, agents’ monthly optimal groundwater pumping rates are derived through the probabilistic inference. Such data-driven relationships and probabilistic quantifications are then coupled with a physically-based groundwater model to investigate the interactions between agents’ pumping behaviors and the underlying groundwater system in the context of coupled human and natural systems.