IPA (v1): a framework for agent-based modelling of soil water movement

Mewes, Benjamin; Schumann, Andreas

doi:10.5194/gmd-11-2175-2018

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…A general assumption is a flat land topography with no significant side movements of water. An alternative to the PDE formalism and the associated spatial discretization is the agent-based approach proposed in [161]. In this work, the layers were treated as agents that interacted with their upper and lower neighbors, exchanging water downward through infiltration or gravity and upward through evaporation or capillarity.…”

Section: Spatial Heterogeneitymentioning

confidence: 99%

Dynamic Modeling of Crop–Soil Systems to Design Monitoring and Automatic Irrigation Processes: A Review with Worked Examples

Lopez-Jimenez

Wouwer

Quijano

2022

Water

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The smart use of water is a key factor in increasing food production. Over the years, irrigation has relied on historical data and traditional management policies. Control techniques have been exploited to build automatic irrigation systems based on climatic records and weather forecasts. However, climate change and new sources of information motivate better irrigation strategies that might take advantage of the new sources of information in the spectrum of systems and control methodologies in a more systematic way. In this connection, two open questions deserve interest: (i) How can one deal with the space–time variability of soil conditions? (ii) How can one provide robustness to an irrigation system under unexpected environmental change? In this review, the different elements of an automatic control system are described, including the mathematical modeling of the crop–soil systems, instrumentation and actuation, model identification and validation from experimental data, estimation of non-measured variables and sensor fusion, and predictive control based on crop–soil and weather models. An overview of the literature is given, and several specific examples are worked out for illustration purposes.

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Section: Spatial Heterogeneitymentioning

confidence: 99%

Dynamic Modeling of Crop–Soil Systems to Design Monitoring and Automatic Irrigation Processes: A Review with Worked Examples

Lopez-Jimenez

Wouwer

Quijano

2022

Water

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“…Agent-based modeling (ABM) is defined as a computational method that enables a researcher to create, analyze, and experiment with models comprised of agents that interact within an environment [38,39]. Agent-based models allow a deeper analysis of a system's behavior; the relationships among dynamic components; and last but not least, the ability to model unforeseen dynamics in certain model cases that would otherwise be smoothed out in classic numerical models [40]. In ABM, a system is modeled as a collection of autonomous decision-making entities called agents.…”

Section: Agent-based Modelingmentioning

confidence: 99%

Agent-Based Modeling (ABM): Support for Emphasizing the Air Transport Infrastructure Dependence of Space Systems

et al. 2019

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Critical infrastructure is foundational for the prosperity and quality of life in any society. By definition, its destruction or disruption would cause severe damage and possibly loss of life. Within this understanding, space systems are a new category of critical infrastructure, emerging as an enabler of new applications which are critical within the wider system-of-systems. This paper presents the results of a modeling exercise validating the proof of concept regarding the idea of the global, air-transport-critical infrastructure’s dependence on space systems. By using an open-source application, the authors constructed a complex system made up of 18 airports for which six scenarios were modeled that represent either the exposure to specific space phenomena or the effects of a partial or total critical space infrastructure disruption. Despite the limitations and assumptions made in the building of this model, its results suggest that a significant impact would result from disruptive events, with the potential for cascading disruptions within the system, beyond the system under analysis, and into the wider system-of-systems. Tools such as this model are useful to policy- and decision-makers, not only to protect existing, critical infrastructures, but also to adequately source future risks, vulnerabilities, and threats, and design and build new infrastructures.

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“…For instance, an agent structure has been proposed to represent the management decisions of farmers [11,12]. In relation to soil description, [13] presents an ABM framework to model the movement of water through a soil column. To the best of our knowledge, however, no integrated model has addressed surface land variability yet.…”

Section: Introductionmentioning

confidence: 99%

An Agent-Based Crop Model Framework for Heterogeneous Soils

2021

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Climate change and the efficient use of freshwater for irrigation pose a challenge for sustainable agriculture. Traditionally, the prediction of agricultural production is carried out through crop-growth models and historical records of the climatic variables. However, one of the main flaws of these models is that they do not consider the variability of the soil throughout the cultivation area. In addition, with the availability of new information sources (i.e., aerial or satellite images) and low-cost meteorological stations, it is convenient that the models incorporate prediction capabilities to enhance the representation of production scenarios. In this work, an agent-based model (ABM) that considers the soil heterogeneity and water exchanges is proposed. Soil heterogeneity is associated to the combination of individual behaviours of uniform portions of land (agents), while water fluxes are related to the topography. Each agent is characterized by an individual dynamic model, which describes the local crop growth. Moreover, this model considers positive and negative effects of water level, i.e., drought and waterlogging, on the biomass production. The development of the global ABM is oriented to the future use of control strategies and optimal irrigation policies. The model is built bottom-up starting with the definition of agents, and the Python environment Mesa is chosen for the implementation. The validation is carried out using three topographic scenarios in Colombia. Results of potential production cases are discussed, and some practical recommendations on the implementation are presented.

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IPA (v1): a framework for agent-based modelling of soil water movement

Cited by 4 publications

References 32 publications

Dynamic Modeling of Crop–Soil Systems to Design Monitoring and Automatic Irrigation Processes: A Review with Worked Examples

Dynamic Modeling of Crop–Soil Systems to Design Monitoring and Automatic Irrigation Processes: A Review with Worked Examples

Agent-Based Modeling (ABM): Support for Emphasizing the Air Transport Infrastructure Dependence of Space Systems

An Agent-Based Crop Model Framework for Heterogeneous Soils

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