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Useful properties of phenomenological-based models

Author

Listed:
  • Aguirre-Zapata, Estefania
  • Lema-Perez, Laura
  • Gomez-Echavarria, Lina
  • Botero-Castro, Hector
  • Maya, Juan C.
  • Chejne, Farid
  • Alvarez, Hernan

Abstract

This work compiles the conceptual and practical proposals of various research groups regarding interesting properties of mathematical models. Although different types of mathematical models are mentioned, phenomenological-based semi-physical models (PBSM) are the main focus of this paper. The partitioning of the process to be modeled, the scalability of the model, and the interpretability of its parameters are presented as important properties of the model that help to understand and use the mathematical model. The use of these properties is illustrated with examples, providing the respective bibliographical reference where an expanded presentation of each example can be reviewed. The importance of each model property presented is explained from the inherent condition of the model as a mathematical object and from the expectation of the final users of the model. The confidence in the model is increased when the user knows how the model is obtained. These properties provide valuable information to the end use of the model. Therefore, the modeler must understand and apply this knowledge during model deduction and write, in the model report, how the mentioned properties were tested.

Suggested Citation

  • Aguirre-Zapata, Estefania & Lema-Perez, Laura & Gomez-Echavarria, Lina & Botero-Castro, Hector & Maya, Juan C. & Chejne, Farid & Alvarez, Hernan, 2024. "Useful properties of phenomenological-based models," Ecological Modelling, Elsevier, vol. 496(C).
    • Handle: RePEc:eee:ecomod:v:496:y:2024:i:c:s0304380024002382
    DOI: 10.1016/j.ecolmodel.2024.110850
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    References listed on IDEAS

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    1. Aguirre-Zapata, Estefania & Alvarez, Hernan & Dagatti, Carla Vanina & di Sciascio, Fernando & Amicarelli, Adriana N., 2023. "Parametric interpretability of growth kinetics equations in a process model for the life cycle of Lobesia botrana," Ecological Modelling, Elsevier, vol. 482(C).
    2. Aguirre-Zapata, Estefania & Morales, Humberto & Dagatti, Carla V. & di Sciascio, Fernando & Amicarelli, Adriana N., 2022. "Semi physical growth model of Lobesia botrana under laboratory conditions for Argentina’s Cuyo region," Ecological Modelling, Elsevier, vol. 464(C).
    3. Quang Hung Nguyen & Hai-Bang Ly & Lanh Si Ho & Nadhir Al-Ansari & Hiep Van Le & Van Quan Tran & Indra Prakash & Binh Thai Pham, 2021. "Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-15, February.
    4. Gilioli, Gianni & Pasquali, Sara & Marchesini, Enrico, 2016. "A modelling framework for pest population dynamics and management: An application to the grape berry moth," Ecological Modelling, Elsevier, vol. 320(C), pages 348-357.
    5. Wang, Hsiao-Hsuan & Van Voorn, George & Grant, William E. & Zare, Fateme & Giupponi, Carlo & Steinmann, Patrick & Müller, Birgit & Elsawah, Sondoss & van Delden, Hedwig & Athanasiadis, Ioannis N. & Su, 2023. "Scale decisions and good practices in socio-environmental systems modelling: Guidance and documentation during problem scoping and model formulation," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 5, pages 1-1.
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