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Modeling the brown plant hoppers surveillance network using agent-based model: application for the Mekong Delta region

Published: 13 October 2011 Publication History

Abstract

This paper aims at modeling a brown plant hopper (BPH) surveillance network, called BPH surveillance network model (BSNM). In this model, we apply the Unit Disk Graph (UDG) technique to setup a graph with multiple surveillance nodes (light traps). An agent-based model (ABM) is built to model some necessary environmental and ecological factors, in which, the model contains two smaller models called the Environmental and Ecological Model (EEM) and the Surveillance Network Model (SNM). The key ideas of the surveillance network concentrate on the density estimations based on UDG technique and the up-scale aggregation via multiple levels of spatial regions. The simulation is applied for the province Dong Thap, a typical province in region of Mekong Delta of Vietnam.

References

[1]
Otuka, A. 2005. A migration analysis of the rice planthopper Nilaparvata lugens from the Philippines to East Asia with three-dimensional computer simulations. In The Society of Population Ecology and Springer-Verlag Tokyo.
[2]
Fujita, D., et al. 2009. The genetics of host-plant resistance to rice planthopper and leafhopper. Planthoppers: new threats to the sustainability of intensive rice production systems in Asia. International Rice Research Institute (Los Baños), 389--400.
[3]
Spalding, A. 2004. Light trap transects -- a field method for ascertaining the habitat preferences of night-flying Lepidoptera, using Mythimna turca (Linnaeus 1761) (Lepidoptera: Noctuidae) as an example. Kluwer Academic, Journal of Insect Conservation, 8, 185--190.
[4]
Schmera, D. 2003. Assessing stream dwelling caddisfly assemblages (Insecta: Trichoptera) collected by light traps in Hungary. Kluwer Academic, Biodiversity and Conservation, 12, 1175--1191.
[5]
Kato, M., et al. 2000. Various population fluctuation patterns of light-attracted beetles in a tropical lowland dipterocarp forest in Sarawak. In The Society of Population Ecology and Springer-Verlag Tokyo. Population Ecology, 42, 97--104.
[6]
Marathe, M. V., Breu, H., Hunt, H. B. 1995. Simple Heuristics for Unit Disk Graphs. Wiley Periodicals, Inc. Networks, 25 (2), 59--68.
[7]
Nguyen, N. D., et al. 2010. Inferring equation-based models from individual-based models. India. PRIMA2010 Conference, 183--190.
[8]
Köhl, M., Magnussen, S., Marchett, M. 2006. Sampling Methods, Remote Sensing and GIS Multiresource Forest Inventory. Berlin. Spinger, ISBN: 3-540-32571-9.
[9]
Kozak, M. and Wieczorlowski, R. 2005.IIPS sampling versus stratified sampling - Comparison of effeciency in agricultural surveys. Statistics in transaction, 7 (1), 5--12.
[10]
Trumper, E. V. and Garat, O. 2001. Population density sampling and dispersion pattern of Delphacodes kuscheli Fennah (Homoptera: Delphacidae) in oat crops: Asociación Argentina de Ecología, Ecologia Austral, 11, 123--130.
[11]
Bellido, J. M., Bellido, J. M. and Pérez, N. 2007. A new optimal allocation sampling design to improve estimates and precision levels of discards from two different Fishery Units of Spanish trawlers in northeast Atlantic waters (ICES subareas VIIc, j, k): Instituto Español de Oceanografia, Boletin. Instituto Español de Oceanografia, 23, 73--83. ISSN: 0074--0195.
[12]
Talvitie, M., Leino, O. and Holopainen, M. 2006. Inventory of Sparse Forest Populations Using Adaptive Cluster Sampling. In The Finnish Society of Forest Science, Silva Fennica, 40 (1), ISSN: 0037--5330.
[13]
Brännström, Å., Sumpter, D. J. T. 2005. Coupled map lattice approximations for spatially explicit individual-based models of ecology. Elsevier Ltd., Bulletin of Mathematical Biology, 67, 663--682.
[14]
Phan, H. C., Huynh, X. H., Drogoul, A. 2010. An agent-based aproach to the simulation of Brown Plant Hopper (BPH) invasion in the Mekong Delta. International Conference on Computing and Communication Technologies (IEEE-RIVF 2010), Hanoi, Vietnam.
[15]
Treuil, J-P., Drogoul, A. and Zucker, J-D. 2008. Modélisation et Simulation à base d'Agents. Dunod Editions. ISBN: 978-2-10-050216-5.
[16]
Taillandier, P., Vo, D. A., Amouroux, E. and Drogoul, A. 2010. GAMA: a simulation platform that integrates geographical information data, agent-based modeling and multi-scale control. 13th International Conference on Principles and Practice of Multi-Agent System (PRIMA 1010), Kolkata, India.
[17]
Amouroux, E., Gaudou, B., Desvaux, S., Drogoul, A. 2010. O.D.D.: a Promising but Incomplete Formalism For Individual-Based Model Specification. International Conference on Computing and Telecommunication Technologies (IEEE-RIVF 2010), Hanoi, Vietnam.
[18]
Brent N. C., Charles J. C. and David S. J. 1990. Unit Disk Graph. Holland. Elsevier Science Publishers B. V. (North-Holland). Discrete Mathematics, 86, 165--177.
[19]
Glad, A., Simonin, O., Buffet, O., Charpillet, F. 2010. Influence of Different Execution Models on Patrolling Ant Behaviors: from Agents to Robots. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS '10), 3, 1173--1180.
[20]
Le, N. M., Nguyen, T. 2010. A Robotics Modeling, Design and Simulation Toolbox in Ptolemy II. Chinese Control and Decision Conference, 2297--2301. 978-1-4244-5182-1/10
[21]
Website: http://code.google.com/p/gama-platform/
[22]
Truong, X. V., Huynh, X. H., Le, N. M., Drogoul, A. 2011. Estimating the density of Brown Plant Hoppers from a light-traps network based on Unit Disk Graph The 2011 International Conference on Active Media Technology (AMT 2011), LNCS 6890, Springer-Verlag Berlin Heidelberg 2011, 276--287.
[23]
N. Mantzafleri, Ar. Psilovikos, A. Blanta. 2009. Water Quality Monitoring and Modeling in Lake Kastoria, Using GIS. Assessment and Management of Pollution Sources. Water Resour Manage, 23, 3221--3254.
[24]
Jeremy S. (2009). Better simulation metamodeling: the why, what, and how of stochastic Kriging. Winter Simulation Conference (IEEE 2009). 978-1-4244-5771-7/09.

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    cover image ACM Other conferences
    SoICT '11: Proceedings of the 2nd Symposium on Information and Communication Technology
    October 2011
    225 pages
    ISBN:9781450308809
    DOI:10.1145/2069216
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 13 October 2011

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    Author Tags

    1. Agent-Based Model (ABM)
    2. Brown Plant Hopper (BPH)
    3. Unit Disk Graph (UDG)
    4. estimation
    5. sampling
    6. sensor network
    7. surveillance network
    8. system dynamics

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    • (2015)Prediction of rice brown planthoppers based on system dynamics2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)10.1109/FSKD.2015.7382359(2559-2564)Online publication date: Aug-2015
    • (2012)Upscaling and Assessing Information of Agriculture Indicators in Agent-Based Assessment Model from Field to Region ScaleProceedings of the 2012 Fourth International Conference on Knowledge and Systems Engineering10.1109/KSE.2012.9(136-142)Online publication date: 17-Aug-2012
    • (2012)Dynamic Evaluating Rice Pest Risk State of Decision Maker Agents in Rice Pest Management ModelProceedings of the 2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation10.1109/EMS.2012.55(39-47)Online publication date: 14-Nov-2012
    • (2012)Toward an Agent-Based Multi-scale Recommendation System for Brown Plant Hopper ControlProceedings of the 2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation10.1109/EMS.2012.18(9-14)Online publication date: 14-Nov-2012
    • (2012)Designing Multi-criteria Decision Making Agents in Agent-Based Model for Rice Pest Risk ManagementProceedings of the 2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation10.1109/CIMSim.2012.21(88-93)Online publication date: 25-Sep-2012
    • (2012)Modelling multi-criteria decision making ability of agents in agent-based rice pest risk assessment modelProceedings of the 8th international conference on Active Media Technology10.1007/978-3-642-35236-2_14(134-144)Online publication date: 4-Dec-2012
    • (2012)Modeling a Surveillance Network Based on Unit Disk Graph Technique – Application for Monitoring the Invasion of Insects in Mekong Delta RegionPRIMA 2012: Principles and Practice of Multi-Agent Systems10.1007/978-3-642-32729-2_16(228-242)Online publication date: 2012

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