Considerations and the necessity of designing a citizen science application software for monitoring hydroclimatic variables (case study: introducing and evaluating the effectiveness of “Payeshyar” App)

Document Type : Original Article

Authors

1 PhD student, Department of Watershed management, Rangeland and Watershed Management Faculty, Gorgan University of Agricultural Sciences and Natural Resources

2 ,Professor, Department of Watershed management, Rangeland and Watershed Management Faculty, Gorgan University of Agricultural Sciences and Natural Resources

3 Assistant Professor, Department of Watershed management, Rangeland and Watershed Management Faculty, Gorgan University of Agricultural Sciences and Natural Resources

Abstract

In recent decades people around the world have embraced "smartphones" and their technologies. The application softwares (Apps) in these devices have become efficient and easy to use. Citizen science (CS) projects that attempt to collect data affordably from people of diverse backgrounds require the design and delivery of a user-friendly technology. In this research, an attempt was made, through reviewing the steps of designing a CS App to address the considerations and necessity of designing and disseminating a CS App with the subject of meteorology and hydrology. Payeshyar App that has been designed by the authors aimed at monitoring three parameters of air temperature, precipitation and water level in border and remote villages of Golestan. In order to gain sufficient experience, after placing simple and inexpensive measurement tools at the disposal of volunteers, the necessary data were collected for a period five months (December 2021-May 2022) via various communication methods including text messages, phone calls, paper and pencil forms and social media Apps. After that, necessary feedbacks and tips were recorded and according to the experiences gained, the initial version of Payeshyar was designed. In addition, in this research, by comparing the data collected through this App during eight months (June 2022 -January 2023) with the conventionally recorded data at formally established monitoring stations, its weaknesses and strengths were listed and its efficiency was investigated. Finally, the review of the effectiveness, advantages, disadvantages, limitations and challenges of Payeshyar showed that a well-designed Apps that can be installed and run on smartphones facilitates the communication of information from users to relevant databases and researches and seems that they are necessary to promote and expand long-term monitoring and surveillance systems based on CS. However, methods of sending meteorological and hydrological data to CS systems is a new field that needs more research.

Keywords

Main Subjects


  1. بهمن‌پور، ث. و سخنی، ا. س. (1387). پایش مشارکتی جنگل حوزه آبخیز صفارود رامسر: روستای بامسی، دیسر، گاورمک، مازولنگه، میان لات و واچ کلایه. انتشارات نخبگان. 206 صفحه.
  2. حاج‌محمدی، م. سعدالدین، ا. عابدی سروستانی، ا. شیخ، و. ب. و جزی، ه. (1396). تحقیق مشارکتی و قابلیت کاربرد آن در تعیین وضعیت سلامت رودخانه دلیچای با کمک جوامع محلی و داوطلب. پایان نامه کارشناسی ارشد. دانشگاه علوم کشاورزی و منابع طبیعی گرگان. گرگان. 86 صفحه.
  3. سنجری بنستانی، م. شیخ، و. ب. زارع گاریزی، آ. آورند، آ. (1398). اهمیت و کاربرد علم شهروندی در هیدرولوژی و مدیریت منابع آب. نشریه آب و توسعه پایدار. 7 (2): 12-1.
  4. سنجری بنستانی، م. شیخ، و. ب. زارع گاریزی، آ. آورند، آ. (1399). امکان‌سنجی پایش حوزه آبخیز با کمک شهروندان آبخیز نشین، مطالعه موردی: آبخیز چهل‌چای استان گلستان. نشریه مهندسی و مدیریت آبخیز. 13(2): 430-417.
  5. شیخ، و. ب. سنجری بنستانی، م. زارع گاریزی، آ. حاتمی گل‌مکانی، پ. (1399). نقش علم شهروندی در تهیه سامانه‌های هشدار سریع بلایای طبیعی. اولین همایش ملی تجربه‌نگاری و آینده‌پژوهی مدیریت مردم نهاد مخاطرات طبیعی با تأکید بر سیل، دانشگاه علوم کشاورزی و منابع‌طبیعی گرگان. 4 و 5 آذرماه.
  6. فرزانه، م. ر و بنی‌مصطفی‌عرب، ف. (1402). تحلیل قوانین سازگاری با تغییر اقلیم در کشورهای درحال توسعه. نشریه پژوهش‌های تغییرات آب و هوایی. 4(13): 54-35.
  7. مصطفی‌زاده، ر. و شیخ. و. ب. (1389). ارزیابی ترکم شبکه بارانسنجی استان گلستان با استفاده از روش همبستگی مکانی. پژوهش‌های آبخیزداری. 93، 87-79.
  8. Android authority, https:// androidauthority.com /develop-android-apps-languages-learn-391008/, (Visited 13 June 2023).
  9. Antoniou, V., and Potsiou, C. (2020). A deep learning method to accelerate the disaster response process. Remote Sensing, 12 (3), 544.
  10. Antoniou, V., and Schlieder, C. (2014). Participation patterns, VGI and gamification. Presented at the proceedings of AGILE 2014.
  11. Bonney R., Ballard H., Jordan R., McCallie E., Phillips T., Shirk J. and Wilderman C.C. (2009). Public Participation in Scientific Research: Defining the Field and Assessing Its Potential for Informal Science Education. Center for Advancement of Informal Science Education (CAISE), 58p.
  12. Bonney, R., Shirk, J. L., Phillips, T. B., Wiggins, A., Ballard, H. L., Miller Rushing, A. , and Parrish,J. K. (2014). Citizen science. Next steps for citizen science. Science, 343 (6178), 1436–1437.
  13. Bowser, A., Hansen, D., He, Y., Boston, C., Reid, M., Gunnell, L., and Preece, J. (2013).  Using gamification to inspire new citizen science volunteers. In Proceedings of the first international conference on gameful design, research, and applications. 18-25.
  14. Crocker, E., Condon, B., Almsaeed, A., Jarret, B., Nelson, C. D., Abbott, A. G and Staton, M. (2020). TreeSnap: A citizen science app connecting tree enthusiasts and forest scientists. Plants, People, Planet, 2 (1), 47-52.
  15. Danielsen F., Burgess N. D. and Balmford A. (2005). Monitoring matters: examining the potential of locally-based approaches. Biodiversity and Conservation, 14(11): 2507-2542.
  16. Designveloper, https://www.designveloper.com/blog/android-app- development- languages/. (Visited 13 June 2023).
  17. Emizent, https://www.emizentech.com/blog/programming-languages-for-android-app-development.html, (Visited 13 June 2023).
  18. Gilmer, J., Adams, R. P., Goodfellow, I., Andersen, D., and Dahl, G. E. (2018). Motivating the rules of the game for adversarial example research. ArXiv preprint arXiv: 1807.06732.
  19. Gura, T. (2013). Citizen science: amateur experts. Nature 496: 259–261.
  20. Gviafrica https://www.gviafrica.co.za/blog/smb-citizen-science-and-climate-change-how-you-can-make-a-difference/. (Visited 17 August 2023).
  21. Haklay, (2013). Citizen science and volunteered geographic information: Overview and typology of participation. InD. Sui, S.Elwood, and M. Goodchild (Eds.), Crowdsourcing geographic knowledge. 105–122.
  22. Haupenthal, K., and Fischer-Stabel, P. (2023). Smart Citizen Science in pluvial flood disaster risk reduction: Building a mobile application as one tool for drain path identification (Work in progress). Short-/Work in Progress-Papers, 65.
  23. Heaven, (2019). Why deep-learning AIs are so easy to fool. Nature, 574(7777), 163.
  24. Hockey PAR, Dean WRJ, Ryan PG (Eds). (2005). Roberts birds of southern Africa (7 edn). Johannesburg, South Africa: Trustees of the John Voelcker Bird Book Fund.
  25. Knapp, C. N., Reid, R. S., Fernandes-Gimenez, M.E., Klein, J. A., and Galvin, K.A. (2019). Placing transdisciplinarity in context: A review of approaches to connect scholars, society and action. Sustainability 11, 18. 
  26. Lee, A.T.K., and Nel, H. (2020). BirdLasser: The influence of a mobile app on a citizen science project. African Zoology, 55(2), 155-160.
  27. Lee, V. (1994). Volunteer monitoring: a brief history. The Volunteer Monitor, 6(1): 29-33.
  28. Lemmens, R., and Antoniou, V. (Eds.) (2019). Hackathon report: House of Apps: Create great apps for citizens. Citizen Science COST Action CA15212.
  29. Lemmens, R., Antoniou, V., Hummer, P., and Potsiou, C. (2021). Citizen science in the digital world of apps. The Science of Citizen Science, 461-474.
  30. Liao, Y. W., Huang, Y. M., Chen, C., and Huang, S. H. (2015). Exploring the antecedents of collaborative learning performance over social networking sites in a ubiquitous learning context. Computers in Human Behavior, 43,313–323.
  31. Liu, H. Y., Dörler, D., Heigl, F., and Grossberndt, S. (2021). Citizen science platforms. The Science of Citizen Science, 22, 439-459.
  32. Mazumdar, S., Ceccaroni, L., Piera, J., Hölker, F., Berre, A., Arlinghaus, R., and Bowser, A. (2018). Citizen science technologies and new opportunities for participation. UCL Press. 303-320.
  33. Mobilecellphoner, https://www.mobilecellphonerepairing.com/mobile-phone-os-operating-system-top-5-with-market-share.html, (Visited 13 June 2023).
  34. Njue N., Kroese J. , Gr.f J., Jacobs S. R., Weeser B., Breuer L. and Rufino M.C. (2019). Citizen science in hydrological monitoring and ecosystem services management: State of the art and future prospects. Science of the Total Environment, 693 (13): 1-18.
  35. Pejovic, , and Skarlatidou, A. (2020). Understanding interaction design challenges in mobile extreme citizen science. International Journal of Human–Computer Interaction, 36 (3), 251–270.
  36. Plageras, A. P., Psannis, K. E., Stergiou, C., Wang, H., and Gupta, B. B. (2018). Efficient IoT-based sensor BIG Data collection Processing and analysis in smart buildings. Future Generation Computer Systems, 82,349–357.
  37. Rieger, , and Majchrzak, T. (2019). Towards the definitive evaluation framework for cross platform app development approaches. Journal of Systems and Software, 153,175–199.
  38. Rothstein, M. A., Wilbanks, J. T., and Brothers, K. B. (2015). Citizen science on your smartphone: an ELSI research agenda: currents in contemporary bioethics. Journal of Law, Medicine and Ethics, 43(4), 897-903.
  39. Rüfenacht, S., Woods, T., Agnello, G., Gold, M., Hummer, P., Land-Zandstra, A., and Sieber, A. (2021). Communication and dissemination in citizen science. The Science of Citizen Science, 475, 520.
  40. See, L. (2019). A review of citizen science and crowdsourcing in applications of pluvial flooding. Frontiers in Earth Science, 7, 44.
  41. Silvertown, (2009). A new dawn for citizen science. Trends in Ecology and Evolution, 24(9), 467–471.
  42. Spasiano, A., Grimaldi, S., Braccini, A. M., and Nardi, F. (2021). Towards a transdisciplinary theoretical framework of citizen science: Results from a meta-review analysis. Sustainability 13, 7904.
  43. Spasiano, A., Grimaldi, S., Nardi, F., Noto, S., and Braccini, A. M. (2023). Testing the theoretical principles of citizen science in monitoring stream water levels through photo-trap frames. Frontiers in Water, 5, 4.
  44. Sturm,U., Luna, , Albert,A., Schade, S., and Kasperowski, D. (Eds.) (2017). Defining principles and guidelines for mobile apps and platform development for best practice in citizen science. Berlin, December13–14, 2016.Report of the workshop.
  45. Teacher, A.G.F., Griffiths, D.J., Hodgson, J., and Inger, R. (2013). Smartphones in ecology and evolution: A guide for the app rehensive. Ecology and Evolution, 3(16), 5268–5278.
  46. Trojan, J., Schade, S., Lemmens, , and Frantál, B. (2019). Citizen science as a new approach in geography and beyond: Review and reflections. Moravian Geographical Reports, 27(4), 254–264.

 

  1. Turabieh, H., Salem, A. A., and Abu-El-Rub, N. (2018). Dynamic L-RNN recovery of missing data in IoMT applications. Future Generation Computer Systems, 89, 575–583.
  2. Tvinnereim, E., Flottum, K., Gjerstad, O ., Johannesson, M. P., and Nordo, A. D. (2017). Citizens’ preferences for tackling climate change. Quantitative and qualitative analyses of their freely formulated solutions. Global Environmental Change, 46, 34-41.
  3. Vercayie, D., and Herremans, M. (2015). Citizen science and smartphones take roadkill monitoring to the next level. Nature Conservation, 11, 29-40.
  4. Wolterink, J. M., Leiner, T., Viergever, M. A., and Išgum, I. (2017). Generative adversarial networks for noise reduction in low-dose CT. IEEE Transactions on Medical Imaging, 36(12), 2536–2545.
  5. Zhang, Q., Yang, L. T., Chen, Z., and Li, P. (2018). A survey on deep learning for big Information Fusion, 42, 146–157.
  6. Zhou, C., and Paffenroth, R. C. (2017). Anomaly detection with robust deep auto encoders. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 665–674). New York: ACM.