CFP last date
20 December 2024
Reseach Article

Evaluating the Constraints of Integrating Additional Climate Data in Developing Zambia’s Rainfall Forecast based on Artificial Intelligence Models

by Lilian Mzyece, Jackson Phiri, Mayumbo Nyirenda
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 45
Year of Publication: 2024
Authors: Lilian Mzyece, Jackson Phiri, Mayumbo Nyirenda
10.5120/ijca2024924111

Lilian Mzyece, Jackson Phiri, Mayumbo Nyirenda . Evaluating the Constraints of Integrating Additional Climate Data in Developing Zambia’s Rainfall Forecast based on Artificial Intelligence Models. International Journal of Computer Applications. 186, 45 ( Oct 2024), 56-68. DOI=10.5120/ijca2024924111

@article{ 10.5120/ijca2024924111,
author = { Lilian Mzyece, Jackson Phiri, Mayumbo Nyirenda },
title = { Evaluating the Constraints of Integrating Additional Climate Data in Developing Zambia’s Rainfall Forecast based on Artificial Intelligence Models },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2024 },
volume = { 186 },
number = { 45 },
month = { Oct },
year = { 2024 },
issn = { 0975-8887 },
pages = { 56-68 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number45/evaluating-the-constraints-of-integrating-additional-climate-data-in-developing-zambias-rainfall-forecast-based-on-artificial-intelligence-models/ },
doi = { 10.5120/ijca2024924111 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-10-26T00:55:48.974425+05:30
%A Lilian Mzyece
%A Jackson Phiri
%A Mayumbo Nyirenda
%T Evaluating the Constraints of Integrating Additional Climate Data in Developing Zambia’s Rainfall Forecast based on Artificial Intelligence Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 45
%P 56-68
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Rainfall forecasting is one of the most challenging topics across the earth and it remains one of the most complex domains. To generate accurate rainfall forecasts, requires use of more meteorological data from both ground and satellite observations with better spatial coverage. Medium and short term (ten days, seven days and daily) forecasts in Zambia are generated by analysing some global models which ingest few of the available surface land observations. While long term (Seasonal rainfall) forecast accuracy was improved when Artificial Intelligence techniques were applied, although only manual station and oceanic data sets were used. To assess the constraints of ingesting additional climate data in the current rainfall forecasting methods in Zambia, a survey questionnaire based on the Unified Theory of Acceptance and Use of Technology (UTAUT) Model was used. The results obtained have shown strong correlation between the independent variables and behavioral intention to use technology. It can therefore be concluded that there is user acceptance and willingness to ingest additional climate data and adopt artificial intelligence technologies in forecasting rainfall in Zambia, that could enhance forecast accuracy.

References
  1. Sethupathi M. Gowtham, Ganesh Yenugudhati Sai, and Ali Mohammad Mansoor, “Efficient Rainfall Prediction and Analysis using Machine Learning Techniques," Turkish Journal of Computer and Mathematics Education, pp. 3467-3474, 2021.
  2. L. Mzyece, M. Nyirenda and J. Phiri, "Forecasting Seasonal Rainfall using a Feed Forward Neural Network with Back-Propagation: A Case of Zambia," IOSR Journal of Computer Engineering (IOSR-JCE), vol. 25, no. 5, pp. 08-18, 2023.
  3. H. Gohil, M. Dodiyar, S. Garasiya and K. Raval, "A Review on Rainfall Forecasting," International Journal of Pure and Applied Research in Engineering and Technology, vol. 7, no. 6, pp. 137-140, 2019.
  4. P. Chouksey and A. S. Chauhan, "A Review of Weather Data Analytics using Big Data," International Journal of Advanced Research in Computer and Communication Engineering, vol. 6, no. 1, pp. 365 - 368, January 2017.
  5. S. Lovalekar, "Big Data: An Emerging Trend in Future," International Journal of Computer Science and Information Technologies (IJCSIT), vol. 5, no. 1, pp. 538-541, 2014.
  6. J. M. Colstona, T. Ahmedb, C. Mahopoc, G. Kangd and M. Koseka, "Evaluating meteorological data from weather stations, and from satellites and global models for a multisite epidemiological study," Elsevier, pp. 91 - 109, 2018.
  7. L. Mzyece, M. Nyirenda, M. K. Kabemba and G. Chibawe, "Forecasting Seasonal Rainfall in Zambia – An Artificial Neural Network Approach," Zambia Information Communication Technology (ICT), pp. 16 - 24, Volume 2 (Issue 1), 2018.
  8. A. Kulkarni and D. Mukhopadhyay, "Internet of Things Based Weather Forecast Monitoring System," Indonesian Journal of Electrical Engineering and Computer Science, vol. 9, no. 3, pp. 555-557, 2018.
  9. M. Nallakaruppan and U. S. Kumaran, "IoT based Machine Learning Techniques for Climate Predictive Analysis," International Journal of Recent Technology and Engineering (IJRTE), vol. 7, no. 2, pp. 171 - 175, January 2019.
  10. Simon H. Chiwamba, Mayumbo Nyirenda, Jackson Phiri, Monde M. Kabemba, Philip O. Y. Nkunika and Philemon H. Sohati, "Machine Learning Algorithms for Automated Image Capture and Identification of Fall Armyworm (FAW) Moths", Zambia Information Communication Technology (ICT) Journal, Vol 3, Issue 1, pp. 1-4, 2019.
  11. M. Jarraud, J. Lengoasa and E. Manaenkova., "The World Weather Watch at 50," The Journal of the World Meteorological Organization, vol. 62, no. 1, pp. 1 - 44, 2013.
  12. Z. Mumba, "Consultancy for the Establishment of Weather and Climate Facility at the Zambia Meteorological Department", Completion Report, Lusaka, 2020.
  13. L. Mzyece, "Forecasting Seasonal Rainfall in Zambia using Artificial Neural Networks," University of Zambia, Lusaka, 2019.
  14. B. Chimnulu, "Government Completes Installing 121 Automatic Weather Stations," Daily Nation Newspaper, Living stone, 2024.
  15. T. R. Prajwala, D. D. Ramesh and H. D. Venugopal, “Modeling and Forecasting of Rainfall using IoT sensors and Adaptive Boost Classifier for a Region,” International Conference on IoT based Control Networks and Intelligent Systems (ICICNIS 2020), 2020.
  16. Singh Sumanta Kumar and Mishra Ashis Kumar, "Rainfall Prediction Using Big Data Analytics," International Journal of Innovations in Engineering and Technology (IJIET), vol. Volume 15, no. Issue 1, pp. 58 - 60, December 2019.
  17. WMO World Meteorological Organisation, WIGOS Data Quality Monitoring System - ECMWF, "https://wdqms. wmo.int/nwp/land_surface/six_hour/availability/pressure/all/20 23-10-15/18/", "[Online; accessed 17-October-2023]" 2023.
  18. T. Dinku, R. Faniriantsoa, R. Cousin, I. Khomyakov and A. Vadillo, "ENACTS: Advancing Climate Services Across Africa," Climate Services, a Section of the Journal Frontiers in Climate, vol. 3, pp. 1- 16, 2022.
  19. Roy Chaikatisha and Mayumbo Nyirenda, "Resilient Machine Learning Model for Seasonal Rainfall Forecasting Using Enhanced Forecast Data", IOSR Journal of Computer Engineering (IOSR-JCE) Vol 26, Issue 2, Ser. 3 (Mar – Apr 2024), PP 23-32, 2024.
  20. Mohammed Alshehri, Steve Drew, Rayed AlGhamdi, "Analysis of Citizens’ Acceptance for E-Services: Applying the UTAUT Model," IADIS International Conferences Theory and Practice in Modern Computing and Internet Applications and Research, 2012.
  21. Thomas, Troy Devon; Lenandlar Singh and Gaffar Kemuel, "The utility of the UTAUT Model in Explaining Mobile Learning Adoption in Higher Education in Guyana," International Journal of Education and Development using Information and Communication Technology (IJEDICT), vol. 9, no. 3, pp. 71- 85, 2013.
  22. Pedro Neves Rito, "Analyzing the Use and extensions of UTAUT Model," Innovation Vision 2020: Sustainable growth, Entrepreneurship, and Economic Development, pp.1000- 1007, 2020.
  23. V. Venkatesh, Micheal G. Morris, Gordon. B. Davis. and Fred D. Davis, "User Acceptance of Information Technology: Toward a Unified View," MIS Quarterly, vol. 27, no. 3, pp. 425-478, 2003.
  24. A. M. Momani, "The Unified Theory of Acceptance and Use of Technology: A New Approach in Technology Acceptance," International Journal of Socio-Technology and Knowledge Development, vol. 12, no. 3, pp. 79 - 98, 2020.
  25. G. B. Batucan, G. G. Gonzales, M. G. Balbuena, K. R. B. Pasaol, D. N. Seno and R. R. Gonzales, "An Extended UTAUT Model to Explain Factors Affecting Online Learning System Amidst COVID-19 Pandemic: The Case of a Developing Economy," Frontiers in Artificial Intelligence, vol. 5, no. 768831, p. 1 13, 2022.
  26. P. Isaias, F. Reis, C. Coutinho and A. J. Lencastre, " Empathic technologies for distance/mobile learning: An empirical research based on the unified theory of acceptance and use of technology (UTAUT).," Interactive Technology and Smart Education, vol. 14, no. 2, p. 159–180., 2017.
  27. Arumugam Raman, Yahya Don, Rozalina Khalid1 and Mohd Rizuan, "Usage of learning management system (Moodle) among post-graduate students: UTAUT model.," Asian Social Science, vol. 10, p. 186–195, 2014.
  28. Sodiq Onaolapo and Olawale Oyewole, "Performance Expectancy, Effort Expectancy, and Facilitating Conditions as Factors Influencing Smart Phones Use for Mobile Learning by Postgraduate Students of the University of Ibadan, Nigeria," Interdisciplinary Journal of E-Skills and Life Long Learning, vol. 14, pp. 96 - 115, 2018.
  29. Sahar Afshan and Arshian Sharif, "Acceptance of mobile banking framework in Pakistan.," Telematics and Informatics, vol. 33, no. 2, p. 370–387, 2016.
  30. Goncalo Baptista and T. Oliveira, "Understanding mobile bank ing: The unified theory of acceptance and use of technology combined with cultural moderators.," Computers in Human Behavior, vol. 50, p. 418–430, 2015.
  31. C. Martins, T. Oliveira and A. Popovic, "Understanding the internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application," International Journal of Information Management, vol. 34, no. 1, p. 1–13, 2014.
  32. L. H. Asastani, V. H. K. Harisno and S. H. L. H. Warnars., "Factors affecting the usage of mobile commerce using technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT).," Indonesian Association for Pattern Recognition International Conference (INAPR), 2018.
  33. S. Kabanda and I. Brown, "A structuration analysis of Small and Medium Enterprise (SME) adoption of E-Commerce: The case of Tanzania.," Telematics and Informatics, vol. 34, no. 4, p. 118–132, 2017.
  34. A. Ayaz and M. Yanartas, "An analysis on the unified theory of acceptance and use of technology theory (UTAUT): Acceptance of electronic document management system (EDMS," Elsevier - Computers in Human Behavior Reports, vol. 2, pp. 1 -7, 2020.
  35. Carlos M. Afonso, José L. Roldán, Manuel Sánchez-Franco and María de la O Gonzalez, "The moderator role of gender in the unified theory of acceptance and use of technology (UTAUT): A study on users of electronic document management systems," In En 7th International Conference on partial least squares and related methods, Houston, 2012.
  36. A. Donmez-Turan, "Does unified theory of acceptance and use of technology (UTAUT) reduce resistance and anxiety of individuals towards a new system?" Kybernetes, vol. 49, no. 5, p. 1381–1405, 2019.
  37. Olefhile Mosweu, Kelvin Joseph Bwalya, and Athulang Mutshewa, "A probe into the factors for adoption and usage of electronic document and records management systems in the Botswana context," Information Development, vol. 33, no. 1, p. 97–110, 2016.
  38. Mohammad Zahedu Alam, Wang Hu, Zapan Barua, "Using the UTAUT Model to Determine Factors Affecting Acceptance and Use of Mobile Health (mHealth) Services in Bangladesh," Journal of Studies in Social Sciences, vol. 17, no. 2, pp. 137- 172, 2018.
  39. Sera Yoo, and Hae-Ra Han, "Facilitators of and Barriers to mHealth Adoption in Older Adults with Heart Failure.," CIN: Computers Informatics, Nursing, vol. 36, no. 8, pp. 376-382, 2018.
  40. C. Y. Joa and K. Magsamen-Conrad, "Social influence and UTAUT in predicting digital immigrants’ technology use," Behaviour and Information Technology, pp. 1 - 19, 2021.
  41. Zahir Irania, Peter E.D. Love and S. Jones, " Learning lessons from Evaluating eGovernment: Reflective Case Experiences that Support Transformational Government," Journal of Strategic Information Systems, vol. 17, no. 2, pp. 155-164, 2008.
  42. I. Ajzen, "The Theory of Planned Behaviour. Organizational Behaviour and Human Decision Processes," Elsevier, vol. 50, no. 2, pp. 179-211, 1991.
  43. P. Leedy and J. Ormrod, Practical Research: Planning and Design (7th Ed) Upper Saddle River, NJ: Merrill Prentice: SAGE Publication, 2001.
  44. R. B. Kline, Principles and Practice of Structural Equation Modeling (2nd ed.)., New York: Vineland adaptive behavior scales. Circle Pines, MN: American Guidance Service, 2005.
  45. Uma Sekaran, Research Methods for Business: A Skill-Building Approach, New York: John Wiley and Sons, 2003.
  46. Said Taan EL Hajjar, "Statistical Analysis: Internal- Consistency Reliability and Construct Validity" International Journal of Quantitative and Qualitative Research Methods - European Centre for Research Training and Development UK, Vol.6, No.1, p.46-57, February 2018.
  47. K. S. Taber, "The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education," Research Science Education, vol. 48, p. 1273–1296, 2018.
  48. Richard P. Bagozzi, Youjae Yi and Lynn W. Phillips, "Assessing Construct Validity in Organizational Research" Administrative Science Quarterly - Published By: Sage Publications, Inc., Vol. 36, No. 3, p. 421-458, September, 1991).
  49. Siddig Omer Abdalla, Hasaballah Abdel Hafiez Ali, Al-Tit Ahmad, Almohaimmeed Bade, "Confirmatory factor analysis for testing the validity and reliability of an internal capability and logistics outsourcing measurement scale", DOI 10.20858/tp.2019.14.1.13, Vol 14, Issue 1, 2019.
  50. Maan Isabella Cajita, Nancy A. Hodgson, Katherine Wai Lam, Matthew W. Gallagher and Timothy A. Brown, "Handbook of Quantitative Methods for Educational Research," Sense Publishers, 2013, p. 289–314.
  51. Gordon W. Cheung, Helena D. Cooper-Thomas, Rebecca S. Lau and Linda C. Wang, "Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations" Asia Pacific Journal of Management, Volume 41, p. 745–783, 2024.
  52. David Gefen and Detmar Straub, "A practical guide to factorial validity using PLS-Graph: Tutorial and annotated example," The communications of the Associations for Information Systems, vol. 16, no. 5, p. 91–109., 2005.
  53. Thomas Groß, "Validity and Reliability of the Scale Internet Users’ Information Privacy Concerns (IUIPC)" Proceedings on Privacy Enhancing Technologies, 2 p. 235–258, 2021.
  54. J. Hair, W. Blake, B. Tatham and R. Babin, Multivariate Data Analysis, New Jersey: Prentice Hall, 2006.
  55. J. W. Young, J. D. Jentsch, T. J. Bussey, T. L. Wallace and D. M. Hutcheson, "Consideration of Species Differences in developing Novel Molecules as Cognition Enhancers," Elsevier - Neuroscience and Biobehavioral Reviews, vol. 37, no. 9, pp. 2181-2193, 2013.
  56. Todd Michael Franke, Timothy Ho and Christina A. Christie, "The Chi-Square Test: Often Used and More Often Misinterpreted", American Journal of Evaluation vol 33, Issue 3, p. 448-458, 2012.
  57. Dr. Nowacki, “Biostatistics and Epidemiology”, CLEVELAND CLINIC JOURNAL OF MEDICINE vol 84, 2, p. 20 - 25, 2017.
  58. N. S. Turhan, "Karl Pearson’s chi-square test Christina A. Christies," Academic Journal - Educational Research and Reviews, vol. 15, no. 9, pp. 575-580, 2020.
  59. Giovanni Di Leo and Francesco Sardanelli, "Statistical significance: p value, 0.05 threshold, and applications to radiomics reasons for a for a conservative approach", European Radiology Experimental, vol 4, 18, 2020.
  60. D. Bergh, "Chi-Squared Test of Fit and Sample Size-A Comparison between a Random Sample Approach and a Chi-Square Value Adjustment Method," Journal of Applied Measurement, vol. 16, no. 2, pp. 204-217, 2015.
  61. H.-Y. Kim, "Statistical notes for clinical researchers: the independent samples t-test," doi: 10.5395/rde.2019.44. e26, vol. 44, no. 3, 2019.
  62. Knowledge Portal, title = JMP Statistical Discovery LLC, author = Sall, year = 2013, url = https://www.jmp.com/en-ch/statistics-knowledge-portal/t- test.html, URL date = 25 July 2024
  63. Ebenezer E. AKPAN and Lion J. Clark, "Independent T-Test Statistics: It’s Relevance in Educational Research", International Journal of Eminent Scholars, Vol 10, Issue 1, P. 79 - 88, 2023
  64. A. Pwasong and M. M. K. M.M., "The P-Value Concept in Hypothesis Testing and Its Application on Mortality Rate Data," West African Journal of Industrial and Academic Research, vol. 10, no. 1, pp. 136 - 143, 2014.
  65. Nikolaos Pandis, "Linear Regression", Statistics and research design Vol 149, Issue 3, p. 431-434, 2016.
  66. K. Kumari and S. Yadav, "Linear Regression Analysis Study, "Journal of the Practice of Cardiovascular Sciences, vol. 4, no. 1, pp. 33 - 36, 2018.
  67. X. Jiao and F. Pretis, "Testing the Presence of Outliers in Regression Models, "Oxford Bulletin of Economics and Statistics-doi: 10.1111/obes.12511, vol. 84, no. 6, pp. 1452 - 1484, 2022.
  68. L. O. Tedeschi and M. L. Galyean, "A practical method to Account for Outliers in Simple Linear Regression using the Median of Slopes," Animal Science and Pastures, vol. 81, pp. 1 - 8, 2023.
  69. S. Rahman, M. Sathik and K. Kannan, "Multiple Linear Regression Models in Outlier Detection," International Journal of Research in Computer Science, vol. 2, no. 2, pp. 23-28, 2012.
  70. K. R. Das and A. H. M. R. Imon, "A Brief Review of Tests for Normality," American Journal of Theoretical and Applied Statistics, vol. 5, no. 1, pp. 5 - 12, 2016.
  71. R. M. Ibrahim, M. A. Ghani and A. M. M. S. Embat, "Organizational Citizenship Behavior among Local Government Employees in East Coast Malaysia: A Pilot Study," International Business Research, vol. 6, no. 6, pp. 83 - 94, 2013.
  72. R. Christensen and E. J. Bedrick, "Testing the Independence Assumption in Linear Models," Journal of the American Statistical Association, vol. Vol. 92, no. 439, pp. 1006-1016, 1997.
  73. S. Sumaedi, I. G. M. Y. Bakti and N. Metasari, "The Effect of Students’ Perceived Service Quality and Perceived Price on Student Satisfaction," Management Science and Engineering, vol. 5, no. 1, pp. 88-97, 2011.
  74. M. L. Mouritsen, J. T. Davis and S. C. Jones, "ANOVA Analysis of Student Daily Test Scores in Multi-Day Test Periods," Journal of Learning in Higher Education, vol. 12, no. 2, pp. 73 - 82, 2016.
  75. J. Walker, "Hypothesis Testing," BJA Education, vol. 19, no. 7, p. 227–231., 2019.
Index Terms

Computer Science
Information Sciences
Artificial Intelligence
Big Data Technologies
Machine Learning
Rainfall Forecasting
Climate Data Integration
Weather Prediction Systems

Keywords

Artificial Intelligence Big Data Technologies Machine Learning Rainfall Forecast UTAUT Model