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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
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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.

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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

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