A LOOK AT HOW RAINFALL CHANGES AND HOW THE RAINY Period CHANGES OVER TIME IN MADAGASCAR'S FAR NORTH

Title

A LOOK AT HOW RAINFALL CHANGES AND HOW THE RAINY Period CHANGES OVER TIME IN MADAGASCAR'S FAR NORTH

Authors

1. Steve Lai Chin, University of Toliara, Madagascar, Student, Madagascar

Abstract

Anomalous Deposition and self-organizing patterns (Kohonen Networks) are two important methods for analysing the variability and growth of rainfall over the wet season. The coordinates (latitude/longitude) 13°-17° S / 44°-56° E define the area we study. Regionalization using Kohonen networks identified 20 distinct zones based on the average annual precipitation in our study region between 1980 and 2020. Anomaly accumulation tells us that the northern Madagascar monsoon season normally begins in early December and ends in early April. An average of 125 days, or about 34 months, is how long it lasts. The duration of the wet seasons has been trending downwards. However, with the exception of the eastern maritime areas of our study region, precipitation amounts have increased.

Keywords

climates meteorological rainfall precipitation latitude meteorology

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Conclusion

This work may be broken down into three major phases: dividing the research area into distinct regions as established by the Kohonen network; identifying the beginning and ending of the rainy season in each of these regions using the A technique; and analysing the results. The precipitation patterns in our research area from 1980 to 2020 were used to divide the area into 20 distinct zones, as determined using Kohonen network analysis. Precipitation during in the monsoon season has risen across the board climatologically, with the exception of regions 4 and 10. You've reached the coastal regions to the east of our main research area. The agricultural cycles in this region of Madagascar may be understood by learning about the rainy seasons and their unique features. In order to modernise the agricultural calendar, we propose forecasting the beginning and ending of the rainy season using a nonlinear technique, such as a synthetic neural network.


Reference

1. Pölzlbauer, G. (2004). Application of self-organizing maps to a political dataset. Master Thesis, Vienna University of Technology.

Author Contribution

The author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation.

Funding

The authors did not receive any specific grants from funding agencies in the public, commercial, or non-profit sectors for the research, authorship, and/or publication of this article.

Software Information

Not applicable

Conflict of Interest

All authors declare that they have no conflicts of interest.

Acknowledge

I thank the following individuals for their expertise and assistance in all aspects of our study and for their help in writing the manuscript. I am also grateful for the insightful comments given by anonymous peer reviewers. Everyone's generosity and expertise have improved this study in myriad ways and saved me from many errors.

Data availability

Not applicable