Machine Learning Models for Climate Prediction and Adaptation in Mali: A Technological Assessment
Year: 2014
Type: article
Abstract: This study addresses a current research gap in Computer Science concerning Machine Learning Models for Climate Prediction and Adaptation Planning in Mali. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Machine Learning Models for Climate Prediction and Adaptation Planning, Mali, Africa, Computer Science, original research This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Author: Keita, Ibrahima
Language: English
Subfield: Agronomy and Crop Science
Domain: Life Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W7136181783