To ensure continued food security and economic development in Africa, it is very important to address and adapt to climate change. Excessive dependence on rainfed agricultural production makes Africa more vulnerable to climate change effects. Weather information and services are essential for farmers to more effectively survive the increasing occurrence of extreme weather events due to climate change. Weather information is important for resource management in agricultural production and helps farmers plan their farming activities in advance. Machine Learning is one of the technologies used in agriculture for weather forecasting and crop disease detection among others. The objective of this study is to develop Machine Learning-based models adapted to the context of daily weather forecasting for Rainfall, Relative Humidity, and Maximum and Minimum Temperature in Senegal. In this study, we made a comparison of ten Machine Learning Regressors with our Ensemble Model. These models were evaluated based on Mean Absolute Error, Mean Squared Error, Root Mean Squared Error and Coefficient of Determination. The results show that the Ensemble Model performs better than the ten base models. The Ensemble Model results for each parameter are as follows; Relative Humidity: Mean Absolute Error was 4.0126, Mean Squared Error was 29.9885, Root Mean Squared Error was 5.4428 and Coefficient of Determination was 0.9335. For Minimum Temperature: Mean Absolute Error was 0.7908, Mean Squared Error was 1.1329, Root Mean Squared Error was 1.0515 and Coefficient of Determination was 0.9018. For Maximum Temperature: Mean Absolute Error was 1.2515, Mean Squared Error was 2.8038, Root Mean Squared Error was 1.6591 and Coefficient of Determination was 0.8205. For Rainfall: Mean Absolute Error was 0.2142, Mean Squared Error was 0.1681, Root Mean Squared Error was 0.4100 and Coefficient of Determination was 0.7733. From the present study, it has been observed that the Ensemble Model is a feasible model to be used for Rainfall, Relative Humidity, and Maximum and Minimum Temperature forecasting.

The extraction of knowledge about the prevalent issues discussed on social media in Africa using Artificial Intelligence techniques is vital for informing public governance. The goals of our study are twofold: (a) to develop machine learning-based models to identify common topics of social concern about Africa on social media, and (b) to design a classifier capable of inferring a particular common topic associated with a given social media post.We designed a three-step framework to achieve the former goal, namely, topic identification. The first step uses text-based representation learning methods to generate text embeddings for feature representation. The second step leverages state-of-the-art Natural Language Processing models, commonly called topic modeling, to organize the representations into groups. The third step generates topics from each group, including by means of using large language models to generate meaningful short-sentence labels from the bag-of-tokens associated with each group. Furthermore, we use Llama2 to deduce the words into a single word theme that describes each topic in relation to social concerns about development. To achieve the second goal of classification; we trained classifiers using ensemble voting and stacking learners to infer which among the identified common topics best characterizes the social media post. For our experimental study, we collected a text corpus called Social Media for Africa composed of 22,036 records extracted from social media comments on Twitter (X) and YouTube. The clustering-based model BERTopic yielded 304 topics, at topic coherence 0.81 C-v. On merging the topics into classes, the BERTopic+ created 11 common topic classes at topic coherence 0.76 C-v. For theme extraction, we additionally refined the leading token words with Llama2 to generate concise single-word theme labels, resulting in 98 unique themes by BERTopic_theme with a C-v score of 0.75 and an IRBO score of 0.50.We then utilized the identified topics based on the resulting groupings as labels for training a topic classifier. These labels were created using Llama2 on our SMA corpus. Our comparative study of topic classifiers using stacking and voting schemes shows that the BERTopic model features 0.83 accuracy and 0.82 F1 score with ensemble voting for training on topics. Furthermore, training on topic classes, BERTopic+ with ensemble voting had the highest accuracy of 0.95 and F1 score of 0.95 compared to other alternate methods on our corpus. The BERTopic_theme also achieved higher performance with ensemble voting classifier at 0.93 F1 score and accuracy 0.93. The overall performance of classifiers using the ensemble stacking is slightly better than that of voting methods for short sentence topic labeling. For Africa, policymakers should focus on the most pressing social issues: COVID-19 restrictions affecting public health and economic recovery, promoting entrepreneurial innovation in energy and environmental sustainability to combat climate change, and strategically responding to China’s rise in global politics to maintain geopolitical stability and foster international cooperation.