NIGERIAN JOURNAL OF THEORETICAL AND ENVIRONMENTAL PHYSICS Spatiotemporal Precipitation Trends and Hybrid Machine Learning Prediction in the Humid Rainforest Climatic Zone of Nigeria

Spatiotemporal Precipitation Trends and Hybrid Machine Learning Prediction in the Humid Rainforest Climatic Zone of Nigeria

This study investigated the spatiotemporal trends and hybrid machine learning prediction of precipitation in Nigeria’s humid rainforest climatic zone using 45 years of monthly rainfall data from Calabar, Yenagoa, Benin City, and Ikeja using Random Forest–Support Vector Regression and Convolutional Neural Network–Long S...

Spatiotemporal Temperature Trends and Hybrid Machine Learning Prediction Across the Coastal Rainforest Zone of Nigeria

This study assessed spatiotemporal temperature trends and machine-learning-based temperature prediction across the coastal rainforest zone of Nigeria. Monthly and annual temperature data for Calabar, Yenagoa, Benin City and Ikeja were obtained from the NASA POWER database for the period 1981–2025. The dataset was proce...

COMPARATIVE EVALUATION OF MACHINE LEARNING AND DEEP LEARNING MODELS FOR WEATHER PREDICTION IN NIGERIA USING SPATIAL ANALYSIS

Accurate prediction of meteorological variables is essential for agricultural planning, disaster risk reduction, water resource management, and climate adaptation in Nigeria, where substantial climatic variability exists across diverse ecological zones. This study presents a comparative evaluation of machine learning,...

A Comparative Study of SVM and GRU for Rainfall Prediction in Manado Using BMKG Meteorological Time-Series Data

Daily rainfall prediction in Lagos using logistic regression, random forest, and support vector machine with web development

An end-to-end rainfall prediction pipeline tailored to the Lagos environment is provided and the practical value of coupling ML with accessible deployment frameworks for climate decision-making in developing countries is demonstrated.

Long-term trend analysis and seasonal forecasting of temperature and rainfall in Sundarban Biosphere Reserve, India using non-parametric and hybrid deep learning approaches