MODELING

Flooding remains one of the most pressing environmental challenges in the Niger Delta region of Nigeria, where rapid urbanization, land degradation, and climate variability interact to intensify hydrological extremes. This study evaluates the combined impact of land use/land cover (LULC) changes and climate variability on flooding in the Lower Niger Region, focusing on Edo, Delta, Bayelsa, and Rivers States. An integrated methodological framework was developed using remote sensing (RS) and machine learning (ML) techniques implemented on the Google Earth Engine (GEE) platform. Rainfall and temperature datasets were obtained from multiple satellite products, CHIRPS, PERSIANN-CDR, PERSIANN-CCS, ERA5, and CPC, and validated against observations from the Nigerian Meteorological Agency (NiMET). LULC classification was conducted using Random Forest (RF) and Support Vector Machine (SVM) algorithms applied to Sentinel-2 and Landsat-8 imagery, generating five major land cover classes: water bodies, forest, barren land, vegetation, and built-up areas. Flood dynamics were assessed using multiple spectral indices, including AWEI, FWI, NDWI, MNDWI, WRI, and NDVI, evaluated across different temporal scales to capture hydrological variations. The results reveal significant interannual and seasonal rainfall variability, with the Standardized Precipitation Index (SPI) effectively identifying alternating wet and dry cycles, although long-term annual rainfall trends were largely non-significant. There is, however, evidence of increasing frequency of extreme rainfall events, particularly in Delta and Rivers States. Temperature analysis (1971–2023) indicates a statistically significant warming trend, with minimum temperatures rising faster than maximum temperatures—most notably during the dry season (DJF). LULC assessment confirmed the superior performance of the RF classifier over SVM and showed a consistent pattern of urban expansion at the expense of vegetation and wetlands. Flood analysis revealed a persistent hotspot occurring along river corridors, low-lying floodplains, and southeastern basins. Overall, the findings demonstrate that while climate variability influences hydrological extremes, LULC transformations, particularly urban encroachment into natural flood buffers, have been a more decisive factor driving flood vulnerability in the Lower Niger Region.

This project models the spread of information in social media networks through advanced computational techniques and simulations. In today’s digital age, social media platforms serve as primary channels for information dissemination. As a result, the rate at which information—and misinformation—spreads increases exponentially, leading to significant implications for society. Understanding the dynamics of information spread proves crucial for various applications, including public health messaging, marketing strategies, and efforts to combat misinformation. The project employs graph theory as a foundational framework to represent social networks, allowing for the visualization and analysis of user interactions and relationships. By constructing a model that captures the essential characteristics of these networks, we simulate how information propagates across different nodes and edges within the network. Key components of the study include an examination of the role of influencers— individuals who possess a higher degree of connectivity and significantly accelerate information dissemination. By identifying these pivotal nodes, the model provides insights into how targeted messaging can effectively reach larger audiences. Additionally, the project investigates the impact of network structure on the spread of information. Researchers analyze different configurations of social networks, such as those characterized by high clustering or short path lengths, to determine how these factors influence the rate and extent of information diffusion. Through a series of simulations, this study explores various scenarios, including the effects of strategic interventions, such as promoting specific influencers or modifying the network structure to enhance information flow. Ultimately, the findings from this project aim to contribute to a deeper understanding of information dynamics in social media, offering valuable insights for practitioners and researchers alike in the fields of public health, marketing, and information science. By enhancing our grasp of these dynamics, we can better leverage social media for positive outcomes while mitigating the risks associated with the rapid spread of misinformatio

This project investigates the effect of key drilling parameters on Rate of Penetration (ROP) using real-world field data from a selected well. The parameters analyzed include Weight on Bit (WOB), Rotational Speed (RPM), and mud properties such as Plastic Viscosity, Yield Point, and Gel Strength. The study aims to understand how variations in these parameters influence ROP and to identify combinations that could
enhance drilling efficiency. Microsoft Excel was used for organizing, calculating, and analyzing the data, with additional tools such as Solver applied for basic optimization. By focusing on a practical, data-driven approach, this work contributes to ongoing efforts in optimizing drilling operations, especially in regions where advanced software and models may be inaccessible. The findings provide insight into the practical relationships between operational parameters and ROP, and highlight opportunities for performance improvement in similar field environments

Corporate mortality modeling refers to the process of predicting the likelihood of a company in a specific sector going out of business or experiencing financial distress. In the manufacturing sector, understanding and accurately predicting corporate mortality is highly important due to the complex and volatile nature of the industry. This work focuses on the analysis of corporate mortality in the manufacturing sector. The manufacturing sector plays a vital role in the global economy, employing a significant number of individuals and contributing to GDP. However, it also faces numerous challenges, such as intense competition, technological advancements, changing consumer demands, and economic fluctuations. The objective of this study is to develop a robust corporate mortality model specifically designed for the manufacturing sector. The model will incorporate various financial and non-financial factors that may influence the likelihood of a company going out of business. Financial factors such as profitability, liquidity, leverage, and solvency will be considered, along with non-financial factors such as industry dynamics, management quality, and market conditions. Data will be collected from a sample of manufacturing companies over a specific period of observation. This data will be used to build a predictive model using advanced statistical techniques such as logistic regression, survival analysis, and machine learning algorithms. The model will be validated using historical data and tested for its predictive accuracy. The results of this study will provide valuable insights into the factors that contribute to corporate mortality