Principal Component Analysis

This study assessed the quality of groundwater in Ekosodin community, Edo State, Nigeria, using geospatial techniques and Principal Component Analysis (PCA). The objectives were to analyze selected physicochemical parameters of groundwater, compare the results with World Health Organization (WHO) and Nigerian Industrial Standards (NIS), map the spatial distribution of groundwater quality, and identify the major factors influencing contamination. The study was necessitated by the increasing dependence on groundwater due to inadequate public water supply and the rising risk of contamination from anthropogenic activities. Groundwater samples were collected from eight boreholes across the study area, and their geographic coordinates were recorded using a handheld GPS device. Laboratory analyses were conducted on selected physicochemical parameters including major ions and heavy metals using standard APHA procedures. Geospatial analysis was carried out using GIS (Inverse Distance Weighting) to map the spatial distribution of groundwater quality parameters. Principal Component Analysis (PCA) was applied to reduce data dimensionality and identify the dominant factors influencing groundwater quality. The results revealed significant variation in groundwater quality across the study area. The Water Quality Index (WQI) ranged from 30.31 to 227.36, classifying samples into excellent, good, poor, and very poor categories. Samples 6 and 7 recorded excellent quality (30.54– 41.84), while samples 2, 3, and 4 showed poor to very poor quality (136.47–227.36), indicating unsuitability for drinking without treatment. Principal Component Analysis (PCA) extracted four components accounting for 97.4% of the total variance, indicating that both geogenic processes and anthropogenic activities are the major factors influencing groundwater quality in the area. The study concluded that groundwater in Ekosodin is vulnerable to contamination and requires regular monitoring, improved waste management, and public awareness to ensure safe and sustainable use

Wireless Sensor Networks (WSNs) play a crucial role in modern communication systems, particularly in environmental monitoring, industrial automation, and smart cities. However, a major challenge in WSNs is optimizing energy consumption due to the limited power resourcesof sensor nodes. One of the most effective ways to enhance energy efficiency is through data compression, which reduces the amount of transmitted data while preserving essential information.
This project explores the integration of machine learning-based data compression techniques to improve energy-efficient transmission in WSNs. A hybrid approach is proposed, combining Run-Length Encoding (RLE) as a traditional lossless compression method with Principal Component Analysis (PCA) as a machine learning algorithm to reduce data redundancy while maintaining accuracy. The study focuses on temperature sensor datasets collected over a specified period, ensuring real-world applicability.
The methodology involves preprocessing raw temperature data, applying Run-Length Encoding (RLE) for initial redundancy reduction, and then leveraging PCA to extract principal components, further reducing data dimensions before transmission. The efficiency of the proposed model is evaluated based on key metrics such as compression ratio, reconstruction accuracy, and energy savings. Performance comparisons are made with conventional lossless compression algorithms like Huffman Coding and Arithmetic Coding to assess improvements.
Preliminary results indicate that the combined approach achieves a higher compression ratio while preserving critical temperature variations, leading to significant energy savings in wireless
transmissions. This work contributes to advancing energy-efficient data handling in WSNs, making it highly relevant for resource-constrained environments. Future research directions include expanding the model to handle multi-sensor data streams and implementing real-time adaptive compression strategies