Journal of Engineering Research

Groundwater Quality Suitability Using Indices for Industrial and Domestic Purposes in Oredo LGA of Benin City, Nigeria

Animetu Rawlings — 2026-03-12

Groundwater is a vital resource for industrial and domestic purposes, particularly in regions where surface water is scarce. Its suitability, however, is often compromised by corrosion and anthropogenic activities, which reduce industrial efficiency and pose risks to infrastructure and public health. Although physicochemical parameters provide valuable insights into groundwater quality, they do not capture all aspects of suitability. To address this limitation, water quality indices were applied to integrate multiple parameters into a single measure of suitability for industrial and domestic purposes. Borehole samples were collected and tested for pH, Temperature, Electrical Conductivity (EC), Total Dissolved Solids (TDS), Dissolved Oxygen (DO), Bicarbonate (HCO₃^-), Calcium (Ca²⁺), Magnesium (Mg²⁺), Potassium (K⁺), Sodium (Na⁺), Chloride (Cl^-), Nitrite (NO₂^-), Nitrate (NO₃^-), Sulfate (SO₄^2-), Iron (Fe), Zinc (Zn), Copper (Cu), Manganese (Mn), Ammonium Nitrogen (NH₄N), and Coliforms. The Langelier Saturation Index (LSI) indicated a high tendency toward corrosion due to groundwater acidity, rendering it unsuitable for industrial applications. In contrast, the Water Quality Index (WQI) classified the groundwater as suitable for drinking and other domestic uses, though potential threats from anthropogenic activities were evident. These findings highlight the dual nature of groundwater suitability: acceptable for domestic consumption but problematic for industrial use. Regular monitoring, treatment, and management strategies are recommended to mitigate corrosion risks and safeguard public health.

Keywords: Groundwater, Index, Parameter, Suitability, Industrial, Corrosion, Oredo LGA

Application of Convolutional Neural Networks in the Classifying Growth Stages in Rice

Samuel Oluyemi Owoeye — 2026-03-12

Rice is a staple food in many countries, making its efficient production critical to meet growing demand. This study introduces a novel approach by developing a machine learning-based classification system that accurately identifies the growth stages of rice crops—specifically, the "Vegetative," "Reproductive," and "Ripening" stages—using advanced convolutional neural network (CNN) architectures. Utilizing a unique dataset sourced from Roboflow, which includes annotated rice plant images, we meticulously divided the data into training, validation, and testing subsets to ensure robust model performance. Through the application of transfer learning on the ImageNet dataset, we explored the effectiveness of models such as ResNet50, InceptionV3, and MobileNetV2. Our findings indicate that InceptionV3 significantly outperformed the others, achieving a classification accuracy of 95.1% with a log loss of 0.13, compared to 87.3% and 93.5% for ResNet50 and MobileNetV2, respectively. This research not only demonstrates the potential of CNNs in precision agriculture but also provides practical insights into optimal model selection and data preparation techniques. This study highlights the potential of CNNs in precision agriculture and emphasizes the importance of model selection and data preparation in developing efficient crop monitoring and classification systems.

Keywords: Rice Growth, Machine Learning, Convolutional Neural Networks, ResNet50, MobileNetV2, InceptionV3

Prediction of Car Prices in Nigeria Using Machine Learning Models

Samuel Oluyemi Owoeye — 2026-03-12

In Nigeria, where more than 95% of vehicles are used cars, precise car valuation is essential for buyers, sellers, and dealers alike. This research employs machine learning techniques to forecast the prices of pre-owned cars based on various vehicle features. Data were collected from online car sales platforms in Nigeria, encompassing over 8,000 vehicles with attributes such as make, model, transmission type, and overall condition. Following data preprocessing, four machine learning algorithms were evaluated, namely Linear Regression, Random Forest, XGBoost, and Multi-Layer Perceptron (MLP) Regressor. The XGBoost model demonstrated superior performance, achieving a Root Mean Square Error (RMSE) of N7, 047, 536.43, a Mean Absolute Error (MAE) of N 3,540,639.15, and an R-squared (R2) score of 0.8612, indicating that it accounts for 86.12% of the variance in car prices. The most effective model was implemented in a web application based on Streamlit, allowing users to enter vehicle details and obtain price estimates and providing a valuable resource for the automotive market in Nigeria.

Keywords: Car prices; Machine Learning; Linear Regression; Multi-Layer Perceptron; Random Forest; XGBoost