###### Abstract
Our linear regression models incorporate various soil properties and environmental variables to predict geotechnical behavior. We consider soil systems' inherent variability and uncertainty by including a noise component. This approach allows for a more realistic representation of the complex interactions between soil properties and external factors affecting dam performance. While these methods have shown promise in enhancing our understanding of earth dam behavior, there is still a need for further integration of multidisciplinary approaches to improve soil behavior models' predictive accuracy and reliability. Future research could explore applying more advanced modeling techniques like machine learning and artificial intelligence to provide deeper insights into the complex interactions between soil properties and environmental factors [23]. Numerous studies have focused on the stability analysis of earth dams under various loading conditions, such as high reservoir levels, rapid drawdown, and seismic events. These analyses often employ finite element modeling techniques and slope stability methods to assess factors of safety and potential failure mechanisms [26; 27]. Soil erodibility, which quantifies a soil's inherent susceptibility to detachment and transport by erosive forces, has been extensively studied in agricultural land management and soil conservation [28]. However, its application in earth dam engineering is limited, presenting an opportunity for cross-disciplinary research. Additionally, the effects of seasonal variations and long-term environmental changes on soil behavior have been explored, particularly in regions with pronounced wet-dry cycles [29]. Phenomena such as soil expansion and contraction can have significant implications for the structural integrity of earth dams, necessitating the development of predictive models and monitoring protocols. Our research presents several significant advancements in the field of geotechnical engineering, particularly in the context of earth dam stability assessment. A key innovation is applying a linear regression model with noise to predict soil expansion rates. This model achieved remarkably high accuracy, with R\({}^{2}\) values of 0.990 for training and 0.989 for testing. Such precision is uncommon in geotechnical engineering, where soil behavior is typically influenced by a multitude of factors, making accurate predictions challenging. Another notable contribution is the innovative application of the Revised Universal SoilLoss Equation (RUSLE) to estimate soil erodibility factors in a dam environment. This cross-disciplinary approach leverages an established soil erosion model to enhance geotechnical risk assessment. The RUSLE, initially developed for agricultural contexts, was adapted to suit the specific conditions of earth dam structures [30]. Our comprehensive study of the downstream area of the Tiga Dam offers valuable insights into its geotechnical behavior. Analyzing soil expansion rates from January 2010 to December 2020, we identified detailed seasonal patterns and long-term trends, providing a nuanced understanding of temporal variations in soil properties. The Tiga Dam in Kano State, Nigeria, is a significant hydraulic structure whose downstream soil properties have been extensively studied to understand their geotechnical behavior [31]. This research mainly focused on various aspects of soil properties, including expansion rates, erodibility, Arterberg limits, specific gravity, grain size distribution, permeability, shear strength characteristics, and stability analysis. This study aims to conduct a comprehensive geotechnical evaluation of the downstream area of the Tiga Dam.