Paper Details

Effective flood management relies on accurate predictions. Visual modeling techniques play a crucial role in hydrology and water resources management. This study analyzed data from Hydrological Area 8. The analysis employed flexplot, linear modeling, mixed modeling, and generalized linear modeling. The results provide valuable insights into hydrological patterns and trends. Flexplot visualization revealed a significant positive relationship between Kastina and the response variable. Linear modeling identified Kastina (β = 0.464, p < 0.01) and Gusa (β = 0.552, p < 0.01) as significant predictors, while Goroyo showed no significant effect. Mixed modeling confirmed these findings, with Kastina (estimate = 0.267, p < 0.01) and Gusa (estimate = 0.272, p < 0.01) exhibiting significant positive relationships. Generalized linear modeling supported these results, with Kastina (estimate = 0.274, p < 0.01) and Gusa (estimate = 0.313, p < 0.01) showing significant positive effects. Model comparisons confirmed the importance of Kastina and Gusa. The regression analysis yielded significant results, providing insights into the relationships between variables. These findings suggest that Kastina and Gusa are significant predictors, contributing to the variation in the response variable. The results provide valuable insights for engineering applications, highlighting the importance of considering these variables in predictive models. Keywords: Hydrological data, Statistical analysis, Predictive modeling, Hydrological patterns, and Regression analysis