This study targeted 13 geological regions in four areas that experienced landslides due to heavy rains between 2014 and 2019. LiDAR topography data before and after the disasters were used to estimate the differences in the elevation values of the source areas of the landslides so that the landslide volumes could be estimated. Next, the α and γ parameters of an equation used to estimate the landslide volume were calculated and modeled for each region. The parameters were incorporated into equation (1), which is the functional form of Simonett’s equation (Simonett, 1967), to estimate the landslide volume. The aim of this study was to improve the formula itself and clarify its compatibility with actual measured values. When the relationship between the α value, which corresponds to the intercept of V=αAγ, and values obtained for standardized cumulative rainfall, we found that the R2 value was the highest for the 48‐hour cumulative rainfall at 0.518. There was a strong negative correlation between the γ and α values, with an R2 value of 0.844;the γ value can be estimated from the α value. After examining the factors that statistically affect the α value, the standardized 48‐hour cumulative rainfall (R2=0.518) and slope (R2=0.309) were selected. Since both of these two factors could be approximated by linear equations, we modeled the α value using a basic general linear model (GLM) and obtained α”=0.686－1.747×1+0.013×2″ (R2=0.53, p=0.02, x1 is the standardized48‐hour cumulative rainfall, and x2 is the slope). We improved the formula for estimating the landslide volume using three factors: cumulative landslide area and rainfall, slope. When we investigated the applicability of the improved estimation formula and found that the calculated values were concentrated in a fairly close range on the 1:1 line for all geology types. When the actual cumulative values of landslide volume, they was 101.7 % for granites and 96.0 % for rhyolites. It is therefore considered that the improved estimation formula could be applied to the geological features included in the model dataset (such as granites and rhyolites).