# 2-D Cross-Plot Model Analysis Using Integrated Geophysical Methods for Landslides Assessment
## 4 Discussion
Generally, the profile shows very loose-loose material at a depth range of 3-10 m. This shows the soil profile was considered as highly weathered with the presence of the boulder. Figure 9: _Cont._  
Figure 9: Correlated results of geophysical and borehole investigation: **(a)** line HR1 and HS1 with BH10; **(b)** line HR1 and HS2 with BH9; **(c)** schematic geological profile based on the \(N\)-value of Standard Penetration Test (SPT) distribution from borehole of BH7, BH8, BH9, and BH10. The correlation of overview hydrogeological parameter and landslide initiation was presented using available rainfall data distribution based on the nearest rainfall station. As mentioned before, rainfall distribution in Malaysia is mainly influenced by two monsoons seasons, namely southeast and northeast monsoons, from May-September and November-March. However, the highest precipitation occurs during the transition period between the monsoon seasons, which are known as inter-monsoon of March-April and October-November, as shown in Figure 10a. The inter-monsoon seasons show the highest domination of rainfall distribution of 361-242 mm and 273.2-267 mm for Mach-April and October-November, respectively. This prolonged rainfall shows as a factor in building up to the mechanism of landslides. However, in a rainfall-induced mechanism, it is widely debated regarding the antecedent rainfall, referring to the rainfall in the days immediately preceding landslide events, while triggering rainfall represented as the rain that falls in a day of landslide occurrence [79; 80]. Previous researcher has well accepted the effects of antecedent rainfall on slope instability; however, the duration needed for cumulative rainfall needs to be considered [81]. In this study, 15-days rainfall data were used, suggested by [PERSON] et al., as antecedent rainfall for shallow landslide [82]. Figure 10b shows the overview of rainfall distribution for three months before the landslide event. The bar chart shows the rainfall amount upsurge trend from September to October and gradually increases from October to November. Based on the chart given, the rainfall is intense in October-November with a rainfall amount of 30-90 mm per day. This shows this area exhibits daily rainfall and leads to the progressive weathering of the soil. The cumulative rainfall data (Figure 10c,d) for 30-days were analyzed for October-November, as the graph trends show, and drastically increased in November. The antecedent rainfall (Figure 10d) for 15-days before the landslide events show an increasing value of 100 mm to 267 mm. The intense rainfall shows high precipitation within 15 days and is indicative of initiating the landslide. The variation of weak zones indicated by the geophysical results show the presence of subsurface materials, which exhibit the additional weathering process, which tends to  
Figure 10: Rainfall distribution for Hulu Yam landslide: (**a**) overview rainfall distribution throughout the year; (**b**) daily rainfall distribution within three months (September–November); (**c**) 30 days of cumulative rainfall for October; (**d**) 30 days of cumulative rainfall for November. produce poor rock mass quality. The extreme weather climate leads to the progressive weathering processes due to the abundance of rainfall and moistures as accelerating the transformations of homogenous subsurface materials in heterogeneous conditions [83]. This process allows the water to flow and weaken the subsurface geo-materials according to the current topography of surface direction. The intense precipitation will contribute to surface runoff, as the increasing groundwater level in saturated conditions, as well as increasing soil mass become the additional trigger factors for failure. The intense and continuous precipitation causes the pore pressure to increase as the groundwater rises rapidly. The continuous process reduces the shearing resistance of geo-materials and contributes to slope failure.