Introduction
Sinkholes are a significant geohazard, particularly in urban areas like Kuala Lumpur and Ampang Jaya, where the increasing number of incidents has raised concerns over public safety and urban infrastructure. Since 1968, the Klang Valley region has witnessed a growing frequency of sinkholes, posing serious threats to human lives, assets, and structures, particularly in Malaysia’s bustling capital. To address this issue, Rosdi et al. (2017) conducted a study that employed Geographic Information Systems (GIS) integrated with the Analytical Hierarchical Process (AHP) to develop a Sinkhole Hazard Model (SHM). This article discusses the findings of this study, the methods used, and the potential for future research in this critical area of disaster management.
Sinkhole Susceptibility Hazard Zonation
The SHM developed by Rosdi et al. (2017) categorizes the study area into five zones of sinkhole susceptibility: very low, low, moderate, high, and very high hazard. These classifications are based on a combination of five key criteria: Lithology (LT), Groundwater Level Decline (WLD), Soil Type (ST), Land Use (LU), and Proximity to Groundwater Wells (PG). By assigning relative weights to each of these factors through expert judgment and a pairwise comparison matrix, the study produced susceptibility maps that highlight areas at greatest risk.
The results, depicted in the sinkhole susceptibility hazard zonation maps, show that 31% of the study area falls within the high hazard zone, while 10% is classified as very high hazard. These high-risk zones are predominantly located in the North West part of Kuala Lumpur, an area characterized by Kuala Lumpur Limestone Formation bedrock geology, consisting mainly of limestone/marble and acid intrusive lithology. This geological setting, combined with high levels of groundwater level decline, makes these areas particularly prone to sinkhole development.
GIS and AHP Integration
The integration of GIS and AHP in this study allowed for a systematic and spatially explicit assessment of the factors contributing to sinkhole formation. AHP, in particular, facilitated the weighting of different factors, enabling the researchers to rank the susceptibility of different areas accurately. The susceptibility maps generated from this model provide valuable insights into the spatial distribution of sinkhole hazards, helping urban planners and decision-makers prioritize areas for monitoring and mitigation efforts.
Validation and Model Accuracy
Rosdi et al. (2017) validated their model using a dataset of 33 previous sinkhole events. The validation results were promising, with 64% of the sinkhole events falling within the high hazard zones and 21% within the very high hazard zones. This strong correlation between the model’s predictions and actual sinkhole occurrences demonstrates the effectiveness of the AHP approach in predicting sinkhole hazards.
Limitations and Future Research
Despite the success of the SHM, the study acknowledges several limitations and suggests avenues for future research. One key limitation is the reliance on the AHP technique, which, while effective, may not capture the full complexity of the factors influencing sinkhole formation. The study recommends exploring alternative multi-criteria decision-making techniques, such as Fuzzy AHP, Weight of Evidence (WoE), and other methods that could potentially improve the accuracy of sinkhole susceptibility models.
Another limitation is related to data acquisition, particularly regarding geological and hydrological data. The study suggests that high-resolution satellite imagery could be used to update land use and land cover data, providing a more accurate and timely assessment of sinkhole risk. Additionally, the study highlights the importance of understanding the triggering effects of sinkholes, such as heavy rainfall and excessive groundwater extraction, which could be incorporated into future models.
Finally, the study recommends the computation of the magnitude and frequency relationship of sinkholes as a valuable technique for predicting the likelihood of future sinkhole occurrences. By analyzing the size and frequency of past sinkholes, researchers could better estimate the risk of future events, providing a more comprehensive tool for risk assessment and urban planning.
Conclusion
The study by Rosdi et al. (2017) represents a significant contribution to the understanding of sinkhole susceptibility in Kuala Lumpur and Ampang Jaya. The integration of GIS and AHP allowed for a detailed and spatially explicit analysis of the factors contributing to sinkhole formation, resulting in highly accurate susceptibility maps. However, the study also highlights the need for further research to refine these models and improve the accuracy of sinkhole risk assessments. By exploring alternative techniques and addressing the limitations identified, future studies could provide even more reliable tools for predicting and mitigating sinkhole hazards in urban areas. This ongoing research is crucial for safeguarding urban infrastructure and protecting the lives of those living in sinkhole-prone regions.
References
Rosdi, M. A. H. M., Othman, A. N., Zubir, M. A. M., Latif, Z. A., & Yusoff, Z. M. (2017). Sinkhole susceptibility hazard zones using GIS and analytical hierarchical process (AHP): A case study of Kuala Lumpur and Ampang Jaya. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W5, 145–151. https://doi.org/10.5194/isprs-archives-XLII-4-W5-145-2017