Abstract
The hill road between Kodaikkanal and Palani which serves as major connecting link between the two places is affected by landslides every year during monsoons and intense summer showers. This causes a severe impediment to the growth and development of the hill town, leading to social and economic impacts on the community. Therefore, it is mandatory to formulate a preparedness plan to combat the impacts of slope failure along this stretch. The role of ICT enabled technologies play a vital role in connecting the various segments of the preparedness plan. In line with this need, this paper attempts to identify zones susceptibility to landslides along the selected stretch using a bivariate statistical model frequency ratio, map the spatial variability of susceptibility to landslides using Geographic Information System (GIS) and formulate a preparedness plan to minimize the social and economic impacts of landslides on the community. The preparedness plan links various levels of administration and the hill community, training them in hazard response and mitigation using various ICT based technologies for effective communication during response and rescue operations. Planning includes the identification of risks involved in the event of hazard, development of early warning systems and identifying the specific needs of the community for protection from risk, resource identification, awareness of the government and non-government agencies involved in the action plan, detailed chain of command within and outside each organization to co-ordinate relief activities.
Similar content being viewed by others
References
Girma F, Raghuvanshi TK, Ayenew T, Hailemariam T (2015) Landslide hazard zonation in Ada Berga district, Central Ethiopia—a GIS based statistical approach. J Geom 9(I):25–38
Hamza T, Raghuvanshi TK (2017) GIS based landslide hazard evaluation and zonation—a case from Jeldu District, Central Ethiopia. J King Saud Univ Sci 29(2):151–165
Mengistu F, Suryabhagavan KV, Raghuvanshi TK, Lewi E (2019) Landslide hazard zonation and slope instability assessment using optical and InSAR data: a case study from Gidole town and its surrounding areas, Southern Ethiopia. Rem Sens Land 3(1):1–14
Chimidi G, Raghuvanshi TK, Suryabhagavan KV (2017) Landslide hazard evaluation and zonation in and around Gimbi town, Western Ethiopia—a GIS-based statistical approach. Appl Geomat 9(4):219–236
Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81:1–11
Steger S, Brenning A, Bell R, Glade T (2016) The propagation of inventory-based positional errors into statistical landslide susceptibility models. Nat Hazards Earth Syst Sci 16(12):2729–2745
Ilia I, Tsangaratos P (2016) Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map. Landslides 13:379–397
Shano L, Raghuvanshi TK, Meten M (2020) Landslide susceptibility evaluation and hazard zonation techniques—a review. Geoenviron Disasters 7:18
Mohan P, Mittal H (2020) Review of ICT usage in disaster management. Int J Inf Tecnol 12:955–962
Siddique AA, Qadri MT (2018) Wireless sensor network (WSN) based flood warning system. Int J Inf Tecnol 12:567–570
Mohammadian M, Yamin M (2017) Intelligent decision making and analysis using fuzzy cognitive maps for disaster recovery planning. Int J Inf Tecnol 9:225–238
Dai FC, Lee FC (2002) Landslide characteristics and slope instability modelling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–238
Kondratyev KY, Krapivin VF, Varotsos CA (2006) Natural disasters as interactive components of global ecodynamics. Environmental sciences. Springer, Berlin, pp 437–478
Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41
Hürlimann M, Rickenmann D, Medina V, Bateman A (2008) Evaluation of approaches to calculate debris-flow parameters for hazard assessment. Eng Geol 102:152–163
Shieh CL, Chen YS, Tsai YJ, Wu JH (2009) Variability in rainfall threshold for debris flow after the Chi-Chi earthquake in Central Taiwan, China. Int J Sediment Res 24(2):177–188
Luna QB, Blahut J, van Westen CJ, Sterlacchini S, van Asch TWJ, Akbas SO (2011) The application of numerical debris flow modelling for the generation of physical vulnerability curves. Nat Hazards Earth Syst Sci 11:2047–2060
Liang W, Zhuang D, Jiang D, Pan P, Ren H (2012) Assessment of debris flow hazards using a Bayesian Network. Geomorphology 171–172:94–100
Okanao K, Suwa H, Kanno T (2012) Characterization of debris flows by rainstorm condition at a torrent on the Mount Yakedake volcano, Japan. Geomorphology 136(1):88–94
Elkadiri R, Sultan M, Youssef AM, Elbayoumi T, Chase R, Bulkhi AB, Al-Katheeri MM (2014) A remote sensing–based approach for debris-flow susceptibility assessment using artificial neural networks and logistic regression modeling. IEEE J Sel Top Appl Earth Observ Rem Sens 7(12):4818–4835
Chung CF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogramm Eng Rem Sens 65:1389–1399
Acknowledgements
This study was supported by DST-SERB under fast track scheme (SR/FTP/ETA-0062/2011). The authors would like to acknowledge with thanks, the financial support rendered by DST for the research.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sujatha, E.R. A hazard preparedness plan for a selected stretch of hill road between Kodaikkanal and Palani. Int. j. inf. tecnol. 13, 751–757 (2021). https://doi.org/10.1007/s41870-020-00580-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41870-020-00580-z