Arabian Journal of Geosciences (2021) 14:838 https://doi.org/10.1007/s12517-021-07156-6 ORIGINAL PAPER Landslide susceptibility investigation for Idukki district of Kerala using regression analysis and machine learning Sheelu Jones1 & A. K. Kasthurba1 & Anjana Bhagyanathan1 & B. V. Binoy1 Received: 15 January 2021 /Accepted: 23 April 2021 # Saudi Society for Geosciences 2021 Abstract Kerala is the third most densely populated state in India, with 860 persons per square kilometer. The uniqueness and diversity of the state’s topology make it highly vulnerable to natural hazards. Kerala State Emergency Operations Centre Kerala State Disaster Management Authority (2016). This study was initiated in the backdrop of landslides and floods in 2018, which had wreaked havoc in the region. Among the 4728 landslides reported in the state’s ten districts, Idukki was the worst affected with 2219 landslide occurrences. A statistically significant cluster of landslide hotspots was identified within the Idukki district using Getis-Ord Gi* statistics. Landslide susceptibility analysis was carried out using logistic regression (LR) and artificial neural network (ANN). Natural parameters influencing landslides such as slope, elevation, rainfall, geology, distance to drainage, and anthropogenic conditioning factors such as land use, road density, and quarry density were considered in this study. The results indicate that both natural and anthropogenic conditioning factors have a significant influence on landslide occurrences. According to the LR results, about 37.87% and 38.07% of the district’s total area is situated in high and medium landslide susceptibility zones. The results establish that ANN has better predictive performance compared with LR. Keywords Hotspot analysis . Landslide conditioning parameters . Logistic regression . Artificial neural network Introduction (lithology and morphology), triggering factors (intense rainfall, earthquake, etc.), and accelerating factors (landscape modifica- Landslides are mass movements of rock, debris, or earth down tions) that result in landslide occurrences (Santini et al. 2009). the slope under the influence of gravity, resulting in a geomor- These factors along with high population density pose an in- phic change of the earth’s surface (Cruden et al. 1996; Guzzetti creased risk of vulnerable situations in developing countries et al. 2005;Phametal.2019; Van Thom et al. 2016). Every like India (Kritikos and Davies 2015). These unpredictable year, hundreds of people worldwide lose their lives in land- and massive natural hazards result in property damages incur- slides (Yalcin et al. 2011). The hydro-geomorphic processes ring financial losses in many parts of the world, impacting local are developed by the combination of predisposing factors and global economy (Suzen and Doyuran 2004; Chang and Chiang 2009; Cogan and Gratchev 2019; Lee et al. 2012; 2 Responsible Editor: Biswajeet Pradhan Nourani et al. 2014). About 15% (~ 0.42 million km )of India’s total landmass falls under the landslide-prone hazardous * Sheelu Jones zone (Kanungo and Sharma 2014; Harilal et al. 2019;National [email protected] Disaster Management Authority 2019; Thennavan and Pattukandan Ganapathy 2020). Rao (1993) identified four A. K. Kasthurba landslide-prone regions in India as Western Himalayas (Uttar [email protected] Pradesh, Himachal Pradesh, and Jammu and Kashmir), Eastern Anjana Bhagyanathan and North Eastern Himalayas and plateau margins (West [email protected] Bengal, Sikkim, and Arunachal Pradesh), Naga-Arakkan B. V. Binoy Mountain belt (Nagaland, Manipur, Mizoram, Tripura), and [email protected] plateau margins of the Western Ghats and some parts of 1 Eastern Ghats (Kerala, Tamil Nadu, Karnataka, and Department of Architecture and Planning, National Institute of Maharashtra) (Rao 1993). Technology Calicut, Kozhikode, Kerala, India 838 Page 2 of 17 Arab J Geosci (2021) 14:838 Kerala is vulnerable to natural disasters due to the shifting approach analyzes the relationship between landslide condi- climatic conditions and diverse topographical characteristics. tioning parameters and past landslide events (Zêzere et al. The state’s location with the Arabian Sea in the west and the 2017). The major assumption made in this approach is that steep slope gradient of the Western Ghats on the eastern side, the conditions which accelerated landslides in the past will also and high population density (860 persons per square kilome- trigger future events (Dapples et al. 2002). The three funda- ter), accelerates the vulnerability to disasters (Shaharban and mental data-driven methodologies commonly employed for Rathnakaran 2019; Prakashkumar 2019). According to the the study are the bivariate statistical method, multivariate Kerala State Disaster Management Plan 2016, Kerala is listed method, and artificial intelligence (AI)-based methods as the multi-hazard-prone state in the country as it has been (Nourani et al. 2014;Phametal.2016;Jose2018;Vineesh identified with 39 hazards Kerala State Emergency Operations 2019; Dao et al. 2020). The bivariate statistical method as- Centre Kerala State Disaster Management Authority (2016). sesses the relationship between each independent variable Landslides are one of the frequently occurring natural hazards and landslide occurrences separately. The multivariate ap- in sloping terrains of the state, especially during monsoons. proach considers the collective association of dependent and All districts except Alappuzha (coastal district) are prone to independent variables for the assessment (Mutlu and Goz landslides (Kuriakose et al. 2009b). The discrepancy in cli- 2020). Logistic regression (LR) is one among the multivariate matic conditions along with high population density and land- regression methods which explicate the presence or absence of use changes had accelerated the number of landslides within a phenomenon under consideration (Makealoun et al. 2015). the state in recent decades. Therefore, landslide susceptibility This is one of the most reliable and widely used approaches for studies have gained significant importance in the mountainous landslide susceptibility studies (Guzzetti et al. 2005; Lee and regions of the state, with increasing demand for developmen- Pradhan 2007; Rickli and Graf 2009; Yilmaz 2010;Erenerand tal undertakings and hazard mitigation activities. Düzgün 2012). In recent times, AI is widely used in modeling Landslide susceptibility studies elucidate the possibility of intricate phenomena such as landslide susceptibility mapping landslide occurrences of any chosen region in the future (Nourani et al. 2014; Hong et al. 2017; Chen et al. 2018). The (Hemasinghe et al. 2018). It forecasts “where” landslides are artificial neural network (ANN) model is one of the most com- likely to happen within the study area (Guzzetti et al. 2005). monly used AI techniques for landslide susceptibility study Landslide susceptibility studies are conducted with some as- (Zare et al. 2013;Chenetal.2018; Nguyen et al. 2019;Dao sumptions as: (i) the previous landslide events leave visible et al. 2020). ANN uses a computational mechanism for ana- indications that can be recognized, classified, and mapped; (ii) lyzing data to study the relationship between the dependent physical laws govern occurrence, which can be evaluated ex- and independent variables (Arora et al. 2004). perimentally, statistically, or deterministically; (iii) future This study was conducted in the wake of the massive land- landslides would more likely occur in similar conditions of slides and floods in Kerala during the year 2018. This flood past events; and (iv) the frequency of spatial landslides can garnered the attention of the research community and can be be inferred from exploratory inquiries that are computed from entitled as the worst flood in this century (Mishra et al. 2018). environmental information or physical models (Guzzetti et al. According to the Kerala State Disaster Management Authority 1999b, 2012; Reichenbach et al. 2018). Landslide susceptibil- (KSDMA), 46.93% of landslides reported in Kerala during ity studies can be classified into two methods such as (i) qual- 2018 had occurred in the Idukki district. Therefore special itative and (ii) quantitative (Erener and Düzgün 2012; attention is required to examine and forecast landslides in Kayastha et al. 2013; Nourani et al. 2014;Chaeetal.2017). Idukki due to the sensitive terrain and intensified landscape Qualitative methods are knowledge-driven and purely based modifications (Abraham et al. 2021). Initiating these types of on the opinion or judgment of experts (Van Westen et al. studies helps authorities to make necessary initiatives in con- 1999; Wahono 2010). In this method, the relationship be- nection with landslide hazard zoning and thus controlling de- tween landslide susceptibility and landslide conditioning pa- velopment activities. The initial phase of the study identifies rameters is obtained by assigning weightage to the factors landslide hotspots throughout the district. In the later part of based on expert opinions (Kaur et al. 2018). In recent times, the study, landslide susceptibility modeling was done using the traditional qualitative methods are replaced by advanced two different data-driven methods, LR and ANN (Guzzetti quantitative methods (Chae et al. 2017; Tien Bui et al. 2017). et al. 2005; Lee and Pradhan 2007; Rickli and Graf 2009; Quantitative methods most commonly used for landslide stud- Yilmaz 2010; Erener