Dynamic association of slope movements in the Uttarakhand Himalaya: a critical review on the landslide susceptibility assessment

Figures & data

Table 1. A basic approach for susceptibility modeling.

Approaches	Qualitative (heuristic on knowledge-driven) approach	It is Subjective, the user generates heuristic determinations of susceptibility and characterizes the ranges of susceptibility using descriptive (qualitative) terms. Representation ability depends on an expert/investigator, interpreting the map for actual and potential slope un-isostatic conditions. Experts assign ranks and weights to the known un-isostatic components based on assumptions or expected importance.
	Quantitative (data-driven) approach	It works based on Numerical estimates. The functional linkages are built between known or inferred un-isostatic factors. It requires a Training set as considers the temporal and present distribution of landslides.

Table 2. Classification of basic approaches used in landslide susceptibility assessment/modelling.

Approach	Types			Classification	References
Qualitative (heuristic on knowledge-driven) approach	Direct method (inventory- based approaches)			Field geomorphological and Morpho-dynamic analysis	Numada et al. 2003; Bai et al. 2019
				Distribution analysis	Chang et al. 2016; Roşian et al. 2016
Alternative-semi quantitative approach	Indirect method (index- based method)			Analytic hierarchy process (AHP)	Lee et al. 2004; Yalcin et al. 2011; Pourghasemi et al. 2012; Park et al. 2013; Kumar and Anbalagan 2016; Saha et al. 2020; Sur et al. 2020;  Feizizadeh et al. 2013
				Fuzzy-set based analysis	Champati Ray et al. 2007; Kanungo et al. 2009; Feizizadeh 2013
				Ordered weighted-averaging function (OWA)	Feizizadeh et al. 2014
				Weighted linear combination	Feizizadeh and Blaschke 2012
Quantitative approach (data-driven)	Artificial intelligence (AҐ) and machine learning (ML)			Artificial neural networks (ANNs)	Arora et al. 2004; Yesilnacar and Topal 2005; Chauhan et al. 2010; Pradhan et al. 2010; Yilmaz 2010; Park et al. 2013; Pradhan and Lee 2010; Wang et al. 2019; Chen et al. 2019
				Support-vector machine (SVM)	Yilmaz 2010; Pradhan 2013; Goetz, Brenning, et al. 2015; Tien Bui et al. 2016; Chen et al. 2017; Zhang et al. 2018; Chang et al. 2019; Wang et al. 2019
				Random forest (RF)	Trigila et al. 2015; Pourghasemi and Kerle 2016; Youssef et al. 2016; Ada and San 2018; Chang et al. 2019; Tang et al. 2020; Hong et al. 2018
				Decision tree (DT)	Pal and Mather 2003; Nefeslioglu et al. 2010; Akgun 2012; Tien Bui et al. 2012; Pradhan 2013; Pham et al. 2016
				AdaBoost (AB)	Micheletti et al. 2014
				Classification and regression trees (CART)	Vorpahl et al. 2012; Felicísimo et al. 2013
				Maximum entropy (MaxEnt)	Felicísimo et al. 2013; Park 2015; Kornejady et al. 2017; Hong et al. 2018
				Fuzzy logic	Chung and Fabbri 2003; Park 2004; Champati Ray et al. 2007; Vahidnia et al. 2010; Oh and Pradhan 2011; Sezer et al. 2011; Pourghasemi et al. 2012; Guettouche 2013; Sharma et al. 2013; Pourghasemi and Rahmati 2018
				Naive Bayes	Lee 2004; Bhargavi and Jyothi 2009; Tien Bui et al. 2012; Pham, Tien Bui, Pourghasemi, et al. 2015; Pham, Tien Bui, Prakash, et al. 2015
				Boosted regression trees	Dickson and Perry 2016; Youssef et al. 2016; Binh Thai et al. 2017
				Adaptive neuro-fuzzy inference system (ANFIS)	Bui et al. 2012; Aghdam et al. 2016; Polykretis et al. 2019; Choi et al. 2011
	Statistical (LSM) technique	Bi-variate		Frequency analysis	Akgun et al. 2008; Vijith and Madhu 2008; Yilmaz and Keskin 2009; Pradhan and Youssef 2010; Pradhan and Lee 2010; Pradhan et al. 2012; Kayastha 2015
				Information value model	Van Westen 1997; Saha et al. 2005; Chen et al. 2016; Juliev et al. 2019
				Weights of evidence model	Lee and Choi 2004; Kayastha et al. 2012; Regmi et al. 2014; Goetz, Cabrera, et al. 2015; Nsengiyumva et al. 2019
				Certainty factor	Kanungo et al. 2011; Devkota et al. 2013; Fan et al. 2017; Chen et al. 2018; Liu and Miao 2018
				Statistical index method (SIN)	Van Westen 1997; Pradhan et al. 2012; Mandal and Mandal 2018; Rai et al. 2022;  Ram et al. 2020
				Landslide susceptibility analysis (LSA)	Suzen and Doyuran 2004
				Dempster-Shafer method	Milaghardan et al. 2016; Mustafa et al. 2017; Feizizadeh et al. 2014
				Index of entropy (IOE) model	Mihaela et al. 2011; Althuwaynee et al. 2012; Devkota et al. 2013
		Multi-variate	Regression model (analysis)	Multi logistic regression	Akgun 2012; Devkota et al. 2013; Ozdemir and Altural 2013; Solaimani et al. 2013; Althuwaynee et al. 2014a; Pradhan 2010
				Multivariate adaptive regression splines (MARS)	Vorpahl et al. 2012; Felicísimo et al. 2013; Conoscenti et al. 2015
				Multivariate regression	Akgün and Türk 2011; Guzzetti et al. 2006
			Linear model (discriminant analysis)	Evidential belief function	Althuwaynee et al. 2014b; Vakhshoori and Zare 2016; Chen et al. 2017; Pradhan and Kim 2017
				General linear model (GLM)	Ayalew et al. 2004; Brenning 2005; Vorpahl et al. 2012; Youssef et al. 2016
				Generalized additive model (GAM)	Brenning 2008; Goetz et al. 2011; Vorpahl et al. 2012
				Quadratic discriminant analysis (QDA)	Rossi et al. 2010; Steorts 2014; Pourghasemi and Rahmati 2018
	Deterministic (geo-technical engineering)/probabilistic analysis or Finite element Method	Static methods		PLAXIS	Salimah and Hasan 2020
				Monte Carlo simulation	Liu 2008; Marine and mattos 2020
		Dynamic method			McDougall and Hungr 2005; Hungr and McDougall 2009
		Deterministic (slope stability model)		DSLAM	Wu and Sidle 1997
				SHETRAN	Parkin 1995; Ewen et al. 2000; Norouzi Banis et al. 2004; Birkinshaw et al. 2010
				LISA model	Hammond et al. 1992
				SHALSTAB	Montgomery and Dietrich 1994
				SINMAP	Pack et al. 1998, 2001

Figure 1. Flowchart of the systematic review for the study.

Table 3. Classification of the failure associated with the composition.

Movement type	Bedrock/clast predominantly (coarse size)/clast (fine size)
Fall	Rock/debris/earth-fall
Topple	Rock/debris/earth-topple
Slide	Rock/debris/earth-slide
Spread	Rock/debris/earth-spread
Flow	Rock/debris/earth-flow

Figure 2. Major geomorphological classes in the Uttarakhand Himalaya.

Figure 3. Geological map view of Uttarakhand Himalayan and Himalayan geological stretch (modified after Yin 2005 and GSI 2021).

Figure 4. Major structural division of the UH.

Figure 5. Cumulative rainfall distribution in UH.

Figure 6. (A,B) Representation of precipitation day hour max trend in 95 administrative blocks of Uttarakhand.

Figure 7. (A) ERA-5 modelled based precipitation observed for the administrative block-Champawat (ID-86), (B) extreme precipitation events in 95 administrative blocks for the study period, and (C) landslide distribution in the region with their vulnerability.

Figure 8. Seismic zonation, archival seismic density map with the structural framework of Uttarakhand Himalaya.

Table 4. Slope class distribution concerning the seismic zone and archives of seismic and landslide with rock type of the region.

Slope (in degree)	Slope percent in the seismic zone. (approx.)	Rock type	Seismic history (earthquakes along the slope classes)	Landslides along the slope classes	Slope failure vulnerability of the region
Zone IV
0–15	77.31	Ph, St, Sl, Qtz, Sle, Lst, Lcgrt	11	221	Low
16–30	53.57	Gnis, Ph, St, Sl, Qtz, Sle, Lst, Lcgrt	30	1292	Moderate
31–45	46.16	Ph, St, Sl, Qtz, Sle, Lst, Lcgrt, Gnis, grt, Sdt., Lst, Dlmt, DnGrvl, Uncslt st	33	1528	High
46–60	28.05	Ph, St, Sl, Qtz, Sle, Lst, Lcgrt, Gnis,grt, Sdt., Lst, DnGrvl, Uncslt st	04	240	Moderately high
61–75	14.70	Ph, St, Sl, Qtz, Sle, Lst, Lcgrt, Dlmt DnGrvl, Clmrt, Uncslt st, Amblt	–	06	Very high
76–90	5.02	Ph, Dlmt, Gnis, Qtz, Lst, St, Amblt, Grt	–	–	Extremely high
Zone V
0–15	22.67	Qtz, Ph, St, Sl, Sle, Clmrt, Gnis, Lst, Dlmt, Lcgrt, Snst	18	112	Low
16–30	46.42	Qtz, Ph, St, Sl, Lst, Clmrt, Gnis, Grt, Amblt, Migt, Dlmt, Dlmt, Lcgrt	63	731	Moderate
31–45	53.83	Qtz, Ph, St, Sl, Lst, Clmrt, Gnis, Lcgrt, Grt, Amblt, Migt, Dlmt, Dlmt	53	906	High
46–60	71.95	Qtz, Ph, St, Sl, Lst, Clmrt, Gnis, Lcgrt, Grt, Amblt, Migt, Dlmt	20	175	Moderately high
61–75	85.30	Qtz, Ph, St, Sl, Lst, Dlmt, Gnis, Migt Amblt, Lcgrt, Grt, Dlmt	03	10	Very high
76–90	94.98	Qtz, Ph, St, Sl, Lst, Gnis, Lcgrt, Grt, Amblt, Migt	–	–	Extremely high
The realized landslide slope values in the literature
Author	A	B	C	D	E	F	G
Authors (1)	0–12	13–25	26–38	39–51	52–64	65–77	>77
Authors (2)	0–15	15–30	30–45	45–60	60–75	75–90
Authors (3)	<15	16–25	26–35	36–45	46–65	>65
Authors (4)	0–15	15–25	26–35	35–50	>50
Authors (5)	<15	16–25	26–35	36–45	>46
Authors (6)	0–8.5	8.5–35	>35

Note: Broad-defined metamorphic/meta-sedimentary rocks—Gnis: Gneisses; Ph: Phyllite; St: Schist; Sl: Slate; Qtz: Quartzite; Sle: Shale; Lcgrt: Leucogranite; grt: Granite; Migt: Migmetites; Sedimentary rocks—Snst: Sandstone; Lst: Limestone, Claystone; Clmrt: Conglomerate and Mudstone; DnGrvl: Doon Gravels; Uncslt st.: Unconsolidated Sediment; Dlmt: Dolomite; Amblt: Amphibolite.

(1) (Gupta and Satyam 2022); (2) (Batar and Watanabe 2021); (3) Kainthura and Sharma 2022); (4) (Bhattacharyya et al. 2020); (5) (Tiwari et al. 2020); (6) (Jain et al. 2022).

Table 5. Characteristics of slope failure.

Different characteristics of landslides and rapid slope failures according to Watari (1986)
	Landslide	Rapid slope failures
Geology (structure and lithology)	Frequently affected by geology and geologic structures	Geology and geologic structure have little to no influence.
Soils (top-soil and regolith	Movement take places along slip surfaces (consists of highly plastic clay)	Generally, participates topsoil, residual soil, and regolith.
Topographic slope	Occur on gentle to moderate slopes (5–45°) The upper slopes supports flat-plateau-like topography	Usually occurs on slopes steeper than 30°
Nature of act	Continual; recurring (repetitive occurrence) The average episode lasts a long time.	Occur quickly and unexpectedly
Movement rate	Typically very slow to slow (0.01–10 mm/day) most common	Exceptionally quick 10 m/s or quicker
Moving mass nature	Hardly any movement within a sliding. The block frequently shifts while retaining the original shape and characteristics.	Erratic movement as a very disturbed mass
Mechanism for triggering	Elevated groundwater table and excess groundwater are the main factors.	Likely influenced by rainfall intensity
Size of slope failure	Surface area is often large (1–100 ha).	surface area is generally small (an average volume of about 440 m^3)
Precursors or surface warning signs	Before sliding, surfaces frequently develop cracks, depressions, upheavals, groundwater fluctuations, etc.	A sudden failure, always occurs without warning
Gradient support to slope failure (in degree)	10–25	35–65
	Classification of slope morphometry defining the terrain
Morphometry-type as slope	Slope categories (in degree)	Probable type of failure (Table 2)
Escarpment or cliff	GT 65	Falls and topples
Very steep	46–65	Falls and topples
Steep	36–45	Slides
Moderately steep	26–35	Slides
Gentle	16–25	Slides with creep movement
Very gentle	LT 15	Slides with creep movement
Landslide movement scale (Varnes 1978)
	Movement type and rate						Mass velocity (mm/s)	Typical velocity
	Falls	Topples	Slides	spread	Flows	Complex type
	Extremely rapid
	Very rapid						5*10^3	5 m/s
	Rapid						5*10^1	3 m/min
	Moderate						5*10^−1	1.8 m/h
	Slow						5*10^−3	13 m/month
	Very slow						5*10^−5	1.6 m/year
	Extremely slow						5*10^−7	16 mm/year

Their classification, and rate of movement as per literatures.

Figure 9. Factor of safety of the region representing slope failure condition.

Figure 10. (A) Technique and data source used widely in LSM/LSA inventory as reviewed and (B) space-time distribution of slope failure in the region.

Figure 11. Causative factor used for the LSM/LSA and used by the articles acquired from the records.

Figure 12. Globally open access/paid datasets for the natural hazard application (LSM/LSZ) model/approach.

Figure 13. Summary of the mapping units in LSM/LSA.

Figure 14. (A) Validation methods of LSM/LSA. (B) Radar diagram of articles published by the type of institution (C) for different applications, and (D) Processing technique on data collection.

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Data availability statement

All materials used for the review article compilation are properly cited. The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.