E-ISSN:2250-0758
P-ISSN:2394-6962

Research Article

Landslides

International Journal of Engineering and Management Research

2025 Volume 15 Number 1 February
Publisherwww.vandanapublications.com

GIS-Based Landslide Mapping and Analysis using QGIS: A Study in Palakkad, Kerala

Mathew BV1, Jayakrishnan R2*, Chandran A3, Pranav A4, Rishikesh VS5
DOI:10.5281/zenodo.15070403

1 B V Mathew, Professor, HoD, Department of Civil Engineering, APJ Abdul Kalam Technological University/Ahalia School of Engineering and Technology, Palakkad, Kerala, India.

2* Jayakrishnan R, Assistant Professor, Department of Civil Engineering, Apj Abdul Kalam Technological University/Ahalia School of Engineering and Technology, Palakkad, Kerala, India.

3 Anagha Chandran, B.Tech Final Year Student, Department of Civil Engineering, APJ Abdul Kalam Technological University/Ahalia School of Engineering and Technology, Palakkad, Kerala, India.

4 Pranav A, B.Tech Final Year Student, Department of Civil Engineering, APJ Abdul Kalam Technological University/Ahalia School of Engineering and Technology, Palakkad, Kerala, India.

5 V S Rishikesh, B.Tech Final Year Student, Department of Civil Engineering, APJ Abdul Kalam Technological University/Ahalia School of Engineering and Technology, Palakkad, Kerala, India.

Landslides are a major natural hazard in hilly regions, posturing critical dangers to life, infrastructure, and the environment. This study centers on landslide vulnerability mapping in Veezhumala, Palakkad, utilizing QGIS as the essential apparatus for geospatial investigation. Different conditioning components such as slope, elevation, aspect, soil type, land use, and rainfall patterns have been considered to evaluate landslide-prone regions.
Information collection included getting Digital Elevation Models (DEMs), meteorological rainfall data, and chronicled landslide events. Thematic maps for each calculate were generated in QGIS to establish their spatial dispersion and impact on landslide vulnerability. The another stage of the ponder will include applying the Evidence-Based Frequency (EBF) Method, which can assign probability weights to each calculate based on its relationship with past landslides. This will empower the creation of a landslide vulnerability index, categorizing the think about region into distinctive chance zones.
The discoveries of this study will contribute to disaster readiness, urban planning, and natural administration by recognizing high-risk zones and suggesting moderation measures. The ultimate vulnerability outline will serve as a important instrument for policymakers, engineers, and nearby specialists in landslide risk evaluation and administration methodologies.

Keywords: GIS, QGIS, Landslide, Mapping

Corresponding Author How to Cite this Article To Browse
Jayakrishnan R, Assistant Professor, Department of Civil Engineering, Apj Abdul Kalam Technological University/Ahalia School of Engineering and Technology, Palakkad, Kerala, India.
Email: 		Mathew BV, Jayakrishnan R, Chandran A, Pranav A, Rishikesh VS, GIS-Based Landslide Mapping and Analysis using QGIS: A Study in Palakkad, Kerala. int. j. eng. mgmt. res.. 2025;15(1):142-150.
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https://ijemr.vandanapublications.com/index.php/j/article/view/1710

Manuscript Received Review Round 1 Review Round 2 Review Round 3 Accepted
2024-12-30 2025-01-15 2025-02-08
Conflict of Interest Funding Ethical Approval Plagiarism X-checker Note
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© 2025 by Mathew BV, Jayakrishnan R, Chandran A, Pranav A, Rishikesh VS and Published by Vandana Publications. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/ unported [CC BY 4.0].

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Download PDFBack To Article1. Introduction2. Objectives3. Literature Review4. Areas and Methods5. Our Works6. ConclusionReferences
Mathew BV, et al. GIS-Based Landslide Mapping and Analysis

1. Introduction

Avalanches are among the foremost dangerous common fiascos, causing noteworthy socio-economic misfortunes and natural debasement universally, particularly in locales characterized by soak landscape, overwhelming precipitation, and delicate geography. The Western Ghats of India, a UNESCO World Legacy Location and one of the eight “hottest hotspots” of biodiversity within the world, is profoundly helpless to avalanches due to its tough geography, seriously monsoonal precipitation, and anthropogenic intercessions. Kerala, a southern Indian state arranged along the western slants of this mountain run, has ended up increasingly vulnerable to visit and disastrous avalanches, especially within the wake of exceptional precipitation occasions in later a long time. The avalanches in Kerala have not as it were driven to the appalling misfortune of lives but too seriously disturbed employments, foundation, and biological systems, highlighting the pressing require for comprehensive landslide susceptibility mapping and hazard appraisal.

The event of avalanches within the Western Ghats is affected by a complex transaction of characteristic and anthropogenic variables. Drawn out and strongly precipitation, ordinary of the storm seasons, acts as the essential trigger by soaking soil layers, decreasing shear quality, and expanding pore water weight. Geographical components such as lithology, geomorphology, and slant steadiness advance compound the powerlessness of the locale. Human exercises, counting deforestation, spontaneous road construction, quarrying, and changes in arrive utilize designs, have heightens the hazard by aggravating the normal harmony of the scene. Kerala's tall populace thickness and the development of settlements into environmentally delicate zones have encourage compounded the challenge, requiring proactive and experimentally strong measures to relieve avalanche dangers.

Avalanche helplessness mapping (LSM) serves as a basic instrument in understanding and overseeing avalanche risks. By distinguishing and portraying ranges inclined to avalanches, LSM empowers policymakers, organizers, and calamity administration specialists to execute focused on intercessions pointed at reducing dangers and minimizing harms.

The integration of geospatial advances, such as Geographic Data Frameworks (GIS) and farther detecting, with progressed multi-criteria decision-making (MCDM) models, has revolutionized the exactness and unwavering quality of LSM. Procedures just like the Explanatory Progression Handle (AHP), Fuzzy-AHP, and Expository Network Process (ANP) give efficient systems for measuring the relative impact of different causative components, empowering the era of point by point and approved vulnerability maps.Within the setting of Kerala, where avalanches have ended up an progressively visit wonder, various thinks about have centered on applying these progressed strategies to particular districts inside the state. For occurrence, the Kuttiyadi Waterway Bowl, the Idukki locale, and the good countries of the Western Ghats have been broadly examined due to their tall helplessness to avalanches and the critical socio-economic impacts of such occasions in these ranges. These thinks about have utilized assorted datasets, counting high-resolution disciple symbolism, computerized rise models (DEMs), precipitation records, and geographical maps, to create topical layers speaking to key causative components such as slant, waste thickness, lithology, geomorphology, and arrive use/land cover. The integration of these variables utilizing MCDM models has encouraged the creation of defenselessness maps that classify locales into distinctive risk zones, extending from steady to exceedingly vulnerable. The investigate moreover emphasizes the significance of approving the created defenselessness maps utilizing avalanche stock information and measurable procedures such as Recipient Working Characteristic (ROC) bends. This approval prepare guarantees the unwavering quality and prescient capability of the models, empowering their down to earth application in catastrophe chance diminishment and arrive utilize arranging. Moreover, the discoveries of these considers emphasize the require for a adjusted approach that combines structural measures, such as incline stabilization and waste change, with non-structural measures, counting arrive utilize directions, community mindfulness, and early caution frameworks.

The centrality of this body of investigate lies not as it were in its logical commitments but too in its viable suggestions for maintainable improvement and calamity strength in Kerala.


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By giving noteworthy bits of knowledge into avalanche defenselessness and chance, these thinks about offer a guide for moderating the impacts of avalanches, shielding lives, and protecting the biological astuteness of the Western Ghats. As climate alter proceeds to compound the recurrence and concentrated of extraordinary climate occasions, the advancement of exact and comprehensive LSM systems gets to be progressively basic. These endeavors speak to a imperative step toward improving the versatile capacity of helpless communities and guaranteeing the economical administration of one of India's most environmentally critical districts.

2. Objectives

Landslides are a critical risk within the Western Ghats of Kerala, requiring nitty gritty ponders to distinguish regions at chance and create relief procedures. One essential objective of the inquire about is to methodicallly portray landslide-prone zones, categorizing them into steady, modestly steady, unsteady, and exceedingly unsteady districts. This includes making nitty gritty landslide susceptibility maps (LSMs) to supply a logical premise for catastrophe hazard administration and spatial arranging. Such mapping is basic for districts like Kerala, where the one of a kind geology and overwhelming monsoonal precipitation make conditions conducive to visit avalanches, frequently with destroying results.

Another key objective is to analyze the variables contributing to avalanche events. This incorporates both normal variables, such as slant slope, geomorphology, lithology, seepage thickness, and seriously precipitation, and human-induced variables like deforestation, unregulated street development, quarrying, and land-use changes. By understanding the intelligent and limits of these variables, analysts point to distinguish the essential drivers of incline flimsiness and assess how anthropogenic exercises compound avalanche vulnerability within the biologically touchy Western Ghats.

A critical accentuation is set on utilizing progressed geospatial innovations and decision-making procedures to upgrade the precision of avalanche defenselessness mapping. Geographic Data Frameworks (GIS), inaccessible detecting, and high-resolution toady symbolism are coordinates with Multi-Criteria Decision-Making MCDM) models such

as the Expository Progression Prepare (AHP), Fuzzy-AHP, and Expository Organize Prepare (ANP). These models are utilized to dole out weights to different causative components based on their relative impact on avalanche event, empowering a data-driven and orderly approach to creating vulnerability maps. This prepare guarantees that the maps reflect the real-world elements of landslide-prone locales.

Approval of the created defenselessness maps is another basic objective. The investigate consolidates thorough approval strategies, such as Collector Working Characteristic (ROC) bends, to evaluate the exactness and unwavering quality of the maps. Chronicled avalanche stock information is additionally utilized to confirm the prescient capabilities of the models. This step is fundamental to guarantee that the defenselessness maps can be successfully utilized for anticipating future avalanches and arranging relief measures.

The investigate moreover points to address territorial specificities by centering on high-risk regions such as the Kuttiyadi Waterway Bowl, Idukki locale, and other vulnerable zones inside the Western Ghats. By analyzing extraordinary climate occasions, such as the exceptional precipitation amid the 2018 rainstorm season, and their part in activating avalanches, the considers give experiences into the spatial and worldly changeability of avalanche events. This localized approach empowers the development of custom-made hazard moderation.

A similarly vital objective is to supply significant bits of knowledge for calamity hazard lessening and economical advancement. The inquire about points to educate nearby specialists, organizers, and policymakers approximately the basic and non-structural measures required to relieve avalanche dangers. Basic measures such as incline stabilization, seepage enhancement, and disintegration control are prescribed nearby non-structural approaches like land-use controls, community mindfulness programs, and early caution frameworks. These recommendations are planned to play down the social, financial, and natural impacts of avalanches.

Moreover, the ponders point to address the broader suggestions of climate alter on avalanche elements. With climate alter powers extraordinary climate occasions, such as drawn out precipitation and cloudbursts,


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the investigate looks for to get it and moderate the compounded dangers postured by these marvels. This includes giving experiences into climate-resilient foundation arranging, versatile arrive administration hones, and the integration of avalanche defenselessness mapping into broader climate adjustment techniques.

Finally, the thinks about point to cultivate information dispersal and capacity building among partners. By preparing nearby governments, fiasco administration offices, and communities with deductively approved apparatuses and experiences, the investigate improves readiness and strength. Raising mindfulness among local populations approximately avalanche dangers and maintainable land-use hones is additionally a basic component of the thinks about, guaranteeing that communities can effectively contribute to lessening their powerlessness. In outline, these thinks about point to address the multifaceted challenges postured by avalanches in Kerala's Western Ghats whereas advancing feasible and strongimprovement

3. Literature Review

1. By Zorgati Anis, et.al

This study applies three bivariate factual models—WoE, EBF, and IoE—and their gatherings with calculated relapse (LR) for avalanche helplessness mapping in Muchuan Province, China. A avalanche stock outline with 279 avalanches was made through field examinations and ethereal photo elucidation. The dataset was part into preparing (70%) and approval (30%) sets. Twelve conditioning variables were chosen, such as height, incline, remove to streets, streams, and other natural highlights. The models produced defenselessness maps, approved utilizing the ROC bend. The EBF-LR show appeared the most elevated exactness (0.826), taken after by IoE-LR (0.825) and WoE-LR (0.792), advertising important experiences for avalanche anticipation and arrive utilizearranging.

2. By Renwei Lee, et.al

This study surveys avalanche vulnerability within the Darjeeling Himalayan locale utilizing the Prove Conviction Work (EBF) and Recurrence Proportion (FR) models. A add up to of 1582 avalanches were analyzed, with 70% utilized for demonstrate preparing and 30% for approval, consolidating sixteen spatial datasets related to geography,

hydrology, geography, and landcover. The ponder classifies the locale into five defenselessness zones, with the EBF model appearing 40.41% within the "exceptionally tall" category and the FR show appearing 33.51%. Approval utilizing the Collector Working Characteristic (ROC) and Zone Beneath the Bend (AUC) uncovered tall exactness, with victory rates of 0.937 (EBF) and 0.936 (FR) and expectation rates of 0.949 (EBF) and 0.953 (FR), highlighting their adequacy in avalanche chanceevaluation.

3. By Debasish Roy, et. Al

This inquire about compares five GIS-based bivariate factual models for foreseeing avalanche vulnerability within the Upper Tista bowl of the Darjeeling-Sikkim Himalaya. The models incorporate Recurrence Proportion (FR), List of Entropy (IOE), Measurable File (SI), Altered Data Esteem (MIV), and Evidential Conviction Work (EBF). The think about utilized a avalanche stock of 477 areas, with 70% of information for demonstrate preparing and 30% for approval. Fourteen components, like height, incline, precipitation, and lithology, were considered. The comes about appeared that the IOE show had the most noteworthy exactness (95.80%), taken after by SI, MIV, FR, and EBF. The models distinguished critical landslide-prone ranges, basically along the Tista Stream and major streets, recommending their appropriateness for avalanche relief and long-term arrive utilize arranging in comparative precipitouslocales.

4. By Jayanta Das, et.al

This consider centers on avalanche mapping along the Bukan-Sardasht mountain street in West Azerbaijan utilizing information mining strategies, such as Weight of Prove (Trouble) and Evidential belief function (EBF), with GIS and farther detecting methods. Twelve variables affecting avalanches, counting lithology, slant, NDVI, remove from waterways, and others, were distinguished and utilized to form a avalanche affectability outline. Information from 109 avalanche focuses were recorded, with 32 utilized for testing and 77 for preparing. The models' precision was evaluated utilizing the ROC bend, with EBF accomplishing the most elevated esteem (0.910), taken after by Burden (0.893). The comes about affirmed that higher avalanche vulnerability classes compared to higher avalanche thickness, approving the exactness of thezoningmaps.


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5. By Pawan Gautam, et.al

Landslide Susceptibility mapping (LSM) helps distinguishing and focusing on avalanche preventive measures, in this manner minimizing potential misfortunes. Different approaches are utilized for LSM in different physiographic locales; in any case, their appropriateness has varied over considers, with restricted understanding on the foremost reasonable approach for LSM in tall mountain regions. Hence, we conducted LSM within the Indrawati watershed, a tall mountain region of Central Nepal, utilizing four approaches: recurrence proportion, calculated relapse, fake neural organize, and bolster vector machine. Nine avalanche causal components (slant, viewpoint, height, topographical arrangement, vicinity to stream, vicinity to street, arrive cover, soil sort, and ebb and flow) were considered for LSM. Rainfall-induced avalanches were mapped by the on-screen digitization of obsequious pictures and field perceptions. The avalanches were arbitrarily part into a proportion of 80:20 for preparing and approving the vulnerability maps. The LSMs gotten by four strategies were at that point approved and compared utilizing zone beneath bend (AUC), kappa record, and factual inductions (affectability, specificity, positive prescient esteem, negative prescient esteem, and precision). Our consider appeared that Eutric Cambisols, a course of soil sort, contains a solid affiliation with avalanche event among the 52 classes of the nine causal variables. We found that the fake neural arrange approach had the most excellent expectation capability (AUC value = 86.9%) among the four strategies, taken after by calculated relapse (85.6%), back vector machine (81.2%), and recurrence proportion (80.1%) approaches. Be that as it may, Kappa index and other measurable deductions recommended the back vector machine approach to be the second-best method. Generally, we found that the manufactured neural organize yields more exact and solid comes about and thus considered as a promising approach for helplessness mapping in tall precipitous locale of Hindu-Kush Himalaya. The discoveries of this consider could be valuable for avalanche investigators, advancement organizers and decision-makers in conducting LSM and improvement arranging in tall mountaindistricts.

6. By Ebrahim Norani, et.al

Avalanches are the foremost visit wonder within the northern portion of Iran, which cause significant financial and life harms each year.

One of the foremost broadly utilized approaches to diminish these harms is preparing a avalanche helplessness outline (LSM) utilizing reasonable strategies and selecting the correct conditioning variables. The current think about is pointed at comparing four bivariate models, specifically the recurrence proportion (FR), Shannon entropy (SE), weights of prove (Burden), and evidential conviction work (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 areas of avalanches were gotten from field studies and translation of airborne photos. At that point, the areas were categorized into two bunches of 70% (74 areas) and 30% (35 areas), arbitrarily, for modeling and approval forms, separately. At that point, 10 conditioning components of incline perspective, ebb and flow, rise, remove from blame, lithology, normalized distinction vegetation record (NDVI), separate from the stream, separate from the street, the slant point, and arrive utilize were decided to build the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning variables within the event of avalanches. The recipient working characteristic (ROC) bend and the region beneath the bend (AUC) were utilized for approval of the four achieved LSMs. The AUC results introduced the victory rates of 0.8, 0.86, 0.84, and 0.85 for EBF, Trouble, SE, and FR, respectively. Also, they shown that the rates of expectation were 0.84, 0.83, 0.82, and 0.79 for Burden, FR, SE, and EBF, respectively. Therefore, the Misfortune show, having the most noteworthy AUC, was the foremost exact strategy among the four executed strategies in recognizing the locales at risk of future avalanches within the think about zone. The results of this investigate are valuable and fundamental for the government, planners, decision producers, analysts, and common land-use organizers within the considerregion.

4. Areas and Methods

1. Study Area

Palakkad area, found in Kerala, India, covers an zone of roughly 4,480 square kilometers. Topographically, it is arranged between 10° 20' N to 11° 10' N scope and 76° 30' E to 77° 10' E longitude. The area lies at the foothills of the Western Ghats, with a assorted geography extending from level fields to tough slopes.


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The region's rise changes essentially, with regions at lower rises around the fields and higher heights within the sloping regions, coming to up to 2,200 meters in a few parts. Palakkad encounters a tropical climate with unmistakable damp and dry seasons, affected by the southwest rainstorm. The yearly precipitation midpoints around 2,500 mm to 3,000 mm, with the rainstorm season from June to September being especially seriously, driving to expanded avalanche hazard due to overwhelming precipitation and soil immersion. The Western Ghats, characterized by soak slants, thick timberlands, and a blend of rough and soil-covered landscapes, are profoundly helpless to avalanches, particularly amid the storm. The combination of overwhelming precipitation, soak slopes, and ongoing deforestation, at the side human exercises like street development, increments the defenselessness of the locale to avalanches, especially in ranges like Attappady, Parambikulam, and Nelliyampathy.

2. Method

The Evidential belief function (EBF) , determined from Dempster–Shafer Hypothesis (DST), is broadly utilized in avalanche defenselessness mapping because it coordinating numerous sources of prove and successfully handles vulnerability and inadequate information. It comprises of four key capacities:

Conviction (Bel), which measures the bolster for avalanche event; Doubt (Dis), which speaks to conflicting prove; Vulnerability (Unc), which accounts for questions; and Credibility (Pls), which characterizes the upper restrain of likelihood. By overlaying avalanche stock maps with different conditioning components such as slant, geography, soil sort, precipitation, and arrive utilize in Geographic Data Frameworks (GIS), the EBF demonstrate makes a difference recognize high-risk zones for avalanches. Its capacity to consolidate numerous natural parameters and oversee fragmented data makes it a adaptable and vigorous apparatus for risk evaluation and catastrophe administration, helping in hazard moderation, foundation arranging, and arrangement improvement.

5. Our Works

We have completed generating maps of different parameters of Palakkad district.

1. Elevation

Height plays a noteworthy part in avalanche helplessness, because it specifically impacts incline soundness, water seepage, and soil disintegration. Higher heights, frequently found in hilly and uneven districts, are more inclined to avalanches due to more extreme slants, expanded gravitational drive, and strongly weathering. These zones too encounter higher precipitation collection, which can lead to soil immersion and incline disappointment. Alternately, lower heights, such as valleys and riverbanks, may be influenced by avalanches due to dregs statement, undermining by water bodies, and collection of free materials. Rise information, frequently determined from Advanced Height Models (DEM), is significant in avalanche hazard mapping, making a difference distinguish defenseless ranges and bolster moderation endeavors through legitimate land-use arranging and foundationadvancement.

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Figure 1: Elevation Map of Palakkad

2. Slope

Slope could be a key calculate in avalanche vulnerability, because it decides the solidness of the landscape. More extreme inclines involvement more grounded gravitational powers, making them more inclined to incline disappointment, particularly when combined with components like overwhelming precipitation, deforestation, or weak soil composition. Slants between 15° and 45° are for the most part the foremost helpless to avalanches, as they give adequate steepness for development whereas still permitting soil and flotsam and jetsam aggregation.


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Delicate slants are ordinarily more steady, but avalanches can still happen in ranges with free or soaked soil. GIS-based incline examination, inferred from Advanced Height Models (DEM), makes a difference in distinguishing high-risk zones and supporting calamity avoidancemeasures.

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Figure 2: Slope map of Palakkad

3. Topographic Wetness Index

Topographic Wetness Index (TWI) could be a key calculate in avalanche helplessness because it speaks to the potential for water amassing in a scene. Higher TWI values show zones with more prominent dampness maintenance, such as valleys and concave slants, where soil immersion increments the hazard of slant disappointment. Then again, lower TWI values are found on edges and soak inclines, where water channels rapidly, lessening avalanche chance. Intemperate water substance debilitates soil cohesion and increments pore water weight, making high-TWI districts more inclined to avalanches, particularly amid overwhelmingprecipitation.

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Figure 3: TWI of Palakkad

4. Land Use/Land Cover

LULC (Land Use and Land Cover) data is key in evaluating landslide dangers.

Forests offer assistance stabilize soil, lessening landslide hazard, whereas urbanization and agriculture on steep inclines can increment it. Urban sprawl and deforestation make soil more inclined to erosion, activating landslides. Rural practices, like overgrazing, can moreover destabilize land. LULC mapping makes a difference recognize vulnerable zones and educate land utilize arranging to minimize landslidedangers.

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Figure 4: LU/LC of Palakkad

5. Terrain Ruggedness Index

TRI (Terrain Ruggedness Index) could be a degree of surface roughness or topographic variability, often utilized to survey landslide vulnerability. The next TRI value demonstrates more rugged landscape, which can be more inclined to landslides due to steep inclines and irregularities. TRI makes a difference recognize ranges with unsteady slants where landslides are more likely to happen, making it a valuable instrument in landslide risk assessments and land-useplanning.

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Figure 5: TRI of Palakkad

6. Curvature

Profile curvature refers to the curvature of the land surface along a slope, influencing water stream and soil stability.


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In landslide considers, concave profiles (bending internal) tend construct upto construct up water, expanding immersion and landslide chance. Curved profiles (bending outward) can improve waste, decreasing landslide hazard. Analyzing profile curvature makes a difference identify ranges inclined to landslides, particularly on inclines with tall water maintenance orpoordrainage.

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Figure 6: Profile Curvature of Palakkad.

7. Rainfall/Precipitation

Rainfall could be a major trigger for landslides, particularly in regions with steep inclines. Heavy or drawn out precipitation saturates the soil, lessening its stability and expanding the probability of landslides. Zones with tall precipitation, especially amid strongly storms, are more vulnerable to avalanches due to the included weight of water and reduced contact between soil layers. Checking precipitation designs makes a difference evaluate landslide hazard in helplessdistricts.

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Table 1: Annual rainfall of Palakkad from year 2018-2024
(in mm)

6. Conclusion

The landslide susceptibility mapping of Palakkad utilizing QGIS has made critical advance within the information collection and spatial examination stages. Different conditioning variables impacting landslide event have been distinguished, and their comparing topical maps have been effectively made. These include height, incline, Topographic Wetness Record (TWI), Topographic Toughness List (TRI), land uselandcover (LULC), and curvature maps, all of which give basic experiences into terrain characteristics and stability. Furthermore, yearly precipitation information from 2018 has been collected, which is able play a vital part in understanding the affect of precipitation on landslide susceptibility.

The fruitful creation of these maps gives a solid establishment for understanding the territory and its helplessness to avalanches. The compiled datasets and spatial investigations offer valuable insights that can contribute to disaster risk assessment and natural planning in Palakkad. These discoveries will be instrumental in enhancing preparedness and directing decision-making for sustainable land use and danger mitigation withintheregion.

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