DOCSLIB.ORG

Landslide Susceptibility Investigation for Idukki District of Kerala Using Regression Analysis and Machine Learning

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Landslide Susceptibility Investigation for Idukki District of Kerala Using Regression Analysis and Machine Learning 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
Load more

Recommended publications
  • IT/KGB/TENDER/3/98/2018 Date: 16-03-2018
    Ref: IT/KGB/TENDER/3/98/2018 Date: 16-03-2018 TENDER FOR AMC OF COMPUTER HARDWARE ITEMS We invite sealed tenders on item rate basis from interested vendors for Annual Maintenance Contract of Computer Hardware Items installed at various branches/offices of our Bank in Trivandrum, Kollam, Pathanmthitta, Alappuzha, Kottayam, Idukki, Eranakulam, Thrissur, Palakkad, Malappuram, Wayanadu districts and part of Kozhikode district. List of branches and Hardware items are mentioned in Annexure D. The number of hardware items may be increased or decreased at the time of contract or even during the period of AMC which may be included or excluded from the list and the location of the equipments may also be changed. The AMC vendor shall include these items under the AMC on being informed by the Bank. Eligibility of vendor for Tender: The Vendor must have at least TWO direct offices in the mentioned geographical area (one office anywhere in TVM, KTM or EKM districts and another one anywhere in TSR, MPM or KZD districts) for fast and effective service support. Address proof of service support office or branch office should be produced such as PAN, Tax receipt, Certificate of registration like Shop & Establishment, Service Tax/Sales Tax/VAT/LBT/etc. The vendor must inform the full address and name of the office-in-charge. The vendor must have at least two qualified Service support engineers at each office above. The vendor must have an average annual turnover of Rs. 10 crore (As per last three years’ Audited Annual Financial Statement/Income tax returns - to be enclosed).
    [Show full text]
  • Relationship Beyond Banking THODUPUZHA BRANCH Sreevalsam Building, Ambalam Bye Pass Road, Thodupuzha, Idukki – 685584 Ph: 0486-2227781
    Relationship beyond Banking THODUPUZHA BRANCH Sreevalsam Building, Ambalam Bye Pass Road, Thodupuzha, Idukki – 685584 Ph: 0486-2227781. Email: [email protected] Zonal Office: Kerala Zone, Kaloor Towers, Kaloor-Kadavanthra Road, Cochin - 17 Phone: 0484-2725723, 2725729, 2725730 Email: [email protected] Sale notice for sale of immovable properties E-Auction Sale Notice for Sale of Immovable Assets under the Securitisation and Reconstruction of Financial Assets and Enforcement of Security Interest Act, 2002 read with proviso to Rule 8 (6) of the Security Interest (Enforcement) Rules, 2002 Notice is hereby given to the public in general and in particular to the Borrower (s) and Guarantor (s) that the below described immovable property mortgaged/charged to the Secured Creditor, the physical possession of which has been taken by the Authorised Officer of Bank of India (secured creditor), will be sold on “As is where is”, “As is what is”, and “Whatever there is” on 28.05.2019, for recovery of dues as mentioned hereunder to Bank of India from the below mentioned Borrower & Guarantors. The Reserve Price and the Earnest Money Deposit are also mentioned hereunder: Borrower/s, Guarantor/s Description of the Date of Date of Date & Upset Price & Name & Address, Total Property Demand Possession Time of EMD Due + Interest From Notice E-Auction of the Property Borrowers: 1.Mr. Sidharthan P S 01.01.2018 22.03.2018 28.05.2019 2.Mrs. Beena Sidharthan Reserve Price: From 12.00 Noon to Rs.42, 90, 000/- Both Residing at 12.95 Ares of Land and 1.00 (Rupees Forty building thereon owned P.M.(IST) Parackal House by Mr.
    [Show full text]
  • Nilgiris District, Tamil Nadu Connie Smith Tamil Nadu Overview
    Nilgiris District, Tamil Nadu Connie Smith Tamil Nadu Overview Tamil Nadu is bordered by Pondicherry, Kerala, Karnataka and Andhra Pradesh. Sri Lanka, which has a significant Tamil minority, lies off the southeast coast. Tamil Nadu, with its traceable history of continuous human habitation since pre-historic times has cultural traditions amongst the oldest in the world. Colonised by the East India Company, Tamil Nadu was eventually incorporated into the Madras Presidency. After the independence of India, the state of Tamil Nadu was created in 1969 based on linguistic boundaries. The politics of Tamil Nadu has been dominated by DMK and AIADMK, which are the products of the Dravidian movement that demanded concessions for the 'Dravidian' population of Tamil Nadu. Lying on a low plain along the southeastern coast of the Indian peninsula, Tamil Nadu is bounded by the Eastern Ghats in the north and Nilgiri, Anai Malai hills and Palakkad (Palghat Gap) on the west. The state has large fertile areas along the Coromandel coast, the Palk strait, and the Gulf of Mannar. The fertile plains of Tamil Nadu are fed by rivers such as Kaveri, Palar and Vaigai and by the northeast monsoon. Traditionally an agricultural state, Tamil Nadu is a leading producer of agricultural products. Tribal Population As per 2001 census, out of the total state population of 62,405,679, the population of Scheduled Castes is 11,857,504 and that of Scheduled Tribes is 651,321. This constitutes 19% and 1.04% of the total population respectively.1 Further, the literacy level of the Adi Dravidar is only 63.19% and that of Tribal is 41.53%.
    [Show full text]
  • Migration and Social History of Anjunadu: Lessons from the Past for Sustainable Development – an Applied Study
    PESQUISA – Vol.3, Issue-2, May 2018 ISSN-2455-0736 (Print) www.pesquisaonline.net ISSN-2456-4052 (Online) Migration and Social History of Anjunadu: Lessons from the Past for Sustainable Development – An Applied Study Santhosh George Assistant Professor, Dept. of History, Pavanatma College, Murickassery Email: [email protected] Article History ABSTRACT Received: At the north east portion of Idukki district of Kerala there are a few locations that 30 March 2018 shows extreme geographical differences compared to the rest of the district. These Received in revised locations include places namely Marayoor, Kanthalloor, Keezhanthoor and Karayoor form: 5 May 2018 and Kottagudi - collectively known as the „Anjunadu‟ (Five places). We can Accepted: experience a replication of Tamil culture on the valleys of this region. These gifted 16 May 2018 places are the abode of natural serenity, cluster of tribal settlements, a treasure of historical knowledge, a land of social formations and a can of cultural blending. KEY WORDS: Through this study the researcher tried to connect past and present for the future of the Anjunadu, Anjunadus. Basic historical courses of this region are tried to be analyzed in order to Responsible prepare a comprehensive outline for the sustainable development of this region. Hence Tourism, Migration, practicability has given more importance. Cultural fusion INTRODUCTION The Anjunadu: the land of Mesolithic and Neolithic life in the present Idukki district of Kerala state. This is the area from where an early image of the prehistoric men reveals. The petrogrphs , dolmens and petrolyph survive in this area gives us an idea to reconstruct the glorious social history of the valleys.
    [Show full text]
  • Accused Persons Arrested in Kottayam District from 03.05.2020To09.05.2020
    Accused Persons arrested in Kottayam district from 03.05.2020to09.05.2020 Name of Name of the Name of the Place at Date & Arresting Court at Sl. Name of the Age & Cr. No & Sec Police father of Address of Accused which Time of Officer, which No. Accused Sex of Law Station Accused Arrested Arrest Rank & accused Designation produced 1 2 3 4 5 6 7 8 9 10 11 Cr. No. 609/20 PALAKKATTU KOTTAYAM U/S KOTTAYAM Bail from 1 ARAVIND SREENIVASAN 24 PARAMBIL H, PARIPPU TOWN 04.05.20 2336,269,188 SURESH T WEST PS Police station KAVALA, AYMANAM BHAGOM 18:29 Hrs IPC & 4(2)(a) OF KEPDO Cr. No. 610/20 OLIPARAMBIL H, U/S CHANDAKKADA KOTTAYAM Bail from 2 SUBIN KUMAR PRASANNAN 30 AYMANAM KAVALA 05.05.20 2336,269,188 SREEJITH T VU WEST PS Police station BHAGOM, AYMANAM 22:09 Hrs IPC & 4(2)(a) OF KEPDO AYIRAVELICHIRA H, Cr. No. 615/20 PALLICHIRA KARA, THIRUNAKKAR U/S ARUN KOTTAYAM Bail from 3 SUKUMARAN 27 KUMARAKOM, A STAND 06.05.20 2336,269,188 SABU SUNNY SUKUMARAN WEST PS Police station MOOLEPPADOM BHAGOM 18:57 Hrs IPC & 4(2)(a) BHAGOM OF KEPDO KAMMATH H, Cr. No. 616/20 PARAKKADAVU U/S VIJAY V VARGHESE KOTTAYAM Bail from 4 26 BHAGOM, PEROOR STAR Jn. 07.05.20 2336,269,188 SREEJITH T KAMMATH KAMMATH WEST PS Police station KARA, PEROOR 11.20 Hrs IPC & 4(2)(d) VILLEGE OF KEPDO Cr. No. 617/20 PLATHOTTATHIL H, U/S PAYYAPPADY KAVALA KOTTAYAM Bail from 5 THANKACHAN SASIDHARAN 52 STAR Jn.
    [Show full text]
  • IDUKKI Contact Designation Office Address Phone Numbers PS Name of BLO in LAC Name of Polling Station Address NO
    IDUKKI Contact Designation Office address Phone Numbers PS Name of BLO in LAC Name of Polling Station Address NO. charge office Residence Mobile Grama Panchayat 9495879720 Grama Panchayat Office, 88 1 Community Hall,Marayoor Grammam S.Palani LDC Office, Marayoor. Marayoor. Taluk Office, Taluk Office, 9446342837 88 2 Govt.L P School,Marayoor V Devadas UDC Devikulam. Devikulam. Krishi Krishi Bhavan, Bhavan, 9495044722 88 3 St.Michale's L P School,Michalegiri Annas Agri.Asst marayoor marayoor Grama Panchayat 9495879720 St.Mary's U P School,Marayoor(South Grama Panchayat Office, 88 4 Division) S.Palani LDC Office, Marayoor. Marayoor. St.Mary's U P School,Marayoor(North Edward G.H.S, 9446392168 88 5 Division) Gnanasekar H SA G.H.S, Marayoor. Marayoor. St.Mary's U P School,Marayoor(Middle Edward G.H.S, 9446392168 88 6 Division) Gnanasekar H SA G.H.S, Marayoor. Marayoor. Taluk Office, Taluk Office, 9446342837 88 7 St.Mary's L P School,Pallanad V Devadas UDC Devikulam. Devikulam. Krishi Krishi Bhavan, Bhavan, 9495044722 88 8 Forest Community Hall,Nachivayal Annas Agri.Asst marayoor marayoor Grama Panchayat 4865246208 St.Pious L P School,Pious Nagar(North Grama Panchayat Office, 88 9 Division) George Mathai UDC Office, Kanthalloor Kanthalloor Grama Panchayat 4865246208 St.Pious L P School,Pious Nagar(East Grama Panchayat Office, 88 10 Division) George Mathai UDC Office, Kanthalloor Kanthalloor St.Pious U P School,Pious Nagar(South Village Office, Village Office, 9048404481 88 11 Division) Sreenivasan Village Asst. Keezhanthoor. Keezhanthoor. Grama
    [Show full text]
  • Organic Carbon Status in Flood Affected High Hills of Kerala
    International Journal of Chemical Studies 2020; 8(4): 3033-3035 P-ISSN: 2349–8528 E-ISSN: 2321–4902 www.chemijournal.com Organic carbon status in flood affected high hills IJCS 2020; 8(4): 3033-3035 © 2020 IJCS of Kerala Received: 01-05-2020 Accepted: 03-06-2020 Sreejitha M Babu and Naveen Leno Sreejitha M Babu Department of Soil Science and Agricultural Chemistry DOI: https://doi.org/10.22271/chemi.2020.v8.i4ak.10111 College of Agriculture, Vellayani, Kerala, India Abstract The abnormally high rainfall received in Kerala from 1 June 2018 to 19 August 2018 resulted in severe Naveen Leno flooding in 13 out of 14 districts in the state. Due to this heavy rainfall, flooding occurred in Kerala state Department of Soil Science and and devastated most of the crops. The cumulative rainfall realised during 15-17 August 2018 resulted in a Agricultural Chemistry total of 414 mm for Kerala state with Idukki receiving 700 mm rainfall falling next to Peermade which College of Agriculture, Vellayani, received the highest rainfall of 800 mm. The paper focused on the effects of flooding on soil quality in Kerala, India southern high hills (AEU 14) of Idukki district, Kerala, for proper soil and flood management to avert soil degradation. The organic carbon content and the content of the available major primary nutrients nitrogen, phosphorus and potassium in these soils were determined. Survey was conducted to identify the flood affected areas of AEU 14 in Idukki district and details were collected. Representative surface soil samples (0-20 cm) were collected from these locations of AEU 14.
    [Show full text]
  • Kattappana School Code Sub District Name of School School Type 30001 Munnar G
    Kattappana School Code Sub District Name of School School Type 30001 Munnar G. V. H. S. S. Munnar G 30002 Munnar G. H. S. Sothuparai G 30003 Munnar G. H. S. S. Vaguvurrai G 30005 Munnar G. H. S. Guderele G 30006 Munnar L. F. G. H. S . Munnar A 30007 Munnar K. E. H. S . Vattavada A 30008 Munnar G. H. S. S. Devikulam G 30009 Munnar G. H. S. S. Marayoor G 30010 Munnar S. H. H. S. Kanthalloor A 30011 Peermade St. George`s High School Mukkulam A 30012 Nedumkandam Govt. H.S.S. Kallar G 30013 Nedumkandam S.H.H.S. Ramakalmettu A 30014 Nedumkandam C.R.H.S. Valiyathovala A 30015 Nedumkandam G.H.S. Ezhukumvayal G 30016 Kattappana M.M.H.S. Nariyampara A 30017 Peermade St.Joseph`s H.S.S Peruvanthanam A 30018 Peermade G.H.S.Kanayankavayal G 30019 Peermade St.Mary`s H.S.S Vellaramkunu A 30020 Kattappana SGHSS Kattappana A 30021 Kattappana OSSANAM ENG MED HSS KATTAPPANA U 30022 Peermade Govt V.H.S.S. T.T. I. Kumaly G 30023 Nedumkandam N S P High School Vandanmedu A 30024 Nedumkandam S.A.H.S. Vandanmedu A 30025 Peermade C.P.M. G.H.S.S. Peermedu G 30026 Peermade M.E.M.H.S.S. Peermede U 30027 Peermade Panchayat H.S.S. Elappara A 30028 Peermade G.H.S.Vagamon G 30029 Peermade St. Sebastians H.S.S. Cheenthalar A 30030 Peermade Panchayat H.S.S. Vandiperiyar A 30031 Nedumkandam Govt. H S S And V H S S Rajakumary G 30032 Peermade St.
    [Show full text]
  • Munnar Landscape Project Kerala
    MUNNAR LANDSCAPE PROJECT KERALA FIRST YEAR PROGRESS REPORT (DECEMBER 6, 2018 TO DECEMBER 6, 2019) SUBMITTED TO UNITED NATIONS DEVELOPMENT PROGRAMME INDIA Principal Investigator Dr. S. C. Joshi IFS (Retd.) KERALA STATE BIODIVERSITY BOARD KOWDIAR P.O., THIRUVANANTHAPURAM - 695 003 HRML Project First Year Report- 1 CONTENTS 1. Acronyms 3 2. Executive Summary 5 3.Technical details 7 4. Introduction 8 5. PROJECT 1: 12 Documentation and compilation of existing information on various taxa (Flora and Fauna), and identification of critical gaps in knowledge in the GEF-Munnar landscape project area 5.1. Aim 12 5.2. Objectives 12 5.3. Methodology 13 5.4. Detailed Progress Report 14 a.Documentation of floristic diversity b.Documentation of faunistic diversity c.Commercially traded bio-resources 5.5. Conclusion 23 List of Tables 25 Table 1. Algal diversity in the HRML study area, Kerala Table 2. Lichen diversity in the HRML study area, Kerala Table 3. Bryophytes from the HRML study area, Kerala Table 4. Check list of medicinal plants in the HRML study area, Kerala Table 5. List of wild edible fruits in the HRML study area, Kerala Table 6. List of selected tradable bio-resources HRML study area, Kerala Table 7. Summary of progress report of the work status References 84 6. PROJECT 2: 85 6.1. Aim 85 6.2. Objectives 85 6.3. Methodology 86 6.4. Detailed Progress Report 87 HRML Project First Year Report- 2 6.4.1. Review of historical and cultural process and agents that induced change on the landscape 6.4.2. Documentation of Developmental history in Production sector 6.5.
    [Show full text]
  • List of Biogas Plants Installed in Kerala During 2008-09
    LIST OF BIOGAS PLANTS INSTALLED IN KERALA DURING 2008-09 by Si ze Block Model Sr. No latrine g date Village amount Dist rict Dist & Name Subsidy Address Category Guidence Technical Technical Inspected Inspected Functionin Beneficiary Beneficiary 1 Trivandrum Vijayakumar.N, S/o Neyyadan Nadar, Vijaya Bhavan, Neyyattinkar Parassala HA 2m3 KVIC 0 3500 26.11.08 K.Somasekhar P.Sanjeev, ADO Neduvanvila, Parassala P.O & Pancht, Neyyattinkara Tq- a anPillai (BT) 695502 2 Trivandrum Sabeena Beevi, Kunnuvila Puthenveedu, Edakarickam, Kilimanoor Pazhayakunnu GEN 3m3 KVIC 0 2700 28.10.08 K.Somasekhar P.Sanjeev, ADO Thattathumala.P.O, Pazhayakunnummel Pancht, mmel anPillai (BT) Chirayinkeezhu Tq 3 Trivandrum Anilkumar.B.K, S/o Balakrishnan, Therivila House, Athiyannur Athiyannur HA 2m3 DB 0 3500 17.01.09 K.Somasekhar P.Sanjeev ADO Kamukinkode, Kodangavila.P.O, Athiyannur Pancht, anPillai (BT) Neyyattinkara Tq 4 Trivandrum Sathyaraj.I, S/o Issac, kodannoor Mele Puthenveedu, Perumkadav Perumpazhuth HA 2m3 DB 0 3500 18.01.09 K.Somasekhar P.Sanjeev ADO Punnaikadu, Perumpaxhuthoor.P.O, Neyyattinkara Pancht & ila oor anPillai (BT) Tq 5 Trivandrum Balavan.R.P, S/o Rayappan, 153, Paduva House, Neyyattinkar Athiyannur HA 2m3 DB 0 3500 04.02.09 K.Somasekhar P.Sanjeev ADO Kamukincode, Kodungavila.P.O, Athiyannur Pancht, a anPillai (BT) Neyyattinkara Tq-695123 6 Trivandrum Ani.G, S/o Govindan.K, Karakkattu Puthenveedu, Avanakuzhy, Athiyannur Athiyannur HA 2m3 DB 0 3500 08.02.09 K.Somasekhar P.Sanjeev ADO Thannimoodu.P.O, Athiyannur Pancht, Neyyattinakara Tq anPillai
    [Show full text]
  • Electronic Kits for Aided Schools
    No of school_ finance_ revenue_ no_of_divisi no_of_stude school_name code Type_Name District_name ons_9 nts_9 Electronic Kits 34001 S F A HSS Arthunkal Aided Alappuzha 7 317 3 34003 St Augustine HS Mararikulam Aided Alappuzha 2 81 1 34004 St Augustine HSS Aroor Aided Alappuzha 7 263 3 34008 HF HSS Kattoor Aided Alappuzha 4 167 2 34009 HSS Kandamangalam Aided Alappuzha 5 190 3 34010 St George HS Thankey Aided Alappuzha 3 111 2 34011 VHSS Kanichukulangara Aided Alappuzha 5 189 3 34012 Girl`s HS Kanichukulangara Aided Alappuzha 3 102 2 34015 St Mathew`s HS Kannankara Aided Alappuzha 2 90 1 34016 ABVHSS Muhamma Aided Alappuzha 5 208 3 34017 ECEK Union HS Kuthiathode Aided Alappuzha 3 108 2 34020 NSS HSS Panavally Aided Alappuzha 4 174 2 34021 VJHSS Naduvath Nagar Aided Alappuzha 10 416 4 34025 St Mary`s Girl`s HS Cherthala Aided Alappuzha 6 344 3 34026 SMSJ HS Thycattuserry Aided Alappuzha 1 29 1 34027 PSHS Pallippuram Aided Alappuzha 1 29 1 34028 TD HSS Thuravoor Aided Alappuzha 9 369 4 34029 St Sebastian HS Pallithode Aided Alappuzha 3 149 2 34030 St Michel`s HS Kavil Aided Alappuzha 4 138 2 34034 SNHSS Sreekandeswram Aided Alappuzha 8 336 4 34035 St Theresas HS Manappuram Aided Alappuzha 3 143 2 34037 St.Raphael's H S Ezhupunna Aided Alappuzha 6 231 3 34038 Holy Family HSS Muttom Aided Alappuzha 5 270 3 34041 SCS HS Valamangalam Aided Alappuzha 2 91 1 34042 ST Antony`s HS Kokkamangalam Aided Alappuzha 2 85 1 34046 MTHS Muhamma Aided Alappuzha 5 211 3 No of school_ finance_ revenue_ no_of_divisi no_of_stude school_name code Type_Name District_name ons_9 nts_9 Electronic Kits 34047 SN Trust HSS SN Puram Aided Alappuzha 4 157 2 35001 S D V B H S S Alappuzha Aided Alappuzha 4 145 2 35002 St.
    [Show full text]
  • Neelakurinji Final -Report.Pdf
    CONTENTS Chapter Content Page no Executive Summary 1 Introduction 1 2 Study Area 3 - 11 3 Hazard Vulnerability & Risk Assessment 12 - 23 4 Suggestions, Recommendation & Conclusion 24 - 25 Annexure I Institutional Details I - vi Annexure II Accident History of the Study Area Vii - viii Annexure III Resource Inventory (IDRN) Executive Summary Neelakurinji Blooming is one of the most amazing tourism events at Munnar in Idukki district, Kerala. It attracts huge number of local, national and international tourists during every blooming season. This spectacular phenomenon happens only once in twelve years, thus it has got its own novelty which is never ending. Each blooming season witnesses an increase in number of visitors comparing to its previous blooming seasons. After 2006, in this year ‘2018’, Kerala is once again getting ready to extend a warm welcome to this marvelous event which will be having its peak time between the months of July to October. The forest department is expecting a total of 8 lakhs visitors in this Neelakurinji season. This study was undertaken as per the outcome of the meeting held on 11/10/2017 chaired by Hon. Chief Minister regarding Neelakurinji Blooming Season Preparedness (vide letter no. DM1/76/2017-DMD). The study conducted by Kerala State Disaster Management Authority (KSDMA) mainly focused in identifying and preventing possible hazards which may cast dark shadow over this remarkable event through effective planning and preparedness on department basis. Approximately 1640 km2 area was demarcated and selected for conducting this study. The area is highly susceptible to major hazards like Landslides (High & Medium Hazard Zones), Flashfloods, Road accidents, Human animal conflicts etc.
    [Show full text]