This document summarizes a research presentation on predicting human-wildlife conflict in Chitwan National Park using data mining. It introduces the topic, discusses key aspects like conflict origins, outcomes, and the K-Modes clustering methodology used. The results show that conflict is most common in winter/spring, with elephants destroying crops randomly and tigers/leopards attacking livestock in spring. Rhinos attack humans and crops in winter. The research was limited by the compensation scheme and lack of temperature data, and could be enhanced by studying other areas and predicting species mobility.

1. Human and Wildlife(HWC) conflict prediction of Chitwan
National Park using Data Mining
Research Presentation
Science, Engineering and Technology(SET) Research Conference
Advance College of Engineering and Management, Kathmandu, Nepal
19th November 2016
Samir Pokhrel
samir.pokhrel02@gmail.com
http://www.samirpokhrel.com.np
Kshitij Wagle
kshitij.wagle@gmail.com
http://www.kshitijwagle.com.np

2. Research Outline
• Introduction
• HWC Demo(video)
• Conflict Origin
• Outcomes of Conflict
• Methodology
• K-Modes: Clustering Categorical Data
• Data Mining process of CNP
• Crop Damage Status of CNP
• Key Findings
• Output: R Studio
• Comprehensive Result
• Research Limitation
• Further Enhancement

3. Introduction
 HWC occur when the needs and behavior of wildlife impact negatively
on the goals of the human
 OR when the goals of the human negatively impact on the need of
wildlife
 The main cause of HWC is the competition of resources between growing
human populations and wildlife
 The total compensation provided by Chitwan National Park is Rs.
1,58,15,000 |- (2055-2072 BS/according to annual report)

4. Introduction: HWC demo(video)

5. Conflict Origin
 Growing human populations overlaps with established wildlife territory
 Reduction of resources to human and wildlife
 Climate change
 Deduction of water source.

6. Outcomes of Conflict
• Injury and loss of life of humans and wildlife
• Crop damage, livestock depredation, predation of managed wildlife stock
• Damage to human property
• Trophic cascades
• Destruction of habitat
• Collapse of wildlife populations and reduction of geographic ranges

7. K-Modes: Clustering Categorical Data
 K-Means cannot handle non-numerical(categorical ie. Month/season )
data
 K-Modes: An extension to K-Means by replacing the Euclidean distance
function, with the simple matching dissimilarity measure
It is a frequency-based method to find the modes
Eg. One of the cluster(Thori) contains total no of attack(ie.
Frequency) by Tiger to Domestic animals

8. Data mining process of CNP

9. Crop Damage Status of CNP
 In past years the crop damage is almost negligible but in recent days it is
more pronounced

10. Key Findings
 Maximum affected area
 Season and month of high conflict
 Maximum affected predators
 Maximum affected prey

11. Output: R Studio

12. Comprehensive Result
 HWC is more pronounced in Winter and Spring season.
 Especially,
• The elephant destroys households and crops
• Tiger and leopard attacks domestic animals
• Rhino attacks human and destroys crops
• The destruction by rhino is during winter season
• Attack by tiger and leopard is during spring season
• In case of destruction by elephant, the season is random

13. Comprehensive Result(contd..)
S.N.
Wild
Animals(Predators) Suffered Places
1 Rhino Lamichaur, Ayodhyapuri, Rewa, NandaVauju, Mrigakunja
2 Elephant Ayodhyapuri,Panchpandav, Rewa, NirmalThori
3 Tiger
Lamichaur, Ayodhyapuri, NirmalThori, Amaltari, BudiRapti,
Sisuwar
4 Bear Ayodhyapuri, Nikung
5 Leopard
Khagendramalli, Panchpandav, Mrigakunja, BudiRapti,
DaunneDevi
fig: Animals and their target places

14. Research Limitation
 No clear approach for compensation scheme
 Unavailability of temperature attribute from the year 2055 to 2072
 Species-specific catastrophes

15. Further Enhancement
 The field of study and mining can be extended to other conservation
areas of Nepal
 Mobility patterns and species specific travel distance prediction

16. Thank you !!!