IRSPAS 2016
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/15651
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Item Predicting landslides in hill country of Sri Lanka using data mining techniques(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Karunanayake, K.B.A.A.M.; Wijayanayake, W.M.J.I.A landslide is the movement of rock, debris or earth down a slope. They result from the failure of the materials which make up the hill slope and are driven by the force of gravity. When it refers to Sri Lankan context landslide is the major natural disaster in hill country of Sri Lanka, creating economical and ecological damage while endangering human lives. Therefore, the fast detection plays an important role in avoiding or minimizing the hazards. Currently in Sri Lanka National Building Research Organization (NBRO) under the Ministry of Disaster Management in Sri Lanka issue landslide early warning messages based on Landslide Hazard Zonation Map and readings of auto meter rain gauging. However, a map is only covering a specific point in time, and do not take current weather and geographical conditions into account. Though they collect current rainfall using auto meter rain gauging this facility is not established in everywhere. As the hill country is a rapidly developing area some causative factors can be changed time to time due to human intervention or natural incidents. Therefore, it is understood that there has a problem in predicting landslide depending on current situation. On the other hand, to deal with the current approach there must have an expert. The main objective of this study is to develop a model which can be embedded to develop an user friendly and efficient computer program which is usable by any ordinary person who is living in a landslide prone area to determine “am I safe in the current place with regards to current geological and weather condition?” by dealing with data of current situation rather than living blindly until NBRO issue warnings. Most of the time landslides often occur at specific location under certain topographic and geologic conditions within the country and it is important to utilize existing data to predict landsides. Data mining techniques can be used to develop prediction models using existing data. Plan-Do-Check-Act data mining methodology has been selected for this study. Initially, study is limited to homogeneous areas of Badulla and Nuwara-Eliya districts which are already identified as landslide prone areas. Based on the homogeneity of these areas models will be developed by incorporating only three causative factors, slope, surface overburden, land use which are varying due to human intervention and natural incidents and triggering factor, rain fall. The historical data are collected using the contours, map of land use, map of overburden and map of landslides. The decision tree algorithm and the neural network technique will be used to develop prediction models out of predictive analysis data mining techniques. The cross validation evaluation technique will be used to test the models and ultimately select the best model out of decision tree algorithm model and neural network model.Item Analysing mobility patterns of people to determine the best transportation method(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Senanayake, J.M.D.; Wijayanayake, W.M.J.I.With the technological enhancements related to Internet, Wireless Communication, Big Data Analytics, Sensor-based Data, and Machine Learning; new paradigms are enabled for processing large amount of data which are collected from various sources. In the past decades, both coarse and fine-grained sensor data had been used to perform location-driven activity inference. In recent years, GPS phone and GPS enabled PDA become prevalent in people’s daily lives. With such devices people become more capable than ever of tracing their outdoor mobility and using locationbased applications. Based on the collected data from these GPS enabled devices with the help of IoT related to user mobility lots of research areas are opened. In this research the data related to user locations when users do any outdoor movements is collected using the mobile devices that are connected to the Internet and is mined using data mining techniques and come up with an algorithm to model & analyse those big data to identify mobility pattern, traffic prediction, transportation method satisfaction etc. The data for this research will be collected using a mobile application which has to be installed in smart devices like smart phones, tablet PCs etc. In this application the user has to enter the activity that he or she currently doing and the method of transportation & the users' opinion on the transportation method if he is doing some sort of travelling. The GPS coordinates (longitude & latitude) as GPS trajectories along with the time stamp and the date will be automatically acquired from the users' IoT device. A cloud based storage will be used to store collected data. Since the dataset is going to be a huge one, there can be data which contains outlier values due to the uncertainty of the mobile network coverage and the GPS coverage of the devices. Therefore, these data should be properly cleaned when doing data mining activities otherwise these data will lead to incorrect results such as wrong traffic prediction in certain places if several users are stuck in the same GPS coordinates for a while. Not only that but also when it comes to the user satisfaction, it might lead to generate incorrect outcome if the users in the sample will not enter their satisfaction accurately. This can be avoided by comparing cluster wise users with the consideration of the location and the transportation method. We can get the average opinion of the users and take it as the satisfaction of the transportation method in that cluster. Using the final results of this research the government can also be benefited if we selected the sample users well with mixing all the types of people and by providing necessary information for planning smart cities.Item Natural language processing framework: WordNet based sentimental analyzer(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Jayakody, J.R.K.C.Sentimental analysis is a technique which is used to classify different types of documents as positive, negative or neutral. Hand written form, mails, telephone surveys or online feedback forms are used to get customer feedbacks about products and services. In fact, sentimental analysis is the technique which is used to mine online and offline customer feedback data to gain insight of product and services automatically. Since business types are different, it is quite challenging to develop a generic sentimental analyzer. Therefore, this ongoing research focused on developing a generic framework that can be extended further in future to develop the best generic sentiment analyzer. Several online customer feedback forms were used as the dataset. Webpage scraping module was developed to extract the reviews from web pages and chunk and chink rules were developed to extract the comparative and superlative adverbs to build the knowledge base. The web site (Thesaurus.com) was used to build the test data with synonyms of good, bad and neutrals. Next WordNet database was used with different semantic similarity algorithms such as path similarity, Leacock-Chodorow-similarity, Wu- Palmer-Similarity and Jiang-conrath similarity to test the sentiments. Accuracy of this framework was improved further with the vector model built with natural language processing techniques. Label dataset of amazon product reviews provided by University of Pennsylvania were used to test the accuracy. Framework was developed to change the multiplied value based on the domain. The accuracy of the final sentiment value was given as a percentage of the positive or negative type. This framework gave fairly accurate results which are useful to generate good insights with user reviews.