International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-2, December 2019 4274 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: B7725129219/2019©BEIESP DOI: 10.35940/ijitee.B7725.129219 Abstract: Landslide is one of the major natural hazards which is experienced all over the world and causes huge losses to land and property. Most of the landslides are generally caused by multiple factors which act together to destabilize the slope. But among them, the most common trigger for the landslides have been excessive rainfall and no proper planning have been leading to disastrous outcomes. So, in this research, mostly focus on the landslides which are induced due to rainfall to find a solution to the problem. It is present an overview on the challenges being faced in the prediction of Rainfall induced landslides. Also the objective is to find relevant approaches and techniques and judge the best possible method and algorithms which gives the most accurate results. Keywords : Support Vectors, Probability, Prediction model, Regression, Risk, Accuracy I. INTRODUCTION Very minimal importance is being given to the topic and very less planning is being done in advance regarding it. So, there is a need to find a reliable system through which the occurrence of such hazards could be predicted and the losses imposed because of it could be minimized. There are already some models which have been proposed but each has some liabilities as well as advantages. There has not been a model which can prove to be fully reliable in such adverse circumstances. The main goal through this research is to analyze the various methods already in use and find out the important functionalities within each system [10]. And today the most widely used method for the prediction related topic is Machine Learning. Machine learning is a way of identifying patterns in data and using them to automatically make predictions or decisions in the future [1]. In this research, to analyzing the various methods and Machine Learning algorithms which have already being implemented and bring out the specific details regarding each model for the Landslide Prediction and also attempting to create a system which can prove to be reliable as well as efficient under such circumstances. Revised Manuscript Received on December 05, 2019. * Correspondence Author K.Uma*,School of Information Technology and Engineering, VIT University, Vellore, India, drumakphd@gmail.com C.Ramesh Kumar, School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, India. mail: c.ramesh@galgotiasuniversity.edu.in T.R.Saravanan, Department of CSE, Jeppiaar SRR Engineering college, Chennai-603103, saravanan5_t_r@yahoo.co.in M.Basha Khaja, Wipro Technology, Software engineer, Ireland, United Kingdom, ghaja.bms@gmail.com II. LITERATURE SURVEY A significant number of papers have been reviewed covering the landslide studies from different regions of the world. There are some distinct, different and new methodologies for the problem and each specific approach has its own limitations and advantages. Each journal has been divided into separate tables with column headings containing the methods/Algorithms Used, Factors considered in the Dataset, the final Results and findings of their work, and the final Remarks [13][14]. A. Comparison Analysis A wide range of models and methods have been used in the various papers according to the dataset which was planned to be used. Some of them had comparisons between the various possible algorithms for finding out the best possible option for their model. But the results were found very contrasting to each other. According to some Support Vector Machines (SVM) was the model giving best probabilities [1].The tree based models such as Logistic Model Tree[2] and Random Forest[4] were also proved to be better in some papers and for some it was Regression and Naïve Bayes giving the best results . Besides these some hybrid algorithms [3][5] have also been used which according to their results were proven to be better than the conventional methods which are mostly used . Besides these methods, the conventional methods like Artificial Neural Networks (ANN) [8] and Multilayer Perceptron’s [6] are also being used. But these methods were majorly used when one of the factors among the dataset was the Plane Curvature, and were mostly used for the prediction of Slope Instabilities considering the Rate of Displacement of the land as inputs. Some other unique models such as Rotation Forest Fuzzy Rules Based Classifier Ensemble (RFCE) [7], Wavelet Transform (WT) and the Artificial Bees colony (ABC) were also used but the details about their implementation were not shared. Every research had their unique set of factors considered for the dataset. Among them, Rainfall and Slope were found to be the most common factors. The factors were chosen taking into consideration as to which location the data has been collected. The weather and climate conditions play a vital role in choosing the factors for a particular place or location. This has been the reason for getting so much disparity among the factors being considered in the various research papers. Some considered Rainfall, Pressure, Wind Speed in the dataset [1], whereas in some Elevation, Distance from River and Water Prediction of Rainfall Induced Landslides using Machine Learning Algorithms K.Uma, C.RameshKumar, T.R.Saravanan, M.Basha Khaja