A Critical Assessment of IoT Based Landslide Prediction Systems Using AHP

Abstract

Worldwide landslide is a serious hazard that causes great damage not only to the economic and societal development but also to precious human lives. Therefore, there is an urgent need for a good landslide prediction system. Recently, Internet of Things (IoT) has emerged as a popular technology that has a quick response to rapid changes in data. Hence, it has been widely used for landslide monitoring and prediction. Present study focuses on a review of existing IoT based landslide prediction systems. Ten most widely referred systems developed by various researchers have been reviewed. A comparative analysis of their key features has been performed. Prioritization of these ten systems has been done using Analytical Hierarchy Process (AHP) based on three factors, i.e., Cost, number of parameters sensed, and technology. The most suitable IoT based landslide prediction system as obtained from AHP based prioritization is recommended for implementation.Landslide is a geological phenomenon caused due to perceptible downward and outward movement of soil, rock, and vegetation under the influence of gravity. Landslides can be classified according to geotechnical properties of rocks, movement of materials, etc. Landslide movement can be of various types viz., fall, slide, topple, spread or flow. The velocity can vary from very slow to rapid [1]. Mountainous regions are generally considered landslide prone areas. In low-relief areas like roadway and relief excavations, landslides occur as cut and fill failures, river bluff failures, lateral spreading landslides, collapse of mine-waste piles (especially