Multi-Parameter Decision Support with Data Transmission over GSM/GPRS Network: a Case Study of Landslide Monitoring

In the current era of technological advancement, the wireless sensor networks (WSNs) appear to be the best suitable candidate to sense the Geo-technical and other environmental parameters like temperature, humidity, wind speed, etc. This paper implements and experienced a realtime monitoring system to know the early occurrences of landslides in the north eastern region of India in the state of Arunachal Pradesh. In this work, an infrastructure based data gathering system has been proposed to collect the geotechincal data and stored it in the centrally located datacenter. In addition to this, an application package has been developed for realtime data analysis and pre- disaster early warning. The system collects data from various geo-sensors such as rain gauge, inclinometer, tilt-meter, piezo- meter and crack meter. This paper also introduces a low cost data transmission method over GSM/GPRS network to the centrally located datacenter. Disaster prevention and mitigation are integral to devel- opment activities. Major natural disasters can and do have sever negative short-run economic impacts. Natural disasters cause significant loss both in terms of human casualty as well as budgetary development. In the recent years, the death and damage in natural disasters have increased. Hence, in this context there is good reason to study and analyse the sensational aspects of disasters. There may have some general principles which creates this irregularity which results in disasters. In this irregularity, whether some general principles exist or not, the answer may be yes. Therefore it is necessary to gather information about earths physical, chemical and biological changes in a global scale. India is being affected by hydro-geological hazards like landslide since years ago. History depicts that, it has happened mostly in Himalayan regions, Hill ranges of Northeast, West- ern & Eastern Ghats of India. The North East (NE) region of India is badly affected by bewildering varieties of landslides; specifically the state of Arunachal Pradesh. The entire hilly region of the state is geologically very young and unstable. This make the landslide monitoring as an important aspects and challenges for the geo-scientists in India. Most of the landslides in India are primarily caused due to heavy rainfall. Depending on meteorological and physiographical conditions, individual rainfall events can cause slope failures in areas of limited extent or in large regions. Developing an early warning system for the monitoring of landslides requires its domain expertise, not just to build the instruments but to use them properly and interpret their output for rational purpose. Knowing the occurrences of this event is a difficult task to tackle and requires a thorough study of past activities of the geo-technical condition as well as the local calamities. However, this event can possibly be predicted if proper geo- technical investigations of the triggering parameters are per- formed. Currently, no system exists for NE region of India to identify the triggering of landslides, largely due to lack of observing the changing conditions of the geology at this region. Apart from the landslides, the state is also prone to various other hazards such as earthquake, floods, forest fires, cyclones, etc. Due to high seismicity, high annual rainfall and geological fragility of the region, almost all parts of the state experience one or more of these disasters every year (1). In this paper, we proposed a framework to develop a real- time monitoring and prediction system looking at the causing parameters around NE region of India with global approach. This paper first articulates the real-time rainfall observations to assess landslide hazards. The goal of this system is to acquire a global view rather than the site specific view to design a fully functional application software module for landslide monitoring. A pilot study has been made at the deployment site to identify the causing parameters and subsequently their corresponding thresholds from the past event that has resulted in landslides.