MACHINE LEARNING ENABLED SYSTEM FOR LANDSLIDE EARLY WARNING

TECHNICAL FIELD

[01] The present disclosure herein, relates to field of disaster preparedness and pertains particularly to a device monitoring landslide and alert the population in the landslide prone areas.

BACKGROUND

[02] Landslide is one of the worst disasters faced by throughout the world, irrespective of the climatic condition and terrains, which leads to loss in human lives and well as monetary losses. If people would be alerted in prior about such events to occur, there would be precautionary measures taken to save human lives and other monetary loss.

[03] US20120206258A1 discloses an invention on network-based system for predicting landslides and providing early warnings. The system measures the geologic motion, hydrologic saturation and pressure levels of soil above the rock and the real time data of geologic and hydrologic from the sensors deployed compared with threshold readings.

[04] CN104794860A discloses an invention on mountain landslide monitoring and warning device based on technology of IoT and control method in which the dip sensor and communication chip is connected with MCU. The alarm information is transmitted to the base station using mobile terminal.

[05] CN103514711A discloses a debris flow disaster early warning system based on wireless sensor network. The early warning system is used to collect the debris flow information, and monitoring system using wireless sensor network. [06] The above-mentioned prior art states that there is a need for a system in which there should be an early warning system to detect landslide through real-time monitoring of the geological and weather data , an generate alarm if any deviation is detected through wireless communication.

[07] The present invention addresses the above-mentioned shortcomings of the prior art.

SUMMARY

[08] In one implementation, vision mote, earth observation mote and weather mote to detect the changes in landslide prone area.

[09] In other implementation, Xbee connection, transfers the data from different to the coordinator device.

[0010] In another implementation, LoRa protocol transfers the data further in long range up to 10km communication.

[0011] In the present implementation, Raspberry pi controller, to measure the changes from the sensors.

[0012] In other implementation , battery powered or solar panel used as the source of power supply.

BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The foregoing detailed description of the system implemented is better understood when read in conjunction with the attached drawings. For better understanding, each component is represented by a specific number which is further illustrated as a reference number for the components used with the figure.

[0014] Figure 1 illustrates , the block diagram of the present invention.

[0015] Figure 2 illustrates the block diagram of the earth observation mote of the present invention. [0016] Figure 3 illustrates the block diagram of the weather mote of the present invention.

[0017] Figure 4 , illustrates the block diagram of the alert system of the present invention.

DETAILED DESCRIPTION

[0018] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail.

[0019] In one embodiment, the device consists of vision mote, vision mote, earth observation mode and weather mode to detect the changes in landslide prone area. The data from different mote transfer data to the coordinator device using Xbe connection , further which will be transferred in long range using LoRa protocol to the site communication.

[0020] In other embodiment , the cloud server and LoRa based server are used for communication and the real-time data are stored in the cloud server.

[0021] Referring to figure 1, the earth observation mote is used to observe or detect the earth structure in various circumstances of the earth using different sensors such as Inclinometer, Tilt Meter, Acoustic Meter and Piezometers to observe earth structure in various circumstances.

[0022] Referring to figure 1, the inclinometer sensor detects the ground movement whereas the tilt sensor detects the angle of head from toe. Acoustic sensor, estimates any small movement of debris fall, which is used to hear the noise in landslide prone area. Piezometer is used to measure the vapour pressure of shear strength and Raspberry Pi controller is used to measure the changes from sensors , which will work with camera.

[0023] Referring to figure 1, the data will be transferred using Zigbee from different nodes to base station , by using Lora protocol the data will be sent in long range. The power source for the system is obtained through battery or solar panel power circuit. [0024] Referring to figure 2 , the geological data of landslides current weather data is required to get the relation between them. The real time of weather data is also required predict early warning system of the landslide equation in comparison with the reason for the landslide and the weather.In this node, the rainfall gauge, Temperature, Humidity and wind speed will be calculated in real time by using IoT and the data will be transfer to cloud service.

[0025] Referring to figure 3, when any deviation is detected, system should generate an alarm to evacuate the population from the landslide prone area. After proper sleeping time to different nodes, the data is collected and analyzed using the controller.

[0026] Referring to figure 3, the IoT data will be transmitted to cloud for further analysis and the wireless sensor network is created by zigbee and LoRa to send data in long range communication.

[0027] In one embodiment , the sensors provide value beyond its characteristics then troubleshooting of hardware is also required.

[0028] In other embodiment , sensors can be increased further to obtain precise value

[0029] Some of the embodiments may further be upgraded depending upon further investigation is required to detect the early landslide and research performed.