Abstract
In the current era of computing, communication, and technology, hydrological, metrological, and geographical parameters supported by sensor-based systems are available to detect, monitor, and analyze natural disasters like landslides. The landslide-related information from the study area is collected in offline mode through site visits. This process of collecting data in offline mode may cause delays in prediction and proactive decision-making in real-time mode. Although landslides cannot be prevented, their impact on human life and the environment can be reduced through real-time monitoring and prediction using IoT, Cloud, and Machine Learning Technologies. This manuscript aims to present a robust, real-time monitoring system that can minimize losses and save lives. The proposed model utilizes Internet of Things (IoT) technology integrated with cloud services to monitor and analyze landslides in a specific study area. The real-time monitoring system relies on three types of parameters: hydrological, meteorological, and geographical. These parameters are used to collect and store real-time information in an IoT cloud platform. The IoT cloud information is fetched on the LANDSLIDE MONITOR application for proactive decisions. To predict landslide events in areas prone to disasters, supervised learning classifiers were employed. The prediction analysis takes into account meteorological, hydrological, and geographical factors. The effectiveness of the proposed real-time landslide monitoring system was tested and evaluated in the Varunavat hills of the Uttarkashi District in Uttarakhand, India. The performance of the system was assessed by analyzing the accuracy of the model at different levels. The major focus of the developed system includes real-time data storage of landslide-prone areas in the IoT cloud, predictive modeling, and lastly the real-time landslide responses on LANDSLIDE MONITOR. The present landslide monitoring system uses long short-term memory networks (LSTM) and gated recurrent units (GRU) for predictive modeling of the landslide events in the study area. Hence the unique contribution of the work includes the technologies integration and real-time data collection from the study area and stored in the IoT cloud. The novel contribution of the work also includes the predictive modeling of landslide events using LSTM and GRU, and study area people awareness using LANDSLIDE MONITOR for level of risk from landslide events. The accuracy rates for the alert classes, ‘no threat’, ‘mild threat’, and ‘high threat’ events are 96.83%, 97.07%, and 98.56%, respectively. With a mean F1 score of 0.96 across the three classes of landslide occurrences, the proposed system demonstrates a high level of accuracy.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The dataset used in this study is available to the authors and will be provided upon request.
References
Angeli MG, Pasuto A, Silvano S (2000) A critical review of landslide monitoring experiences. Eng Geol 55(3):133–147. https://doi.org/10.1016/S0013-7952(99)00122-2
Arif Hakim WM, Ramli AF, Basarudin H, Abu MA, Ahmad I (2020) WSN and IoT based landslide monitoring system. Test Eng Manag 83(May):10926–10932
AX-0315 Accelerometer Datasheet (2023) https://product.tdk.com/system/files/dam/doc/product/sensor/mortion-inertial/accelero/data_sheet/datasheet-axo315-tronics.pdf. Accessed June 2023
Bagwari S, Gehlot A, Singh R, Priyadarshi N, Khan B (2021) Low-cost sensor-based and LoRaWAN opportunities for landslide monitoring systems on IoT platform: a review. IEEE Access 10:7107–7127
Bagwari S, Roy A, Gehlot A, Singh R, Priyadarshi N, Khan B (2022) LoRa based metrics evaluation for real-time landslide monitoring on IoT platform. IEEE Access 10:46392–46407. https://doi.org/10.1109/ACCESS.2022.3169797
Bahmanyar D, Razmjooy N, Mirjalili S (2022) Multi-objective scheduling of IoT-enabled smart homes for energy management based on Arithmetic Optimization Algorithm: a node-RED and NodeMCU module-based technique. Knowl-Based Syst 247:108762. https://doi.org/10.1016/j.knosys.2022.108762
Barcelo-Ordinas JM, Doudou M, Garcia-Vidal J, Badache N (2019) Self-calibration methods for uncontrolled environments in sensor networks: a reference survey. Ad Hoc Netw 88:142–159
Boonchieng E, Chieochan O, Saokaew A (2017) Smart farm: applying the use of NodeMCU, IOT, NETPIE and LINE API for a lingzhi mushroom farm in Thailand. IEICE Trans Commun. https://doi.org/10.1587/transcom.2017ITI0002
Cappelli I et al (2021) Battery-less HF RFID sensor tag for soil moisture measurements. IEEE Trans Instrum Meas 70:1–13. https://doi.org/10.1109/TIM.2020.3036061. (Art no. 9504113)
Casagli N, Intrieri E, Tofani V, Gigli G, Raspini F (2023) Landslide detection, monitoring and prediction with remote-sensing techniques. Nat Revi Earth Environ 4(1):51–64
Chae BG, Park HJ, Catani F, Simoni A, Berti M (2017) Landslide prediction, monitoring and early warning: a concise review of state-of-the-art. Geosci J 21(6):1033–1070. https://doi.org/10.1007/s12303-017-0034-4
Dadi V, Peravali S (2020) Optimization of light-dependent resistor sensor for the application of solar energy tracking system. SN Appl Sci 2(9):1499
Das S, Adak S, Maity S (2021) IoT-based early warning system for landslide disaster management using machine learning approach. Int J Electric Comput Eng (IJECE) 11(2):1592–1603. https://doi.org/10.11591/ijece.v11i2.pp1592-1603
Desnanjaya G, Ariana A, Nugraha M et al (2022) Room monitoring uses ESP-12E based DHT22 and BH1750 sensors. J Robot Control (JRC). https://doi.org/10.18196/jrc.v3i2.11023
Dey S, Saha S, Bhattacharya S (2021) Integrating IoT and machine learning techniques for landslide susceptibility mapping in Darjeeling Himalaya, India. IEEE Access 9:104705–104717. https://doi.org/10.1109/ACCESS.2021.3108297
Dhanagopal R, Muthukumar B (2021) A model for low power, high speed and energy efficient early landslide detection system using IoT. Wirel Pers Commun 117(4):2713–2728
Filipe A, Gaff V, Unrau W, Schreiber-Prillwitz W, David Y (2021) High performance MEMS accelerometer and gyro with a unique SMD and digital interface. In: 2021 DGON Inertial Sensors and Systems (ISS), Braunschweig, Germany, 2021, pp 1–19, https://doi.org/10.1109/ISS52949.2021.9619814
Gidon JS, Borah J, Sahoo S, Majumdar S, Fujita M (2023) Bi-Directional LSTM model for accurate and real-time landslide detection: a case study in Mawiongrim, Meghalaya, India. IEEE Internet of Things J 11:3792–3800
Greff K, Srivastava RK, Koutník J, Steunebrink BR, Schmidhuber J (2017) LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst 28(10):2222–2232. https://doi.org/10.1109/TNNLS.2016.2582924
Gupta AK, Mukherjee MK (2022) Evaluating road-cut slope stability using newly proposed stability charts and rock microstructure: an example from Dharasu-Uttarkashi Roadway, Lesser Himalayas, India. Rock Mech Rock Eng 55:3959–3995. https://doi.org/10.1007/s00603-022-02846-3
Hadi A, Abdullah MZ (2022) Web and IoT-based hospital location determination with criteria weight analysis. Bull Electr Eng Inform 11(1):386–395. https://doi.org/10.11591/eei.v11i1.3214
Hakim WMA, Ramli AF, Basarudin H, Abu MA, Ahmad I (2020) WSN and IoT based landslide monitoring system. Test Eng Manag 83:10926–10932
Haq MA (2022a) SMOTEDNN: a novel model for air pollution forecasting and AQI classification. Comput Mater Continua 71(1):1403–1425
Haq MA (2022b) CNN-based automated weed detection system using UAV imagery. Comput Syst Sci Eng 42(2):837–849
Haq MA (2023) (2023) DBoTPM: a deep neural network-based botnet prediction model. Electronics 12(5):1159. https://doi.org/10.3390/electronics12051159
Haq MA, Khan MAR (2022) DNNBOT: deep neural network-based botnet detection and classification. Comput Mater Continua 71(1):1729–1750
Haq MA, Khan MAR, AL-Harbi T (2022) Development of PCCNN-based network intrusion detection system for edge computing. Comput Mater Continua 71(1):1769–1788
Haq MA, Hadj Hassine SB, Malebary SJ, Othman HA, Tag-Eldin EM (2023a) 3D-CNNHSR: a 3-dimensional convolutional neural network for hyperspectral super-resolution. Comput Syst Sci Eng 47(2):2689–2705
Haq MA, Ahmed A, Gyani J (2023b) Implementation of CNN for plant identification using UAV imagery. Int J Adv Comput Sci Appl (IJACSA). https://doi.org/10.14569/IJACSA.2023.0140441
Haq MA, Khan I, Ahmed A, Eldin SM, Alshehri A, Ghamry NA (2023c) DCNNBT: a novel deep convolution neural network-based brain tumor classification model. Fractals 31(06):2340102. https://doi.org/10.1142/S0218348X23401023
Ji J, Shi W, Cui X (2021) Integration of Internet of Things and machine learning for landslide prediction: a review. IEEE Access 9:49150–49161. https://doi.org/10.1109/ACCESS.2021.3063655
Joshi A, Kanungo DP, Panigrahi RK (2023) WSN-based smart landslide monitoring device. IEEE Trans Instrum Meas 72:1–12
Khan R, Yousaf S, Haseeb A, Uddin MI (2021) Exploring a design of landslide monitoring system. Complexity. https://doi.org/10.1155/2021/5552417
Kumar R, Anbalagan R (2016) Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. J Geol Soc India 87(3):271–286. https://doi.org/10.1007/s12594-016-0395-8
Lau YM, Wang KL, Wang YH, Yiu WH, Ooi GH, Tan PS, Chen CW (2023) Monitoring of rainfall-induced landslides at Songmao and Lushan, Taiwan, using IoT and big data-based monitoring system. Landslides 20(2):271–296
Lin YB, Lin YW, Lin JY, Hung HN (2019) SensorTalk: An IoT device failure detection and calibration mechanism for smart farming. Sensors 19(21):4788
Liu Y, Luo Q, Zhou Y (2022) Deep Learning-Enabled Fusion to Bridge GPS Outages for INS/GPS Integrated Navigation. IEEE Sens J 22(9):8974–8985. https://doi.org/10.1109/JSEN.2022.3155166
Maity S, Adak S, Das S (2021) IoT-based Landslide Early Warning System with Machine Learning Techniques for Safe Disaster Management. In: 2021 2nd International Conference on computer science, engineering and applications (ICCSEA), pp 1–6. IEEE. https://doi.org/10.1109/ICCSEA53026.2021.9469667
Martha TR, Roy P, Govindharaj KB, Kumar KV, Diwakar PG, Dadhwal VK (2015) Landslides triggered by the June 2013 extreme rainfall event in parts of Uttarakhand state, India. Landslides 12(1):135–146. https://doi.org/10.1007/s10346-014-0540-7
Patel A, Devaki P (2019) Survey on NodeMCU and Raspberry pi: IoT. Int Res J Eng Technol 6(): 5101-5105. [Online]. Available: www.irjet.net. Accessed June 2023
Pham BT, Prakash I, Jaafari A, Bui DT (2018) Spatial prediction of rainfall-induced landslides using aggregating one-dependence estimators classifier. J Indian Soc Remote Sens 46(9):1457–1470. https://doi.org/10.1007/s12524-018-0791-1
Polymeni S, Athanasakis E, Spanos G, Votis K, Tzovaras D (2022) IoT-based prediction models in the environmental context: a systematic Literature Review. Internet of Things 20:100612
Rachman NF, Sunardi S, Aghastya A, Wirawan WA, Putri NDO (2023) Simulation of early warning system in landslides and flooding with IoT. AIP Conf Proc 2592(1):060008. https://doi.org/10.1063/5.0114918
Ramesh MV, Vasudevan N (2012) The deployment of deep-earth sensor probes for landslide detection. Landslides 9(4):457–474. https://doi.org/10.1007/s10346-011-0300-x
Ramesh VM, Vasudevan N (2012) The deployment of deep-earth sensor probes for landslide detection. Landslides 9:457–474
Saha S, Saha A, Hembram TK, Mandal K, Sarkar R, Bhardwaj D (2022) Prediction of spatial landslide susceptibility applying the novel ensembles of CNN, GLM and random forest in the Indian Himalayan region. Stoch Env Res Risk Assess 36(10):3597–3616
Sajith PM, Nair V, Suresh VP, Madhu A (2020) IoT based landslide disaster management system. In Second International Conference on computer networks and communication technologies: ICCNCT 2019, pp 660–667. Springer International Publishing.
Sarkar S, Kanungo D, Chauhan P (2011) Varunavat landslide disaster in Uttarkashi, Garhwal Himalaya, India. Q J Eng Geol Hydrogeol 44:17–22. https://doi.org/10.1144/1470-9236/09-029
Savvaidis PD (2003) Existing landslide monitoring systems and technique. From Stars to Earth Cult pp 242–258
Sensirion Soil Moisture Sensor (SH-10) Datasheet (2020) https://www.sparkfun.com/datasheets/Sensors/SHT1x_datasheet.pdf. Accessed June 2023
Shao Y, Wang L, Wang S, Yang S (2021) Real-time monitoring and prediction of landslides using IoT and machine learning: a review. Comput Geosci 156:104862. https://doi.org/10.1016/j.cageo.2021.104862
Sharma K, Anand D, Sabharwal M, Tiwari PK, Cheikhrouhou O, Frikha T (2021) A disaster management framework using internet of things-based interconnected devices. Math Probl Eng. https://doi.org/10.1155/2021/9916440
Suresh MD, Geraldine GG, Shalini S (2021) Landslide detection. Int J Adv Res Sci Commun Technol 5(1):97–107. https://doi.org/10.48175/ijarsct-1105
Tao Q, Tang B (2023) A high-linearity closed-loop capacitive micro-accelerometer based on ring-diode capacitance detection. Sensors 23(3):1568. https://doi.org/10.3390/s23031568
ThingSpeak Dashboard for Landslide Monitoring (2023) Link: https://thingspeak.com/channels/1931773/private_show. Accessed June 2023
Thirugnanam H, Uhlemann S, Reghunadh R, Ramesh MV, Rangan VP (2022a) Review of landslide monitoring techniques with IoT integration opportunities. IEEE J Sel Top Appl Earth Observ Remote Sens 15:5317–5338
Thirugnanam H, Uhlemann S, Reghunadh R, Ramesh MV, Rangan VP (2022b) Review of landslide monitoring techniques with IoT integration opportunities. IEEE J Sel Top Appl Earth Obs Remote Sens 15(January):5317–5338. https://doi.org/10.1109/JSTARS.2022.3183684
Varma Byrraju S (2019) Landslide detection using remote sensing methods a review of current techniques. 2019, [Online]. https://scholarcommons.sc.edu/etd. Accessed June 2023
Wang S, Yang K, Shi Y, Yang F, Zhang H, Ma Y (2023) Prediction of over-the-horizon electromagnetic wave propagation in evaporation ducts based on the gated recurrent unit network model. IEEE Trans Antennas Propag 71(4):3485–3496. https://doi.org/10.1109/TAP.2023.3240998
Yong Yu, Si X, Changhua Hu (2019) A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput 31(7):1235–1270. https://doi.org/10.1162/neco_a_01199
Yuliza E, Habil H, Munir MM, Irsyam M, Abdullah M (2016) Study of soil moisture sensor for landslide early warning system: experiment in laboratory scale. J Phys Conf Ser 739(1):012034
Zhang Yg, Tang J, He Zy et al (2021) A novel displacement prediction method using gated recurrent unit model with time series analysis in the Erdaohe landslide. Nat Hazards 105:783–813. https://doi.org/10.1007/s11069-020-04337-6
Zhou L, Wu X, Xu Z, Fujita H (2018) Emergency decision making for natural disasters: an overview. Int J Disaster Risk Reduct 27(September):567–576. https://doi.org/10.1016/j.ijdrr.2017.09.037
Funding
No funding was received for this research.
Author information
Authors and Affiliations
Contributions
Respected, Editor, Authors have contributed equally in every aspect, Including IoT, ML, and Technical Writing of the manscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
The authors have read, understood, and complied as applicable with the statement on "Ethical responsibilities of Authors, as found in the Instructions for Authors, and are aware that with minor exceptions, no changes can be made to authorship once the paper is submitted.
Informed consent
Not applicable (no human or animal participants were involved in the research).
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Rawat, P.S., Barthwal, A. LANDSLIDE MONITOR: a real-time landslide monitoring system. Environ Earth Sci 83, 226 (2024). https://doi.org/10.1007/s12665-024-11526-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-024-11526-0