Abstract:
This research examines the case of landslide monitoring and how energy-autonomous Wireless
Sensor Networks (WSN) integrated with Machine Learning (ML) technologies enhanced
achievement of implementation attributes of accuracy and efficiency gains. These include the
ability of constantly generating power through solar energy, and the ability of a ML algorithm
to predict since this is the basic building block of this more encompassing architecture. Hence,
the main objective is to explore the traditional and the new models of landslide monitoring,
their problems that might be encountered and the ways of early recognition and prediction. In
the context of the study, a high-level self-operating autonomous system is proposed to for
efficient monitoring of landslides using solar energy powered WSNs and ML techniques. In
proposed approach passive networking technique is used to enhance the operation and
longevity of the nodes. Passive networks have led to a limited energy consumption which has
a figure of 69.76% as compared to the 100 % that is expected with traditional methodologies.
Different types of ML models were employed to obtain more realistic outcomes, and Gradient
Boosting Machines (GBM) was more effective than the other ML models used in this thesis.
To enhance the results of accuracy, precision, recall, F1 score and Matthews Correlation
Coefficient (MCC) a series of techniques like ensemble learning, feature scaling, hyper
parameter tuning were applied on the GBM. However, it was only the Chi-square that
significantly enhanced the findings; the percentage stood at 86. 42% accuracy, 89. 23%
precision, 85. 93% recall, F1 score of 87.55% and MCC value of 72.69%. In further
development of the integrated defining methodologies, more attempts at ML models along with
optimization strategies should be researched. In enhancing the practical use of the system this
proposed model needs to take place in varied geographical and environmental contexts to
increase its robustness as well as its applications.
