# Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
###### Abstract  
In recent years, several strategies have been introduced to enhance early warning systems and lower the risk of rock-falls. In this regard, this paper introduces a deep learning- and IoT-based framework for rock-fall early warning, devoted to reducing rock-fall risk with high accuracy. In this framework, the prediction accuracy was augmented by eliminating the uncertainties and confusion plaguing the prediction model. In order to achieve augmented prediction accuracy, this framework fused prediction model-based deep learning with a detection model-based Internet of Things. This study utilized parameters, namely, overall prediction performance measures based on a confusion matrix, to assess the performance of the framework in addition to its ability to reduce the risk. The result indicates an increase in prediction model accuracy from 86% to 98.8%. In addition, the framework reduced the risk probability from 1.51 \(\times\) 10\({}^{-3}\) to 8.57 \(\times\) 10\({}^{-9}\). Our findings demonstrate the high prediction accuracy of the framework, which also offers a reliable decision-making mechanism for providing early warning and reducing the potential hazards of rock falls. rock-fall risk; Internet of Things IoT; deep learning; early warning +
Footnote †: journal: Applied  
0000-0001-997X/0000-0001-997  
and determine how the rock's effect on the surface was estimated [4]. A micro-seismic approach was introduced to identify rock-fall occurrences in another investigation [5; 6; 7]. Although these methods are excellent at detecting rock-fall occurrences, doing so requires a large number of seismic sensors. Through the use of micro-electromechanical and micro-seismic networks, new methods to circumvent micro-seismic limits have been developed. Camera-based monitoring techniques have recently been used to monitor and track fallen rocks in real time. More recently, the development of photogrammetric monitoring systems for rock-fall monitoring purposes has received increased attention in the scientific literature [8; 9; 10; 11; 12]. A test version of an artificial intelligence camera was used to track and monitor falling rocks in real-time [13]. The camera has outperformed many technologies, even micro-seismic networks, regarding its capability to monitor many rocks simultaneously. Seismic and camera-based monitoring systems are used to identify falling rocks at the time of impact; nevertheless, they must be sufficiently effective to alert vehicles to the risk of rock fall before it happens. Unfortunately, this technology responds to events after causing severe damage to the road and pedestrians. In order to move around the limitations of monitoring approaches, rock-fall occurrences must be predicted. Recently, excellent models to forecast rock-fall dangers have been created using machine learning technology. Various machine learning techniques, including logistic regression, have been utilized for predicting rock falls [14], as support vector machines (SVM) [15; 16]. Another approach [17] developed a tool for predicting the spatiotemporal distribution of rock fall using artificial neural networks and linear regression. The rock-fall risk was assessed using several approaches, such as a hybrid early warning system for rock-fall risk reduction [18]; this system uses three models to predict the likelihood of rock falls: a logistic regression model, a computer vision model, and a hybrid risk-reduction model, which also provides early warning and hazard level classification. Because the model was created using insufficient historical data about a particular location, the current prediction approaches are ineffective at reducing the rock-fall risk in real-time. Although this system contributed to reducing the risk of rock falls, the prediction process still needs to be more accurate and uncertain because it inherited previous models' limitations. In order to overcome the constraints of all earlier models, offering an accurate prediction is necessary. This study proposed a framework to reduce rock-fall risk.