Monitoring Slope Movement and Soil Hydrologic Behavior Using IoT and AI Technologies: A Systematic Review
Abstract
:1. Introduction
2. Types of Landslides and Influencing Factors
2.1. Various Modes of Landslides or Slope Failure
2.2. Factors Affecting Slope Failure
3. Unsaturated Soil Hydrology and Slope Stability
3.1. The Infiltration Hydrology in Slope
3.2. The Governing Equation of Unsaturated Hydrology of Slope
3.3. Shear Strength of Unsaturated Soil
3.4. Early Warning System for Rainfall-Induced Slope Failure
4. Traditional Slope Movement Detection Systems
- Environmental Conditions: Once installed, external factors can influence their readings and affect the accuracy of detecting slope movements.
- Integration with modern technologies: Traditional and contemporary systems often generate data in different formats, making integration complex.
- Continuous Monitoring: High-resolution tiltmeters can continuously monitor internal deformations within a landslide body. This capability is crucial for understanding landslides’ dynamics over short and long periods.
- Decomposition of Movement: The tiltmeters allow for the decomposition of landslide movement into different components, such as long-term trends and seasonal variations. This detailed decomposition helps in better understanding the factors influencing landslide movements.
- Influence of Environmental Factors: Tilt measurements can effectively correlate the impact of environmental factors like rainfall, groundwater levels, and temperature changes on landslide behavior. This is vital for developing predictive models and early warning systems.
- Non-linear Responses: While tilt measurements provide valuable data, the landslide response to environmental triggers is not always linear. This suggests the complexity of the landslide’s response mechanisms, necessitating sophisticated analysis techniques to interpret the data accurately.
5. Soil Hydrological Monitoring Systems Using Modern Technologies
5.1. IoT and WSNs
- Sensor Deployment: As shown in Figure 3, WSN comprises multiple sensors deployed across potentially unstable slopes or areas prone to landslides. These sensors are strategically placed to monitor various environmental and geological parameters, such as soil moisture, pressure changes, and vibrations, critical indicators of potential landslides.
- Data Collection: IoT devices with sensors continuously collect data in real time. This capability is central to the system’s effectiveness, ensuring that the data on critical parameters are updated constantly and available for immediate analysis.
- Connectivity and Communication: The sensors in the WSN are interconnected through a robust network that supports efficient data transmission to a central processing unit or server. IoT technology facilitates this connectivity, ensuring seamless communication across the network. Data from sensors are transmitted wirelessly, minimizing the need for extensive physical infrastructure and enabling easier network scaling.
- Data Processing and Analysis: Once collected, the data are transmitted to centralized systems, where they are processed and analyzed using advanced algorithms. IoT platforms integrate these data and enable sophisticated analytical processes to interpret the information [183], predict potential landslide activities, and determine the immediacy and severity of the risks.
- Scalability and Flexibility: The system is designed to be scalable and flexible, allowing for the addition or reconfiguration of sensors as monitoring needs evolve or as new technologies become available. This adaptability is crucial for accommodating different environmental conditions and expanding the system’s coverage as required.
- Disaster Response Coordination: IoT not only supports data collection and analysis but also plays a crucial role in disaster response coordination. The system can trigger alerts and communicate potential landslide warnings to local authorities and residents, facilitating timely evacuations and mitigations.
- Reliability and Cost-Effectiveness: WSN ensures the reliability of the data collected, which is crucial for the accuracy of landslide predictions and warnings. The cost-effectiveness of the network, coupled with low power consumption, makes it viable for long-term monitoring without requiring frequent maintenance or high operational costs.
- Real-Time Monitoring and Alert Systems: IoT devices continuously monitor geological changes and environmental variables. Data on soil movement, moisture content, and other crucial factors that point to the possibility of landslide activity are gathered by sensors buried in the ground or visible above it. Afterward, real-time alerts and cautions are generated using these data, allowing quick action to reduce risks.
- Predictive Maintenance: IoT systems can forecast possible landslide events ahead of time by evaluating the data gathered. Preventive measures are made more accessible by machine learning algorithms that analyze historical and current data to find patterns or abnormalities before a landslide.
- Environmental Monitoring: IoT sensors track several environmental variables, including changes in terrain, soil water content, and the amount of rainfall that might cause landslides. This thorough monitoring aids in determining an area’s susceptibility to landslides and putting suitable environmental management techniques into place.
- Perception Layer: This is the physical layer where IoT sensors and devices collect environmental and geophysical data. The interface between the physical world and the digital IoT system is crucial in acquiring accurate and timely data.
- Network Layer: The collected data are transmitted through this layer, which involves communication technologies and protocols to ensure that data are sent from sensors to processing units or cloud systems securely and efficiently.
- Middleware Layer: Acts as a bridge between the hardware and application layers. It manages device connectivity, data storage, and processing. It also includes tools and platforms that provide data analytics capabilities, essential for interpreting the vast amounts of data gathered by sensors.
- Application Layer: This layer is where specific applications that utilize the processed data are developed [183]. Landslide monitoring includes visualization tools, alert systems, and decision support systems that help stakeholders make informed decisions.
- Business Layer: Overseeing the entire IoT setup, this layer focuses on integrating IoT solutions into existing business and operational processes. It addresses strategic decision making, monetization, and scalability issues, ensuring that the IoT solutions align with organizational goals and contribute effectively to landslide risk management.
- Type of Movement: Landslide movements are categorized into types such as fall, topple, slide, spread, and flow, each characterized by different dynamics and influenced by varying geological and environmental factors. Understanding these types helps in selecting the appropriate monitoring techniques for each scenario. For example, falls involve the sudden detachment and downward movement of rock or earth, while flows are characterized by the rapid movement of debris or mud down a slope.
- Landslide Monitoring Parameters: There is a range of monitoring parameters that are crucial for assessing landslide risks, including meteorological conditions (e.g., rainfall, geological parameters, soil composition, and structure), hydrogeological factors (e.g., water pressure and saturation), physical parameters (e.g., displacement and velocity), and geophysical parameters that help in understanding the subsurface characteristics. Effective monitoring of these parameters can aid in predicting landslide occurrences and mitigating their impacts.
- Monitoring Aspects: This topic is addressed through different monitoring techniques and their spatial and temporal resolution capabilities. It is essential to integrate real-time and near-real-time monitoring systems to enhance the responsiveness of landslide warning and intervention systems.
5.2. Remote Sensing Technologies (RSTs)
- Environmental Monitoring: Tracking changes in land use, vegetation cover, and water bodies.
- Disaster Management: Detecting and monitoring natural disasters such as earthquakes, floods, hurricanes, and landslides.
- Urban Planning: Assisting in city planning and infrastructure development.
- Agriculture: Monitoring crop health, soil moisture, and predicting yields.
- Climate Change Studies: Observing and analyzing changes in climate patterns and their impacts on the environment.
- Geological Mapping: Identifying geological features and mineral exploration.
- Multispectral Satellite Sensors: Instruments that capture data in multiple wavelengths of light, such as Landsat, SPOT, and Sentinel-2.
- Synthetic Aperture Radar (SAR): Active sensors that use microwave signals to measure ground surface deformations, including Sentinel-1 and TerraSAR-X.
- Interferometric Synthetic Aperture Radar (InSAR): A technique that uses SAR data from different time points to detect ground movement.
- Light Detection and Ranging (LiDAR): Ground-based or airborne laser scanning systems that create detailed 3D models of the terrain.
- Ground-Based Interferometric Radar (GBInSAR): Devices that accurately measure ground displacement and deformation and are used for real-time monitoring and early warning systems.
- Doppler Radar: Used for detecting rapid movements such as rockfalls and debris flows.
- ES (Earth Slide): Movement of earth material, typically slow-moving, with velocities ranging from less than 1.6 m per year to 1.8 m per hour.
- RS (Rock Slide): Downward and outward movement of rock material, with velocities from less than 1.6 m per year to more than 1.8 m per hour.
- EF (Earth Flow): Flow of fine-grained earth materials, with velocities from 1.6 m per year to 1.8 m per hour.
- LS (Lateral Spread): Horizontal movement of soil or rock, usually on gentle slopes, with velocities from less than 1.6 m per year to 1.8 m per hour.
- SL (Shallow Landslide): Movement of soil and debris near the surface, with velocities from less than 1.6 m per year to more than 1.8 m per hour.
- DF (Debris Flow): Rapid flow of a mixture of water, soil, and rock, with velocities greater than 1.8 m per hour.
- Space-Borne Platforms
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- InSAR: Utilized for early identification and monitoring of active landslides. The advantage lies in its ability to provide high-accuracy measurements regardless of weather conditions. InSAR has been instrumental in identifying deformation in a wide range of areas.
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- Optical Sensors: Used for geological and topographic investigations, boundary mapping, and macro deformation analysis. Space-borne optical sensors provide wide-area coverage with high spatial resolution, making them suitable for mapping large-scale landslides.
- Air-Borne Platforms
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- Optical Cameras: Air-borne optical sensors are used for detailed surveys of landslides, particularly in sparsely vegetated areas. UAVs (Unmanned Aerial Vehicles) are commonly employed to capture high-resolution images that can be used to create 3D models of the terrain.
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- LiDAR: Air-borne LiDAR systems are effective in densely vegetated areas. They provide detailed 3D information by filtering out vegetation to reveal the actual ground surface.
- Ground-Based Platforms
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- Ground-Based Synthetic Aperture Radar (GBSAR): Used for real-time monitoring of specific landslides, providing high-resolution data on ground displacement and deformation.
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- Laser Scanners: Ground-based laser scanners (terrestrial LiDAR) are used for detailed surveys of steep slopes and rock faces, offering high-resolution data for small areas.
- Geological Surveys: UAVs are used for detailed mapping and characterizing landslides, identifying features such as scarps, fissures, and micro-landforms.
- Dynamic Monitoring: UAVs monitor temporal and spatial changes in landslide areas, providing data on surface deformation, volume estimation, and crack detection.
- Emergency Response: UAVs play a crucial role in post-landslide scenarios, aiding search and rescue operations and providing real-time data for decision-making.
- RGB Cameras: Capture high-resolution visible light images, useful for mapping and identifying morphological features shown in Figure 6a.
- Multi-Spectral Cameras: Record data across various spectral bands, enabling detailed spectral analysis for vegetation and geological studies shown in Figure 6b.
- Thermal IR Cameras: Detect emitted thermal radiation, identifying temperature variations associated with geological instability shown in Figure 6c.
- SAR Sensors: Use radar to produce high-resolution images regardless of weather conditions, allowing for precise monitoring of surface changes and deformations shown in Figure 6d.
- LiDAR Sensors: Emit laser pulses to measure distances and create detailed 3D models of the terrain, useful for mapping and monitoring surface deformations shown in Figure 6e.
5.3. Machine Learning
5.3.1. ML Approaches for Landslide Detection
- Supervised Learning: Supervised learning algorithms are extensively used in landslide detection due to their ability to classify and predict based on labeled datasets. Logistic regression (LR), Support Vector Machines (SVM), and Random Forests (RF) [190] are prominent examples. Logistic regression, a statistical model, helps in predicting the probability of landslides by analyzing the relationship between dependent and independent variables. SVM, known for its effectiveness in high-dimensional spaces, classifies landslide-prone areas by finding the hyperplane that best separates the data into different classes [193]. Random forests, an ensemble learning method, enhance prediction accuracy by constructing multiple decision trees and merging their results. These algorithms are crucial in analyzing various factors such as topography, geology, and rainfall data to identify and predict landslide occurrences [191,192,194].
- Unsupervised Learning: Unsupervised learning techniques, particularly clustering algorithms, play a significant role in identifying patterns in landslide-prone areas without needing labeled datasets [195]. These algorithms, such as K-means clustering and hierarchical clustering, group similar data points based on their characteristics. In landslide detection, clustering helps recognize natural groupings within the data, such as regions with similar geological features or rainfall patterns. By analyzing these clusters, researchers can identify areas that share common characteristics with previously known landslide-prone zones, thus improving the understanding and prediction of potential landslide sites [188].
- Deep Learning: Deep learning techniques, especially convolutional neural networks (CNNs) [196], have successfully handled large and complex datasets for landslide detection. CNNs can extract features from high-dimensional data, such as satellite images and digital terrain models (DTMs). These networks have multiple layers that automatically learn to identify patterns and features relevant to landslide detection. CNNs excel in processing spatial data, making them ideal for analyzing terrain changes and identifying landslide scars. The ability of deep learning models to handle vast amounts of data and learn intricate patterns makes them powerful tools for enhancing the accuracy and efficiency of landslide detection and prediction.
5.3.2. Data Collection and Preprocessing
- Data Types: For machine learning (ML) applications in landslide detection, various data types are crucial. Topographic data include digital elevation models (DEMs) and slope gradient maps [175,197], which provide information about the terrain’s shape and steepness. Geological data comprise soil types, rock formations, and fault lines, essential for understanding the subsurface conditions. Meteorological data encompasses rainfall, temperature, and humidity, key factors influencing landslide triggers. These data types collectively enable comprehensive analysis and accurate landslide prediction [188].
- Data Integration: Integrating various data sources into a cohesive geodatabase is essential for effective analysis [177,198]. This involves compiling data from multiple sensors and surveys into a unified system. For example, topographic data from DEMs can be combined with geological maps and meteorological records to form an integrated geodatabase. This geodatabase allows for efficient querying and analysis, enabling the identification of correlations and patterns relevant to landslide occurrences [189,193].
- Data Cleaning: Data cleaning involves removing duplicates, correcting errors, and handling missing values to ensure the dataset’s integrity [195].
5.3.3. Feature Engineering
- Key Features: Identifying significant features is crucial for the performance of landslide detection models. Slope gradient is a primary feature, as steeper slopes are more prone to landslides. Aspect, which indicates the slope’s orientation, affects moisture retention and sunlight exposure, influencing landslide risk. Curvature helps identify concave or convex surfaces, which indicate potential accumulation or dispersion of materials. The Topographic Wetness Index (TWI) measures the potential water accumulation in the terrain, a critical factor in landslide initiation. These features are fundamental in capturing the physical and environmental conditions leading to landslides [191].
- Feature Importance and Selection Methods: Assessing and selecting important features involves several methods. Correlation analysis identifies the relationships between variables, helping select features that significantly impact landslide occurrences. Principal Component Analysis (PCA) reduces the dataset’s dimensionality by transforming it into uncorrelated variables, retaining most of the variance. Feature importance rankings from tree-based models, such as random forests, provide insights into which features contribute most to the model’s predictions. These methods ensure the most relevant features are used, enhancing the model’s efficiency and accuracy [199].
5.3.4. Model Training and Evaluation
- Training Techniques: Training ML models for landslide detection involves various techniques. Cross-validation splits the data into training and validation sets multiple times to ensure the model’s robustness. Hyperparameter tuning optimizes the model’s parameters to improve performance. Ensemble methods, such as bagging and boosting, combine multiple models to enhance prediction accuracy and reduce overfitting. These approaches ensure that the ML models are well-trained and capable of generalizing to new data [175].
- Evaluation Metrics: Model performance is evaluated using several metrics. Accuracy measures the proportion of correctly predicted instances. Precision indicates the proportion of true positive predictions among all positive predictions. Recall measures the ability to identify all actual positives. The F1 score balances precision and recall, providing a single metric for model performance. The Area Under the ROC Curve (AUC-ROC) evaluates the model’s ability to distinguish between classes, with higher values indicating better performance. These metrics comprehensively evaluate the model’s effectiveness in landslide detection [174].
5.4. Geographic Information Systems (GIS) and Global Positioning Systems (GPS)
- GIS Techniques for Landslide Monitoring
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- Integration of Spatial Data: GIS is instrumental in integrating various spatial data types, such as topography, geology, hydrology, and vegetation cover. This integration helps understand landslides’ spatial patterns and relationships, enabling better risk assessment and management [202].
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- Temporal Analysis: GIS facilitates temporal analysis, allowing researchers to study changes over time in landslide-prone areas. This capability is essential for understanding the progression of landslides and the effectiveness of mitigation measures [176].
- GPS Techniques for Landslide Monitoring
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- High-Precision Monitoring: GPS technology provides high-precision data on ground movements. Single-frequency GPS sensors, in particular, offer a cost-effective solution for continuously monitoring landslide-prone areas, enabling near real-time data acquisition and analysis [150].
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- LEWS: GPS is integral to Landslide Early Warning Systems (LEWSs), providing critical data that can predict potential landslide events. Detecting minute displacements helps issue timely warnings to prevent damage and save lives [164].
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- Case Study—Carnic Alps: A practical example of GPS application is the monitoring system implemented in the Carnic Alps, northeastern Italy. This system comprises 12 single-frequency GPS stations and provides daily reports of landslide movements, demonstrating its effectiveness in real-world scenarios [69].
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- 3D Displacement Monitoring: GPS systems can monitor three-dimensional displacements, providing comprehensive data on the movement patterns of landslides. This capability is crucial for understanding the kinematics of landslides and implementing appropriate mitigation strategies [171].
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Method | Description |
---|---|
Electrical Resistivity (ER) | Involves measuring the electrical resistance of subsurface materials to infer moisture content and structural variations. It effectively delineates moisture variations and other hydrological dynamics within landslide-prone areas. |
Self-potential (SP) | SP monitoring detects natural electric potentials in the ground, indicating water flow paths and areas of potential weakness in the subsurface. |
Seismic Monitoring | This method records and analyzes ground vibrations to identify movements within landslide bodies. It can differentiate between different landslide movements and detect precursory seismic signals that may precede a landslide event. |
Sensor Type | Pros | Cons |
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Inclinometers |
|
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Extensometers |
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Method | Pros | Cons |
---|---|---|
Data Acquisition Units | Records precise measurements from sensors monitoring various environmental and physical parameters of landslides. | Requires setup and maintenance and may be vulnerable to environmental damage. |
Remote Telemetry | Enables data transmission over long distances using radio, satellite, or cell phone links. | There are potential interruptions in data transmission depending on signal availability and strength. |
Automated Data Processing | Processes large volumes of data quickly to provide timely updates on landslide conditions. | It is a complex setup and requires consistent calibration and validation to ensure accuracy. |
Displays of Current Conditions | It offers immediate access to monitored data online, allowing for quick response. | It requires reliable internet connections and a power supply but may not be accessible during power outages. |
Method | Description |
---|---|
Geological Mapping | Used to investigate all types of landslides, providing detailed data on the geology and structure of the terrain. |
Geotechnical Mapping | Includes using inclinometers to measure in-depth displacements and extensometers to monitor cracks and surface movements. |
Remote Sensing Techniques | Includes the use of LiDAR and radar for the detection and monitoring of earth flows and other types of movements. |
Geodetic Techniques | Employs technologies such as GNSS to monitor earth and rockslides, providing precise data on movements in real time. |
Hydrogeological Techniques | Monitors hydrological conditions such as groundwater level and pore water pressure, which can influence slope stability. |
Mapping Techniques | It includes geomorphological and engineering geological mapping, which is used for risk map production and studying the morphology and evolution of the terrain. |
Method | Description |
---|---|
Automated Total Station | Utilizes automated stations with optical systems to monitor slope movements. It can be hindered by adjacent machinery [1]. |
LiDAR | Employs Light Detection and Ranging technology to provide broad coverage and rapid update rates [169]. |
Slope Stability Radar (SSR) | Uses radar technology to continuously scan and monitor slope surface distortions, providing high-resolution measurements even in poor weather conditions [126]. |
GPS | Uses Global Positioning System technology to monitor discrete points on slopes, providing real-time data [170]. |
TDR | Time Domain Reflectometry uses electromagnetic signals to detect disturbances within slopes [171]. |
Photogrammetry | Applies photography in surveying and mapping to measure distances between points on slopes [172]. |
Microseismic Monitoring | Detects and analyzes microseisms induced by rock fracturing within the slope to anticipate potential failures. |
Crack Meters | Monitors the opening of cracks within slopes to detect movements indicative of potential slope failures [128]. |
OTDR | Optical Time Domain Reflectometry monitors fiber optic cables within the slope to detect deformations that indicate movements. |
Shape Accel Array (SAA) | Utilizes arrays of accelerometers to measure acceleration along a slope to determine movements and predict failures. |
Ground-based SAR Interferometry | Employs radar signals to detect and monitor subtle slope movements over time, allowing for early detection of potential failures. |
Wireless Sensor Networks (WSNs) | Implements networks of sensors that collect data on various physical parameters like moisture, pressure, and movement to continuously monitor and analyze slope stability [173]. |
Characteristics | Advantages | Disadvantages | Applicability | |
---|---|---|---|---|
WSN | Real-time monitoring and site-specific monitoring. It works on site-specific Early Warning Systems (EWSs). | High speed and resolution, Continuous technical development in processor, Communication, Low-power, Usage of embedded computing | Security against hackers, Security against vandalism | Directly applicable by programming intelligent algorithms in the sensor node. |
IoT | Real-time monitoring, Site-specific monitoring, Integration with cloud computing for data aggregation and analysis. It works on site-specific EWSs, national-level EWSs with integration with broader disaster management systems. | Enables remote monitoring, Scalability, Integration with various data platforms and sensors, Real-time data processing and alerts. | Dependence on network connectivity, Potential issues with data privacy and security, Maintenance of sensors and network infrastructure. | Fully integrated with cloud platforms, data analytics, and machine learning for predictive analysis. |
Technique | Detection | Monitoring | Prediction |
---|---|---|---|
MTInSAR | ES, RS, EF, LS, SL | ES, RS, EF, LS, SL | ES, RS |
Multispectral satellite sensors | ES, RS, EF, LS, SL, DF | ES, RS, EF, LS, SL | ES, RS, EF |
Ground-based interferometry | ES, RS, LS | ES, RS, EF, LS, SL | ES, RS, EF |
Doppler radar | RF | RF, DF, SL | — |
Lidar | ES, RS, EF, LS, RF | ES, RS, EF, LS | — |
Technology | Pros | Cons |
---|---|---|
Satellite Imagery | Wide area coverage, frequent revisit times, and multi-spectral data. | Lower spatial resolution and potential cloud cover issues. |
Aerial Photography | High spatial resolution and flexible deployment. | Limited by weather conditions and can be costly for large areas. |
LiDAR | High accuracy can penetrate vegetation and generate detailed 3D models. | Expensive and requires complex data processing. |
Synthetic Aperture Radar (SAR) | All-weather capability and can detect ground deformation. | Complex data interpretation and lower spatial resolution compared to optical sensors. |
Ground-Based Remote Sensing | High spatial and temporal resolution for specific sites. | Limited coverage area and may require more labor-intensive deployment. |
Pros | Cons |
---|---|
High Accuracy: ML algorithms and deep learning models like CNNs achieve high accuracy in landslide detection by effectively processing complex and high-dimensional data. | Data Dependency: ML models require large amounts of high-quality data for training, which may only sometimes be available. |
Automation: Automated detection reduces the need for manual interpretation, saving time and labor. | Overfitting: ML models, especially complex ones, can overfit the training data, reducing their performance on unseen data. |
Scalability: ML algorithms can be scaled to process large datasets and cover extensive geographical areas. | Computational Resources: Training and running complex ML models require significant computational resources and infrastructure. |
Integration of Multiple Data Sources: ML models can integrate diverse data types such as topographic, geological, and meteorological data for comprehensive analysis. | Complexity: The complexity of ML models can make them difficult to interpret and understand, which can be a barrier to their adoption. |
Early Warning Systems: ML-based LEWS can provide timely alerts based on real-time data, potentially saving lives and reducing property damage. | Data Quality Issues: The quality of input data, such as resolution and accuracy, significantly impacts the performance of ML models. |
Adaptability: ML models can adapt to different regions and conditions, making them versatile tools for landslide detection. | Maintenance: ML models require ongoing maintenance and updating to incorporate new data and adapt to changing conditions. |
Enhanced Feature Extraction: Advanced algorithms can automatically extract and prioritize relevant features from the data, improving model performance. | False Positives/Negatives: Despite high accuracy, ML models can still produce false positives and negatives, undermining system trust. |
Technology | Pros | Cons |
---|---|---|
GPS |
|
|
GIS |
|
|
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Alam, M.J.B.; Manzano, L.S.; Debnath, R.; Ahmed, A.A. Monitoring Slope Movement and Soil Hydrologic Behavior Using IoT and AI Technologies: A Systematic Review. Hydrology 2024, 11, 111. https://doi.org/10.3390/hydrology11080111
Alam MJB, Manzano LS, Debnath R, Ahmed AA. Monitoring Slope Movement and Soil Hydrologic Behavior Using IoT and AI Technologies: A Systematic Review. Hydrology. 2024; 11(8):111. https://doi.org/10.3390/hydrology11080111
Chicago/Turabian StyleAlam, Md Jobair Bin, Luis Salgado Manzano, Rahul Debnath, and Ahmed Abdelmoamen Ahmed. 2024. "Monitoring Slope Movement and Soil Hydrologic Behavior Using IoT and AI Technologies: A Systematic Review" Hydrology 11, no. 8: 111. https://doi.org/10.3390/hydrology11080111