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Solar photovoltaics (PV) is central to the global energy transition. In 2024 alone, solar PV accounted for 42% of installed renewable capacity worldwide and more than three-quarters of new renewable additions. Looking ahead, IRENA projections indicate that solar PV will contribute around half of the renewable capacity required by 2050 to meet the Paris Agreement goals.

As deployment accelerates and projects scale up, attention is increasingly turning to how solar PV interacts with local environments. Solar projects can generate significant climate and air quality benefits, yet they may also affect biodiversity, ecosystems, and land use if environmental considerations are not fully integrated into planning, siting, and design. Conversely, when well planned, solar PV can deliver environmental co-benefits—supporting ecosystem restoration, land productivity, and biodiversity through approaches such as agrivoltaics, solar grazing, ecovoltaics, floating PV, and land degradation recovery.

This session, convened during the Sixteenth IRENA Assembly under the theme Powering Humanity: Renewable Energy for Shared Prosperity, will explore how to balance rapid solar PV deployment with environmental protection and enhancement, ensuring that the energy transition benefits both people and nature.

The session will convene policymakers, energy experts, industry representatives, and conservation organisations to:

• Examine the potential negative and positive interactions between large-scale solar PV projects and local environments;
• Share lessons and tools to avoid, minimise, and mitigate environmental risks during project planning, siting, construction, and operation;
• Explore opportunities to scale nature-positive co-benefits from solar PV projects across diverse contexts; and
• Discuss policy, planning, and financing approaches that can enable environmentally responsible solar deployment.

IRENA will present key findings from its latest report on the local environmental impacts and benefits of large-scale solar PV projects, jointly developed with IUCN and the China Renewable Energy Engineering Institute (CREEI). Participants will exchange policy experiences and best practices from different regions and market contexts.

IUCN and IRENA signed a Memorandum of Understanding (MoU) at the IUCN World Conservation Congress in Abu Dhabi in October 2025, with the aim of accelerating a renewable energy transition that is both sustainable and nature-positive.

Theoretical basis

The proposed EWS integrates four core technologies: Internet of Things (IoT), deep learning, cloud computing, and Geographic Information Systems (GIS). Each plays a distinct role in system development:

The IoT is an emerging technology for information sensing and transmission. It connects physical objects with digital networks using sensors and communication protocols, enabling intelligent monitoring and management. Recent IoT advances have significantly improved slope monitoring capabilities. These include multi-source data sensing and real-time transmission, which is crucial for accurate landslide monitoring and early-warning. In this research, IoT technology was implemented for the monitoring of open-pit mine slopes. Monitoring data were transmitted to cloud servers via efficient communication protocols (e.g., MQTT, HTTP), ensuring both data integrity and real-time availability. These real-time reliable multi-source monitoring data are essential for dynamic risk assessment and predictive early-warning of landslides.

Deep learning, a pivotal branch of artificial intelligence, demonstrates significant advantages in complex pattern recognition and prediction tasks. By constructing multi-layer neural networks, it automatically extracts intrinsic features and evolutionary patterns from massive datasets, enabling high-precision predictions for unknown data¹⁸. In geotechnical engineering, particularly for slope stability monitoring and early warning, this technology provides innovative solutions for capturing nonlinear, non-stationary data characteristics that traditional methods often fail to address¹⁹. This study employs Long Short-Term Memory (LSTM) algorithms to analyze temporal evolution patterns from slope monitoring data, establishing an advanced prediction model for slope deformation.

Cloud computing represents an advanced computational mode that creates configurable resource pools. These pools provide on-demand access to computing resources (networks, servers, storage, applications, and services) anytime, anywhere. This mode significantly reduces resource management workloads and minimizes interaction costs with service providers. It enables rapid resource deployment and release²⁰. This study leverages Alibaba Cloud’s Elastic Compute Service (ECS), a virtual server solution in cloud environments. ECS dynamically adjusts computing resources based on user requirements, ensuring efficient resource utilization. By integrating ECS’s advantages (elastic scaling, efficient management, and high reliability), this research achieves two critical functions: (1) Storage of multi-source monitoring data. (2) Real-time data analysis. These capabilities deliver robust computational support for landslide risk assessment and early-warning.

GIS is an integrated system designed for data acquisition, transmission, management, storage, analysis, and visualization. Their core strength lies in efficient integration and dynamic representation of geospatial data²¹. Recent advances in 3D modeling, virtual reality (VR), and artificial intelligence have significantly expanded GIS applications in geotechnical engineering. These technologies demonstrate particular advantages in slope stability monitoring and landslide early-warning. In this study, GIS capabilities for multi-source data management and visualization were leveraged to develop a virtual reality scenario for slope risk management. By integrating topographic, monitoring, and early warning data, the system provides a foundation for mine safety management.

The interrelationships among the four technologies are illustrated in Fig. 1. The EWS acquires multi-source monitoring data through IoT technology. Deep learning provides analytical models for data mining, while cloud computing delivers computational power for data analysis and landslide risk assessment. GIS technology enables integrated visualization of real-time monitoring and early warning data, achieving transparent risk representation. Finally, users manage risks via the GIS system and dispatch control commands through IoT technology, establishing closed-loop management of landslide risks.

Framework design for the EWS

The EWS employs a three-tier framework (Fig. 2): data layer, service layer, and application layer. The data layer is responsible for the real-time collection and storage of on-site monitoring data. The service layer handles data processing to meet the requirements of various disaster early-warning procedures. The application layer is dedicated to the visualization of the early-warning process. This structure ensures comprehensive management of the entire landslide risk process – from data acquisition and risk analysis to warning issuance and risk visualization. Key design considerations for each tier are detailed below.

Data layer

The data layer manages real-time acquisition, transmission, and storage of multi-source slope mechanical response data. This EWS integrates multiple monitoring devices for open-pit slopes. These include GNSS devices, multi-point extensometers, inclinometers, and radar for tracking rockmass displacement. Additionally, crack gauges measure crack width. Blast vibration sensors detect shockwaves from mining operations. These real-time, multi-source monitoring data provide the foundation for comprehensive landslide risk assessment and early warning. Given the significant variations in data structures and acquisition approaches across monitoring devices, the system incorporates tailored data transmission schemes designed for the specific characteristics of each device type to enhance efficiency:

1. (1)

   Point-based monitoring devices, such as GNSS devices and inclinometers, generate multi-channel time-series data. This data type is characterized by small individual packet sizes but high acquisition frequencies. To efficiently transmit this data, the system employs the MQTT protocol. MQTT demonstrates high performance for small payloads, high-frequency communication, and reliable data delivery, making it well-suited for the real-time transmission needs of point-based sensors.

2. (2)

   Radar monitoring offers extensive coverage, high scan frequency, and generates large data volumes per measurement. Following each scan, the radar device locally produces a TXT file containing deformation data. To enable efficient transmission of these substantial files, the EWS employs a combined approach using file system monitoring and the FTP protocol. The file system monitor detects newly generated TXT files in real-time, triggering their immediate transfer to a designated server via FTP. This method ensures both prompt data delivery and file integrity.

Following data acquisition and transmission, the EWS implements differentiated storage strategies based on data types to ensure efficient management and rapid retrieval. For structured data from GNSS and crack gauge sensors, MySQL databases provide optimized storage, leveraging their robust data management and query capabilities. Regarding file-based data generated by radar systems, characterized by large file sizes, the system stores files directly on local disks while using MySQL to record storage paths and metadata. This hybrid approach reduces database load and improves file management efficiency.

Furthermore, to ensure reliable multi-source data transmission and real-time communication, the IoT network within the proposed EWS adopts a 4G-based wireless architecture. Each on-site monitoring device—including GNSS, radar, inclinometers, and crack gauges—is equipped with a data acquisition module and a 4G communication terminal. Monitoring data is transmitted through the public 4G cellular network to the cloud server, where the data layer receives and synchronizes information in real time.

Building on the IoT architecture studies by Dorthi²² and Zhou¹⁵, the IoT communication framework adopted in this study follows a three-tier structure comprising the perception layer, network transmission layer, and application layer.

The perception layer comprises distributed monitoring sensors that collect displacement, deformation, and vibration data from slopes in real time. The network transmission layer utilizes 4G communication protocols to achieve long-distance, low-latency, and stable data transmission between field devices and the cloud platform. The use of existing 4G infrastructure ensures high reliability and scalability while minimizing deployment cost. The application layer runs on the cloud platform, integrating real-time data acquisition, storage, and analysis services through the MQTT and HTTP protocols.

To visually clarify the IoT communication process, a network architecture diagram has been added to the revised manuscript (Fig. 3). Monitoring sensors (GNSS, radar, inclinometer, and crack gauge) collect real-time deformation data and transmit them wirelessly to the 4G base station. Using MQTT and HTTP protocols, the base station uploads the data through the 4G network to the cloud platform for data storage, analysis, and visualization. The process ensures real-time synchronization, low latency, and high reliability for continuous slope risk monitoring.

Service layer

The service layer functions as the core component of the EWS, enabling intelligent data processing and analysis to support all phases of landslide hazard alerts. It comprises six integrated modules: (1) Equipment management services; (2) Data management services; (3) Prediction and early-warning service; (4) Alert dissemination service; (5) Virtual reality scenario service; (6) Data visualization service. These modules operate collaboratively to establish a comprehensive monitoring and warning framework for open-pit coal mine slopes. The specific functions of each module are detailed below.

1. (1)

   Equipment management service: This service provides full lifecycle management for monitoring devices. Key functions include maintaining essential device information. By intelligent device-system matching, the service ensures real-time data transmission and cloud synchronization. This guarantees continuous and complete data acquisition, delivering reliable data for data management services.

2. (2)

   Data management service: This service handles the storage, maintenance, and retrieval of multi-source monitoring data. It provides robust storage and efficient management capabilities for multi-source data streams. By establishing an integrated data management platform, the service enables standardized processing and rapid querying of monitoring datasets. This infrastructure delivers a solid data foundation for predictive analytics and early-warning services.

3. (3)

   Prediction and early-warning service: This service delivers intelligent forecasting of rockmass mechanical behavior and slope stability. It integrates three core functions: data preprocessing, LSTM-based mechanical response prediction, and Dempster-Shafer evidence theory-driven landslide risk assessment. This integrated framework extracts critical landslide precursors from multi-source monitoring data. By implementing data fusion methods, it resolves conflicts in multi-device warning outputs, significantly improving the accuracy and reliability of slope risk predictions. The service thus provides a quantifiable basis for early-warning decision-making.

4. (4)

   Alert dissemination service: This service manages early-warning alert distribution and administration. It enables flexible configuration of alert dissemination channels and closed-loop management of response procedures. By continuously monitoring slope risk status, the service automatically issues alerts to management personnel when the slope is instability. All response actions are systematically recorded, ensuring automatic alert distribution and full traceability of risk mitigation processes. This provides critical decision support for landslide management.

5. (5)

   Virtual reality scenario service: This service constructs a digital twin scenario of the open-pit coal mine using the SuperMap platform. This system integrates UAV-based oblique photogrammetry to achieve high-precision modeling of coal mine slopes. By visualizing rockmass mechanical responses, the service transparently displays real-time slope conditions and deformation trends. This provides managers with an immersive virtual reality (VR)-enabled visualization experience for operational decision-making.

6. (6)

   Data visualization service: This service converts multi-source monitoring data into computer-readable charts and graphs, significantly simplifying data interpretation. It dynamically displays real-time data while generating historical trend plots. These visual tools help users intuitively understand slope risk conditions. Through interactive visual exploration, the service enhances both the efficiency of slope risk management and the scientific basis for decisions.

Application layer

The application layer serves as the user interface for the EWS. It performs two key tasks: calling services and displaying functions. Designed around user needs, it combines multi-source data, intelligent analysis, and visual tools. This layer manages the full lifecycle of landslide early-warnings for open-pit coal mines, which delivers five primary capabilities:

1. (1)

   VR scenario of open-pit coal mine: Leveraging device management and virtual reality scenario services, this module constructs high-precision reality models using UAV oblique photogrammetry. It enables precise device localization and dynamic visualization within a VR scenario.

2. (2)

   Multi-source data query: A data center is built using the device management and data management services. Users can quickly retrieve monitoring data for specific timeframes and locations. This enables a better understanding of rockmass failure progression in slopes.

3. (3)

   Slope risk prediction: This module integrates data management and Prediction and early-warning services to deliver intelligent risk assessment and prediction. The EWS processes real-time monitoring data through an LSTM-based prediction model. The output is then fused with D-S theory to evaluate landslide risk. This integrated approach proactively identifies potential landslide risks, providing a foundation for mitigation strategy development.

4. (4)

   Multi-Source Data Visualization: This module integrates data visualization and virtual reality scenario services to provide a multi-view representation of multi-source monitoring data. By integrating diverse visualization formats (data tables, trend curves, and heatmaps), the EWS dynamically displays current slope conditions and evolutionary trends. This module streamlines data interpretation while delivering intuitive decision-making support to management personnel, which can significantly enhance emergency response efficiency.

5. (5)

   Multi-channel Alert Distribution: This module integrates predictive analytics and alert dissemination services to generate smart tiered alerts for slope risks. Based on predefined risk classification protocols, it automatically disseminates alerts through SMS, email, and other communication channels. This ensures the timely delivery of critical notifications to relevant personnel, enabling rapid emergency response.

Early-warning methods

Prediction and early-warning methods constitute the core of the Early Warning System (EWS) and are crucial for generating high-precision landslide alerts. As shown in Fig. 4, the integrated workflow encompasses data processing, prediction, and risk assessment. The process begins with the real-time acquisition of rock mass deformation data via monitoring equipment such as radar and GNSS. A scheduled task mechanism, whose frequency is adaptively adjusted based on the identified landslide risk level, is employed for periodic data processing. By default, data are processed once per day, while the frequency increases to once per hour when the risk level reaches “Orange” or higher. Upon triggering, the scheduled task first performs data cleaning using the Pauta criterion, as detailed in Section “Data preprocessing based on the Pauta criterion”, to ensure high-quality monitoring data. The cleaned data subsequently serve two purposes: they are fed into an LSTM model to predict future rock mass deformation (Section “LSTM-based prediction model”), while both the cleaned data and prediction results are jointly analyzed to extract landslide risk evaluation indicators, such as the improved tangent angle, for both current and future states (Section “Landslide risk assessment model based on D-S theory”). These indicators are then fused via the Dempster–Shafer (D-S) evidence theory model, enabling a comprehensive landslide risk assessment and graded early warning, as further elaborated in Section “Landslide risk assessment model based on D-S theory”. Detailed methodological descriptions are provided in the subsequent sections.

Data preprocessing based on the Pauta criterion

Harsh mining environments with heavy dust and poor network connectivity often prevent monitoring devices from operating consistently. This instability compromises data integrity and reliability.

Incomplete monitoring data primarily stems from transmission anomalies during collection, caused by unavoidable factors like network disruptions, adverse weather, or mining activities. Resulting data gaps alter dataset structures—for example, shortening time-series sequences. Since most analytical algorithms require structurally consistent inputs, such disruptions frequently cause analytical failures, undermining slope risk assessment accuracy.

Given that most devices follow a scheduled acquisition time interval, data completeness is verified against predefined intervals. Let d represent the data measurement interval (in minutes). Every minute, the system initiates a missing-data check. For each check, the time difference (Δd) between the current check time and the device’s last successful data acquisition is calculated. When Δd > d, the device has failed to collect data within its scheduled measurement interval. To address such missing values, the LSTM-based prediction model (Section “LSTM-based prediction model”) is used to predict current mechanical responses. These predicted values are then stored as monitoring data to preserve dataset integrity.

Data unreliability mainly involves outliers from complex environmental interference during collection. These anomalies distort distributions and impair deep learning model performance. Consequently, robust preprocessing is essential for noise removal. The Pauta criterion (3σ principle)²³ provides effective denoising using statistical properties of normal distributions, combining operational simplicity with proven efficacy. The Pauta criterion is employed to identify noise by establishing a confidence interval based on the mean ((mu)) and standard deviation ((sigma)) of a dataset, as defined in Eq. (1):

where (k) is a scaling factor that determines the breadth of the interval. When (k=1,)approximately 68.27% of the data points are expected to lie within the interval; this proportion increases to about 95.45% for (k=2,)and 99.73% for (k=3.)Any data point falling outside this defined interval is subsequently identified and treated as noise. This study implements real-time Pauta denoising within a 7-day moving window—optimized for slope mechanical response characteristics. As new data arrives, the system continuously performs anomaly detection as follows:

1. 1.

   For a real-time data point A_t measured at time t, extract data from the preceding 7 days to form a time-series dataset L = {A₀, A₁, A₂, …, A_t}.

2. 2.

   Calculate the standard deviation (σ) and mean (μ) of dataset L. Determine the confidence interval C for normal data using Eq. (1). Following the Pauta criterion and considering data characteristics, a conservative threshold k = 2 is adopted, retaining 95.45% of normal data for subsequent calculations.

3. 3.

   Evaluate A_t against the confidence interval. Data points within C are classified as normal, otherwise as outliers.

LSTM-based prediction model

Current early-warning systems (EWSs) typically assess slope stability by comparing real-time monitoring data against predefined thresholds. While this approach is effective for evaluating instantaneous stability conditions, it lacks predictive capability and often leads to delayed warnings. In open-pit mines, slope monitoring data generally exhibit nonlinear and nonstationary temporal patterns. Despite their complexity, these deformation sequences frequently contain short-term trends that can be exploited through time-series modeling.

Long Short-Term Memory (LSTM) networks are well-suited for this task because their gated memory structure effectively captures nonlinear temporal dependencies in sequential data²⁴. After data denoising using the aforementioned procedures, the LSTM model is adopted in this study to forecast short-term mechanical responses, thereby providing near-future deformation trends. The workflow consists of four main stages:

1. (1)

   Pre-processing phase: In this study, multiple types of monitoring instruments were deployed, including Global Navigation Satellite System (GNSS) sensors, ground-based radar, inclinometers, and crack gauges. The sampling intervals were 10 min for GNSS, 30 min for radar, and 5 min for inclinometers and crack gauges. The monitoring data are collected continuously and transmitted to the cloud server in real time. Given that monitoring devices collect data at intervals ranging from minutes to hours, their measurements often exhibit significant fluctuations. To mitigate these fluctuations, daily mean deformation values are calculated at the end of each day. These mean values represent the mechanical response of the slope rockmass for those 24 h.

2. (2)

   Model architecture: The proposed LSTM model employs a concise univariate architecture designed for efficient and stable slope displacement forecasting. Each input sample consists of the most recent 10 consecutive daily displacement values, corresponding to an input shape of (10, 1)—ten time steps and one feature per step. The network includes three layers:

   1. 1.

      an input layer receiving the displacement sequence.

   2. 2.

      a single LSTM hidden layer with 100 memory units, which captures the nonlinear temporal evolution of slope deformation.

   3. 3.

      a fully connected output layer with one neuron and a linear activation, producing the predicted displacement for the next day (output dimension = 1).

   The LSTM layer uses the standard tanh and sigmoid activation functions for internal gating. This lightweight configuration provides sufficient representational capacity for long-term deformation sequences while maintaining low computational cost, enabling real-time operation on industrial hardware.

3. (3)

   Training phase: For model training, the most recent 40 days of monitoring data are used. A sliding window of 10 days serves as the input, and the subsequent day’s displacement as the target output. By moving the window forward one day at a time, 30 training samples are generated. The model is trained using the Adam optimizer (learning rate = 0.001) with mean squared error (MSE) as the loss function. Training proceeds for up to 100 epochs with a batch size of 10, reserving 10% of the samples for validation. An early stopping strategy (patience = 10, restore_best_weights = True) is applied to prevent overfitting. After each forecast, the predicted value is appended to the time sequence, forming a recursive prediction loop for continuous updating.

4. (4)

   Prediction phase: During inference, the model receives the latest 10 days of actual monitoring data to forecast the next day’s displacement. For subsequent predictions, the model combines the most recent 9 days of real data with the previous prediction to form a new input window, iteratively generating forecasts one day at a time. This process continues until 10 consecutive daily forecasts are obtained, providing a near-term prediction horizon for slope deformation evolution.

Landslide risk assessment model based on D-S theory

Current risk assessment practices for slopes in open-pit coal mines often rely on evaluating single parameters from monitoring data. However, slope failure involves complex mechanisms where analysis based on a single parameter is inadequate, frequently leading to conflicting risk assessments. To address this limitation, we apply Dempster-Shafer (D-S) evidence theory. This systematic framework integrates uncertain information from multiple sources. As an extension of probability theory, D-S quantitatively combines evidence from different indicators while accounting for their individual reliability, thereby resolving contradictions between multi-source assessments. This integrated approach provides a more comprehensive and accurate stability evaluation for slope risk management.

1. (1)

   Evaluation indicator selection and quantification of risk value.

When a slope is undergoing creep deformation, Xu et al.²⁵ proposed that the displacement rate (ν) during the secondary creep stage remains constant. To transform the displacement–time curve’s vertical axis into the same time dimension as the horizontal axis, displacement is divided by ν, as shown in Eq. (2):

where (Delta S) is the slope displacement. ν is the secondary creep velocity, and (T) is the transformed vertical coordinate value with the same dimension as time. The tangent angle (alpha) of the displacement curve is then defined as the improved tangent angle, expressed as:

When α < 45º, the rockmass is in the primary creep stage, and failure is unlikely. When α = 45º, it enters the secondary creep stage. When α > 45º, it enters the tertiary creep stage, indicating a potential risk for failure. This indicator (α) has been widely adopted by researchers internationally^26,27. Its effectiveness has been validated through extensive engineering practice, leading to the development of a corresponding four-level early-warning threshold.

Furthermore, based on the report analysis and literature review, cumulative displacement and deformation velocity were selected as additional evaluation indicators. Warning level thresholds for these indicators were established, thus forming a comprehensive slope stability evaluation indicator system. The specific thresholds are detailed in Table 2.

To further quantify the relationship between the different warning levels and the landslide risk, probability ranges for each level were assigned based on previous research²⁸, specifically 0 ~ 0.25 for Advisory, 0.25 ~ 0.5 for Caution, 0.5 ~ 0.75 for Warning, and 0.75 ~ 1 for Alert. Furthermore, relationships linking the landslide risk to each indicator were established through a fitting approach, with the specific mathematical expressions provided in Eqs. (4) to (6). This quantification thus enables enhanced landslide risk assessment and prediction.

1. (2)

   Landslide risk real-time assessment based on D-S theory.

Building upon the above framework, once new real-time monitoring data is received or a prediction is completed, the improved tangent angle, cumulative displacement, and deformation velocity are calculated. The risk level for each indicator is then quantified using Eqs. (4)-(6). Subsequently, the Dempster-Shafer (D-S) evidence theory is applied to fuse the risk parameters derived from these indicators. The procedure is as follows:

In this study, the risk state of the slope is defined within the identification framework Θ = {A, B}, where A represents an unstable state and B represents a stable state. The risk assessment results for the improved tangent angle (R_α), cumulative displacement (R_S), and deformation velocity (R_ν) serve as the evidence sources. Based on Eqs. (4)-(6), the basic belief assignments are determined. Mass functions describing the slope stability states are then constructed for each evidence source according to the principles outlined in Table 3.

Based on D-S evidence theory, the three independent mass functions (m₁, m₂, m₃) are fused into a combined mass function using Dempster’s combination rule to obtain the comprehensive probability, R_f, of the slope being in an unstable state (Proposition A). The rule is defined as follows:

The normalization factor (K) ensures that the total probability is unity and is defined as the sum of the products of masses for all non-conflicting propositions:

The value ({R}_{f}) is subsequently classified into a risk level based on Table 4, achieving a data-driven and fused risk assessment.

Implementation of EWS

The EWS employs a Browser/Server (B/S) architecture. The frontend, developed with Vue.js, Element UI, ECharts, and Cesium, enables real-time monitoring data visualization and slope stability assessment visualization. The backend integrates Maven, Spring Boot, MyBatis, and Python for data management and intelligent analysis. Cloud infrastructure utilizes Aliyun ECS elastic servers and Aliyun RDS MySQL. This architecture eliminates client-side installations—users access the system instantly via any network-connected browser. Domain-based access initiates HTTP requests for frontend-backend data exchange, enabling one-click retrieval and visualization of slope risk state. Figure 5 presents the interface of the developed system.