| 12 May 2026
Reducing False Alarms in Small-Scale Slope Early Warning Systems via Deep Learning-Driven Asynchronous Displacement and Rainfall Data Fusion
Abstract. Real-time crackmeter-based warning systems for rainfall-induced small-scale slope failures suffer from prohibitively high false alarm rates. Across diverse geological settings, existing approaches relying on fixed thresholds or isolated rainfall–displacement relationships are highly sensitive to noise, lacking the robustness to reliably distinguish genuine deformation signals from spurious measurements. To address this operational bottleneck, this study reframes the early warning paradigm from a traditional fixed-threshold alerting process reliant on manual discrimination to an automated alert truth verification problem, proposing a real-time binary classification framework to isolate true early warnings from sensor-triggered false alarms. Utilizing a multi-source dataset of crackmeter displacement and rainfall measurements from diverse monitored slopes in Fujian Province, China, all true early warning instances were rigorously calibrated via field investigations. A patch-based dual-branch temporally-aware Transformer model was developed to explicitly address asynchronous multi-rate data fusion, strict temporal causality between rainfall and displacement, and stringent real-time decision constraints. By simultaneously capturing long-term rainfall-displacement interactions and high-resolution displacement dynamics, the model outperforms competitive baselines, achieving a precision of 90.91 %, a recall of 93.53 %, and an F2-score of 92.99 % in identifying true early warnings. Interpretability analysis reveals the model’s decisions are primarily driven by localized displacement trends and relative rainfall intensity, aligning with expert judgment. The proposed framework significantly curtails false alarms without compromising reliability, acting as a robust decision-support layer to enhance automated slope hazard monitoring. Future work will fuse additional sensor types to suppress false alarms via cross-validation of multi-physical responses.
The manuscript addresses an important and relevant topic within landslide early warning systems, and the machine-learning/data-fusion methodology appears technically rigorous and competently validated.
However, the manuscript currently treats landslide early warning primarily as a signal-classification problem, without proper consideration of the geotechnical, geological and hydromechanical aspects.
The title, abstract and introduction should more clearly define the scope and contribution of the study. The novelty appears to lie mainly in the dataset manipulation and ML workflow.
The term “small-scale” is ambiguous and should be clarified.
A concern is the reliance on crack meters as the principal monitoring approach for shallow landslide warning. Crack opening is not necessarily representative of bulk slope deformation or impending instability, and the manuscript does not sufficiently justify this monitoring philosophy. Later sections imply that this approach has already been widely deployed across Fujian Province, and that the AI framework is intended to mitigate resulting false alarms. If so, this context should be introduced much earlier.
The manuscript also gives insufficient consideration to instrumentation placement and site characterisation. Sensor location relative to slope geometry, drainage, lithology and likely failure mechanism is critical to reliable interpretation.
The definition of “false alarms” remains unclear throughout the manuscript and requires substantial clarification. It is currently uncertain whether false alarms refer to:
These are fundamentally different phenomena with different implications for warning-system performance. Similar clarification is needed for Figure 4 and the “triple-blind review” discussed at line 205.
Figures 11–13 also require clarification. It is not clear whether the timing of actual landslide occurrence is shown relative to the issued alarms.
The statement that landslides are “most strongly associated with precipitation effects within a 6–10 day window” is overly broad and insufficiently justified. Slope response depends strongly on material type, permeability, drainage conditions and failure depth. However, the manuscript provides very limited information regarding geology, geomaterials or failure mechanisms.
The machine-learning workflow appears rigorous, but the manuscript does not compare the proposed framework against simpler or more conventional approaches that may already be sufficient to reduce false alarms. This raises the question of whether a highly sophisticated AI framework is being used to compensate for limitations in an oversimplified monitoring philosophy.
The discussion should also engage more critically with operational geotechnical practice: warning thresholds should generally be site-specific and informed by local geology and failure mechanisms, rather than applied uniformly across many slopes. Warnings are based not only on threshold exceedance, but also on trends in behaviour, rates of change, and engineering judgement.
The manuscript would benefit from discussion of the transferability of the proposed methodology beyond Fujian Province and to settings.