This study presents a machine learning-based approach for landslide forecasting in the Southern Andes, combining feature selection methods (CART and genetic algorithms) with multiple classifiers (SVM, RF, XGB). The research design is sound, the methodology is robust, and the results hold practical significance, particularly in the context of early warning systems for geological hazards. The paper is recommended for publication after addressing the following points.

Major comments:

1. The introduction currently provides a broad overview of landslide forecasting but could better highlight the specific innovations of this study. For example: (1) The proposed solutions for data scarcity in the Southern Andes (e.g., PU Bagging and buffer control sampling); (2) The unique advantages of the hybrid feature selection approach (GA + CART) in landslide prediction.
2. The conclusion should more explicitly summarize the improvements this study offers to existing landslide early warning systems and its practical implications.
3. The abstract should be refined to convey more insightful information.
4. The paper mentions multiple databases (e.g., ERA5, CLSoilMaps) but lacks details on their temporal coverage, resolution consistency, and handling of missing data. These details should be added.
5. The methodology for generating "negative examples" (non-landslide data) via Buffer Control Sampling and PU Bagging requires further justification.
6. Tables 4 and 5 present performance metrics for different methods but lack statistical significance tests (e.g., p-values or confidence intervals). Such tests would strengthen the claim that GA-based optimization outperforms other methods.
7. The similar performance of GA XGB and GA RF (both with 10.95% error rates) warrants discussion on their trade-offs in real-world applications (e.g., computational efficiency, interpretability).
8. The study focuses on two regions in southern Chile (Los Lagos and Los Ríos). The conclusions should clarify whether they are applicable to areas with different geological or climatic conditions.
9. The discussion should more thoroughly address model limitations.

Minor comments:

1. Labels in Figures 3 and 4 (correlation matrices) are too small and should be enlarged or provided in higher resolution.
2. Abbreviated variable names in Tables 1 and 2 (e.g., "AvMoist") should be explained in the main text or footnotes (e.g., "Available Moisture").
3. Inconsistent formatting of terms (e.g., "GA XGB" vs. "GA_XGB") should be unified.

The first paragraph of the conclusion (lines 465–475) could be condensed to avoid redundancy with earlier sections.

This manuscript provides an interesting study on identifying the primary factors controlling rainfall-induced landslides in four Chilean regions. The framework and methodologies are sound. However, I regret to say that the core innovation of this study has not been sufficiently articulated. As the authors note (line 86-87), the study area is unique and complex in geological and climatological features. Nevertheless, the manuscript does not rigorously discuss which features emerge as the most representative variables for volcanic, sedimentary, and glacial terrains, nor how these features influence susceptibility mapping. Instead, the emphasis is placed on machine learning and feature selection techniques, which are widely used and cannot be highlighted as innovative in comparison with the unique geological setting of the study area. Meanwhile, the writing and structure of the manuscript are not well organised, which makes it difficult for the reader to follow the authors’ idea. For these reasons, I do not consider the manuscript is suitable for publication in its current form. Substantial revisions are required to improve its quality. Some detailed comments are as follows.

1. L12-13: The abstract ends abruptly without presenting any concrete results. Please expand the abstract to include the key findings and avoid vague statements such as “various predictive models were tested.”
2. L41-42: Seismic activity is not relevant to this study and should not be included in the introduction.
3. L87-88: The diverse geological composition of the study area should be emphasized as one of the most important aspects. Please elaborate on how different soil and lithological types correspond to the selected controlling features.
4. L119-121: The phrase “considerable attention” is unclear. Please specify the exact steps taken to ensure data quality.
5. L127: The abbreviation “PP” is used without being defined beforehand. Please define it at first mention.
6. Figure 7 and 8: These figures are not properly prepared. They contain non-English words, and their captions are incomplete. Please revise accordingly.
7. Figure 9: Please add the coordinates to the map.