2026-10-12T06:27:07Z https://data.mendeley.com/oai

oai:data.mendeley.com/fd4g8vgsn9.3 2026-09-14T09:33:51Z

Jia, Guiyun Data and code for evaluating hyperparameter optimization in landslide susceptibility mapping in Xifeng District and Longxi County Mendeley Data This dataset provides processed data, evaluation outputs, and source code supporting a unified framework for hyperparameter-optimized landslide susceptibility mapping in Xifeng District and Longxi County, Gansu Province, China. The study examined conditioning factor selection, optimization gains and efficiency, independent model and map evaluation, and spatial sensitivity across two contrasting loess regions. The modeling datasets contain 214 samples in Xifeng (107 landslide and 107 non-landslide samples) and 294 samples in Longxi (147 of each class). Fourteen candidate conditioning factors were considered in Xifeng and 15 in Longxi. Sample identifiers, class labels, training and test assignments, and cross-validation fold assignments are provided. Six models—logistic regression, support vector machine, random forest, multilayer perceptron, gated recurrent unit, and one-dimensional residual network—were compared using budgeted grid search, random search, the tree-structured Parzen estimator, and a genetic algorithm under a common budget of 50 completed trials. The repository includes multicollinearity diagnostics, candidate factor sets, Shapley additive explanations, optimization summaries for all 96 combinations of region, model, factor set, and optimization method, selected configurations, and available trial logs. Model evaluation files contain held-out predictions, receiver operating characteristic curve coordinates, statistical comparisons, repeated-training results, and out-of-fold predictions for random and spatial cross-validation. Map outputs comprise 24 continuous susceptibility rasters and 24 classified rasters, divided equally between independent evaluation and final mapping. They are accompanied by success rate and prediction rate curve coordinates, class-area statistics, and enrichment ratios with their reciprocal seed cell area indices. Independent evaluation uses maps generated by models fitted to training samples; prediction rate and enrichment measures use held-out landslides. Final maps were generated by refitting models to all regional samples. Their class areas describe regional susceptibility patterns, while full-inventory enrichment statistics are retained as archival descriptions. Source code, environment specifications, data dictionaries, and checksums support verification and reuse. Original sample coordinates and full-grid conditioning-factor inputs are restricted, and trained checkpoints are not included. Access prerequisites and reproduction steps are documented in the repository. Loess Landslide Liang, Qingguo Guo, Fuyun Dataset doi:10.17632/fd4g8vgsn9.3 oai:data.mendeley.com/fd4g8vgsn9.3 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/fd4g8vgsn9 2026-09-14T09:33:51Z