An interpretable model for landslide susceptibility assessment based on Optuna hyperparameter optimization and Random Forest

Figures & data

Figure 1. Location of the study area (a) and kernel density map of landslides (b).

Table 1. Detailed information of the data.

Data category	Data name	Time	Spatial resolution	Data source
Precipitation	Daily precipitation	1986–2018	–	Yunnan Province Climate Center
Surface environment	Elevation	2009	30 m	SRTMv3.0
	Lithology	2012	250 m	Sayre et al. (2014)
	Soil types	1995	1 km	RESDC
	NDVI	1986–2020	30 m	Landsat 5/8
	LULC	2005	1 km	RESDC
	Rivers	2015	–	National Catalogue Service for Geographic Information
	Fault zone	–	–	Comprehensive karst hydrogeological map of Yunnan Province
Human activities	Population density	2000–2020	1 km	Tatem (2017)
	Roads	2015	–	National Catalogue Service for Geographic Information

Figure 2. Methodological framework of this study.

Figure 3. Spatial distribution of precipitation factors. (a) PRCPTOT; (b) SDII; (c) RX1D; (d) RX5D; (e) R95P; (f) R99P; (g) R10MM; (h) R20MM; (i) R50MM.

Table 2. Definition of precipitation index.

Precipitation index	Abbreviation	Definition	Unit
Annual total wet-day precipitation	PRCPTOT	Annual total precipitation from days ≥ 1 mm	mm
Simple daily intensity index	SDII	The ratio of annual total precipitation to the number of wet days (≥1 mm)	mm/days
Max 1-day precipitation amount	RX1D	Monthly maximum 1-day precipitation	mm
Max 5-day precipitation amount	RX5D	Monthly maximum consecutive 5-day precipitation	mm
Very wet days	R95P	Annual total precipitation from days > 95th percentile	mm
Extremely wet days	R99P	Annual total precipitation from days > 99th percentile	mm
Number of heavy precipitation days	R10MM	Annual count when precipitation ≥ 10 mm	Days
Number of very heavy precipitation days	R20MM	Annual count when precipitation ≥ 20 mm	Days
Number of extreme heavy precipitation days	R50MM	Annual count when precipitation ≥ 50 mm	Days

Figure 4. Spatial distribution of the surface environment factors. (a) Elevation; (b) SA; (c) Slope; (d) LULC; (e) SR; (f) TR; (g) TWI; (h) Lithology; (i) NDVI; (j) DistRivers; (k) DistFaults; (l) Soiltype.

Figure 5. Spatial distribution of human activity factors. (a) POP; (b) DistRoads.

Table 3. Types of interaction between two covariates.

Description	Interaction
q(X1∩X2) < Min(q(X1), q(X2))	Weakened, nonlinear
Min(q(X1), q(X2)) < q(X1∩X2) < Max(q(X1), q(X2))	Weakened, single factor nonlinear
q(X1∩X2) > Max(q(X1), q(X2))	Enhanced, bi
q(X1∩X2) = q(X1) + q(X2)	Independent
q(X1∩X2) > q(X1) + q(X2)	Enhanced, nonlinear

Table 4. Hyperparameter search spaces of three models.

Classification model	Hyperparameters search spaces	Best parameters
RF	n_estimators: 10-1000; max_depth: 2-32; min_samples_split: 2-20; max_feature: [0.5, ’sqrt’, ’log2’].	n_estimators: 483; max_depth: 17; min_samples_split: 3; max_feature: 0.5.
LGBM	n_estimators: 200-1000; max_depth: 3-8; num_leaves, 8-256; reg_alpha: 1e-3-10.0; reg_lambda 1e-3-10.0; colsample_bytree: 0.4-1.0; learning_rate: 0.01-0.3; subsample: 0.4-1.0; min_child_samples: 5-100.	n_estimators: 535; max_depth: 5; num_leaves, 109; reg_alpha: 0.3764; reg_lambda 0.0018; colsample_bytree: 0.533; learning_rate: 0.024; subsample: 0.562; min_child_samples: 12.
LSTM	batch_size: [32, 64, 128, 256, 512]; hidden_size: [32, 64, 128, 256, 512]; learning_rate: 0.001-0.01.	batch_size: 32; hidden_size: 256; learning_rate: 0.0079.

Figure 6. Spearman correlation coefficient between landslides and influence factors.

Figure 7. Interaction effects of influence factors of landslide based on geodetector (red indicates enhanced, bi; blue indicates enhanced, nonlinear).

Figure 8. (a) Receiver operating characteristic curves for the training set; (b) Receiver operating characteristic curves for the test set.

Table 5. Results of model accuracy evaluation.

Data set	Model	ACC	PPV	TPR	F1
Training set	RF	0.9615	0.9278	1	0.9625
	LGBM	0.8684	0.8155	0.9485	0.8770
	LSTM	0.7304	0.6965	0.8067	0.7475
Test set	RF	0.7792	0.7448	0.8769	0.8055
	LGBM	0.7771	0.7488	0.8613	0.8012
	LSTM	0.7459	0.6945	0.9124	0.7887

Figure 9. Landslide susceptibility map.

Table 6. Landslide susceptibility zoning statistics.

Landslide susceptibility	Number of grids	Percentage of area/%	Number of landslides	Percentage of landslides/%	Landslide density/(Pcs·km^2)
Very low	23344	35.23	9	0.24	0.0004
Low	9708	15.48	43	1.17	0.0044
Moderate	11987	19.12	177	4.80	0.0148
High	13482	21.50	1231	33.37	0.0913
Very high	4183	6.67	2229	60.42	0.5329

Figure 10. SHAP-based feature importance. (a) Factor importance plot; (b) summary plot of SHAP.

Table 7. Performance metrics for RF model under different hyperparameter optimization methods.

Data set	Methods	ACC	PPV	TPR	F1	AUC
Training set	Manual tuning	0.8648	0.7936	0.9821	0.8778	0.9423
	Grid search	0.8651	0.7926	0.9852	0.8784	0.9438
	Random search	0.8651	0.7926	0.9852	0.8784	0.9438
	Bayesian optimization	0.8853	0.8143	0.9952	0.8957	0.9664
	Optuna	0.9615	0.9278	1	0.9625	0.9997
Test set	Manual tuning	0.7650	0.7274	0.8782	0.7957	0.8308
	Grid search	0.7663	0.7295	0.8769	0.7964	0.8292
	Random search	0.7663	0.7295	0.8769	0.7964	0.8292
	Bayesian optimization	0.7697	0.7294	0.8873	0.8007	0.8313
	Optuna	0.7792	0.7448	0.8769	0.8055	0.8387

Figure 11. Importance of features built into the RF model.

Table 8. Model prediction accuracy of the RF model using 22 or 23 drought factors.

	ACC	PPV	TPR	F1	AUC
All	0.7792	0.7448	0.8769	0.8055	0.8387
Elevation	0.7603	0.7377	0.8381	0.7847	0.8335
DistFaults	0.7738	0.7425	0.8666	0.7998	0.8373
DistRoads	0.7549	0.7250	0.8536	0.7841	0.8307
DistRivers	0.7725	0.7425	0.8627	0.7981	0.8380
Lithology	0.7752	0.7436	0.8679	0.8010	0.8386
LULC	0.7738	0.7414	0.8692	0.8002	0.8345
NDVI	0.7664	0.7346	0.8640	0.7940	0.8305
POP	0.7535	0.7294	0.8381	0.7800	0.8308
PRCPTOT	0.7738	0.7420	0.8679	0.8000	0.8396
R10MM	0.7677	0.7357	0.8653	0.7952	0.8385
R20MM	0.7677	0.7362	0.8640	0.7950	0.8379
R50MM	0.7738	0.7436	0.8640	0.7993	0.8385
R95P	0.7758	0.7450	0.8666	0.8012	0.8394
R99P	0.7758	0.7455	0.8653	0.8010	0.8390
RX1D	0.7670	0.7364	0.8614	0.7940	0.8371
RX5D	0.7725	0.7409	0.8666	0.7988	0.8368
SA	0.7765	0.7458	0.8666	0.8017	0.8391
SDII	0.7711	0.7398	0.8653	0.7976	0.8367
Slope	0.7752	0.7453	0.8640	0.8002	0.8390
Soiltype	0.7704	0.7384	0.8666	0.7974	0.8378
SR	0.7725	0.7409	0.8666	0.7988	0.8374
TR	0.7758	0.7418	0.8744	0.8026	0.8385
TWI	0.7731	0.7412	0.8679	0.7995	0.8372

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Data availability statement

The data presented in this study are available on request from the corresponding author.