Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping

Figures & data

Figure 1. Location of the study area and spatial distribution of landslide inventories.

Figure 3. Description and distribution of the nominal parameters: (a) lithology, (b) land use, (c) slope aspect and (d) soil type.

Figure 4.  The graphical methodology of applied procedure for landslide hazard zonation.

Figure 5.  The general structure of MLP.

Figure 6. General mechanism of (a) ABC algorithm and (b) ABC-ANN method.

Figure 7. General mechanism of (a) PSO algorithm and (b) PSO-ANN method.

Table 1.  The spatial relationship between the landslide and its causative factors.

Factor	Classes	No. of pixels	Percentage of domain	No. of prone pixels	Percentage of prone pixels
Slope degree	0–10°	1,292,418	79.23	15,435	99.31
	10–20°	293,940	18.02	108	0.69
	20–30°	40,950	2.51	0	0.00
	30–40°	3807	0.23	0	0.00
	>40°	63	0.00	0	0.00
Slope aspect	North (0–22.5°)	40,158	2.46	0	0.00
	North-East	55,953	3.43	225	1.45
	East	58,023	3.56	126	0.81
	South-East	89,091	5.46	513	3.30
	South	103,032	6.32	531	3.42
	South-West	100,998	6.19	1305	8.40
	West	112,365	6.89	549	3.53
	North-West	228,420	14.00	657	4.23
	North (337.5–360°)	71,658	4.39	270	1.74
	Flat	771,480	47.30	11,367	73.13
Elevation (m)	200–300	348,750	21.38	6228	40.07
	300–450	620,343	38.03	8685	55.88
	450–600	593,946	36.41	630	4.05
	600–750	60,102	3.68	0	0.00
	>750	8037	0.49	0	0.00
Plan Curvature	Concave (–4 – –0.0 01)	14,958	0.92	0	0
	Flat (–0.001–0.001)	1,601,955	98.21	15,543	100
	Convex (0.001–4)	14,265	0.87	0	0
Lithology	Group 1	37,710	2.31	45	0.29
	Group 2	11,898	0.73	0	0.00
	Group 3	51,201	3.14	0	0.00
	Group 4	4752	0.29	0	0.00
	Group 5	1,525,617	93.53	15,498	99.71
Soil Type	Group 1	97,506	5.98	306	1.97
	Group 2	172,800	10.59	0	0.00
	Group 3	1,360,872	83.43	15,237	98.03
Land use	A	5967	0.37	873	5.62
	B	260,253	15.95	3762	24.20
	C	6732	0.41	27	0.17
	D	48,825	2.99	0	0.00
	E	144,612	8.87	1359	8.74
	F	256,266	15.71	45	0.29
	G	22,248	1.36	0	0.00
	H	44,163	2.71	0	0.00
	I	347,481	21.30	5076	32.66
	J	213,075	13.06	2043	13.14
	K	155,880	9.56	612	3.94
	L	123,408	7.57	1017	6.54
	M	2268	0.14	729	4.69
Distance from road (m)	0–100	100,872	6.18	7137	45.92
	100–200	85,446	5.24	3537	22.76
	200–300	90,909	5.57	1611	10.36
	300–400	77,940	4.78	747	4.81
	>400	1,276,011	78.23	2511	16.16
Distance from river (m)	0–100	120,582	7.39	8613	55.41
	100–200	100,062	6.13	3663	23.57
	200–300	105,840	6.49	1035	6.66
	300–400	87,003	5.33	540	3.47
	>400	1,217,691	74.65	1692	10.89
Rainfall (mm/year)	476–491	145,440	8.92	1449	9.32
	491–503	228,321	14.00	3429	22.06
	503–514	313,902	19.24	2484	15.98
	514–524	380,691	23.34	2916	18.76
	524–535	562,824	34.50	5265	33.87
SPI	0–10	1,568,061	96.13	14,940	96.12
	10–20	22,194	1.36	108	0.69
	20–30	8892	0.55	9	0.06
	30–40	5958	0.37	162	1.04
	>40	26,073	1.60	324	2.08
TWI	–6––2	114,642	7.03	495	3.18
	–2–3	494,343	30.31	1206	7.76
	3–6	333,207	20.43	2277	14.65
	6–10	543,942	33.35	7866	50.61
	>10	145,044	8.89	3699	23.80

Figure 8. Sensitivity analysis for ANN based on the number of hidden neurons.

Table 2. Weight and biases of the ANN model.

Neuron (i)	i
	LSV = –0.0047 × Z1 – 0.1870 × Z2 – 0.1439 × Z3 – 0.0145 × Z4 – 0.1459 × Z5 – 1.1577
	Zi = Tansig (Wi1 × A + Wi2 × B + … + Wi12 × L + bi)
	Wi1	Wi2	Wi3	Wi4	Wi5	Wi6	Wi7	Wi8	Wi9	Wi10	Wi11	Wi12	bi
1	–0.4559	–0.3531	0.1034	0.4904	0.5098	–0.6757	–0.1303	0.4670	–0.5694	–0.5553	–0.3443	0.4398	1.6856
2	0.9809	–0.0107	–0.1714	0.1445	0.4505	0.0195	–0.6466	0.1435	–0.1218	–0.7780	0.3958	–0.2329	–0.5345
3	–0.1577	0.2106	0.1482	–0.1720	–0.8059	0.3104	0.6322	0.0982	0.6408	0.4697	0.0248	1.0436	0.143
4	0.6646	–0.1131	–0.1888	–0.0024	0.6835	0.5731	0.7293	0.8883	–0.0716	0.5503	0.2718	–0.3435	0.7654
5	–0.5450	–0.1343	0.8396	0.5029	–0.2736	–0.1200	–0.1491	–0.5532	–0.3939	0.1899	–0.3579	–0.8073	–1.4422

Figure 9. Sensitivity analysis for ABC-ANN based on colony size.

Table 3. Optimized weight and biases of the ABC-ANN model.

Neuron (i)	LSV = –0.0001 × Z1 + 0.0209 × Z2 + 0.0054 × Z3 + 0.0085 × Z4 – 0.1678 × Z5 – 0.8261
	Zi = Tansig (Wi1 × A + Wi2 × B + … + Wi12 × L + bi)
	Wi1	Wi2	Wi3	Wi4	Wi5	Wi6	Wi7	Wi8	Wi9	Wi10	Wi11	Wi12	bi
1	3.2219	5.1953	–51.9518	–75.6243	–6.8825	365.7876	–0.5138	–133.7595	–0.7071	–3.0083	–27.4672	–8.8390	192.7512
2	–1022.4356	–79.0813	–85.6538	36.9012	–24.4347	–29.3689	374.2896	–26.1480	–7.6598	3.7417	–12.1546	0.2060	–12.5510
3	11.9548	30.3340	80.5551	–7.7851	–100.3677	–51.5862	8.0825	83.1883	–12.0480	12.7368	–18.7648	–2.0783	–20.6393
4	126.5222	0.1142	–25.1545	–13.8558	–655.3529	68.3088	12.0814	–38.5672	8.8144	–48.6130	–77.2456	46.2577	13.1375
5	98.3364	21.7059	18.0298	–2.6837	–797.7548	–55.2426	0.2987	13.4053	886.3462	23.5468	130.8857	75.4644	168.7963

Table 4. Optimized weight and biases of the PSO-ANN model.

Neuron (i)	LSV = –0.0275 × Z1 + 0.0951 × Z2 + 0.0147 × Z3 + 0.1397 × Z4 + 0.5515 × Z5 – 0.6988
	Zi = Tansig (Wi1 × A + Wi2 × B + … + Wi12 × L + bi)
	Wi1	Wi2	Wi3	Wi4	Wi5	Wi6	Wi7	Wi8	Wi9	Wi10	Wi11	Wi12	bi
1	0.1016	–0.3819	0.5902	0.3872	0.4509	–0.2766	–0.4612	–0.0039	–0.0252	0.4933	–0.5502	0.0117	–0.3565
2	–0.6568	–0.3073	–0.1829	–0.1773	0.0377	–0.4657	0.0109	–0.7324	0.4008	0.0306	0.2257	0.5338	0.0009
3	0.1800	0.3027	0.5275	–0.7473	0.1486	–0.2882	–0.3661	0.5142	–0.1553	0.3940	–0.2452	–0.2334	0.3769
4	0.0142	–0.3506	–0.3637	0.6541	–0.6400	–0.2534	–0.2468	–0.6497	0.5182	–0.1110	–0.2621	–0.2952	0.1199
5	–0.0246	–0.3065	0.0827	–0.1199	0.1337	0.2980	0.0844	0.3235	–0.6117	0.1419	–0.3275	–0.2340	–0.8339

Figure 10. Sensitivity analysis for PSO-ANN based on swarm size.

Figure 11. A view of landslide hazard maps developed in GIS model (a) SI, (b) ANN, (c) ABC-ANN and (b) PSO-ANN estimation of LSVs.

Figure 12. Column chart based on the percentage of landslide hazard classes.

Figure 13. The ROC diagram, obtained for SI, ANN, ABC-ANN, and PSO-ANN models.