Watershed Prioritization and Decision Making Based On Weighted Sum Analysis, Feature Ranking and Machine Learning Techniques.

doi:10.21203/rs.3.rs-359657/v1

Prediction and validation of Compound factors for prioritization of watersheds is an essential application using Machine Learning (ML) Techniques in water resources engineering. In the current paper, a method is proposed to derive 14 morphometric and 3 Topo-hydrological parameters using Remote Sensing (RS) and Geographical Information System (GIS), whereas prediction and validation of compound factor using ML techniques. Compound factor (CF) values are calculated using Weighted Sum Analysis (WSA), ReliefF, correlation coefficient techniques. A ten-fold cross-validation technique is applied to two machine learning models Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). Predication accuracy of models has been further achieved by feature ranking. The accuracy of ML models is evaluated with three parameters, Mean Absolute Error (MEA), Correlation Coefficient (CC), and Root Mean Square Error (RMSE). With the ranked features and Ten-fold cross-validation, prediction results were found to be better. The methodology will be useful for the accurate prediction of CF values and to reduce the uncertainty in watershed prioritization for conservation techniques for soil and water.

Civil Engineering

Morphometric analysis

RS

GIS

ML

Prioritization

Table 1 Methodology adopted for computations of morphometric and topo-hydrological parameters

Table 2 Stream order

WS No.	I(Stream_O)	II(Stream_O)	III(Stream_O)	IV(Stream_O)	V(Stream_O)	VI(Stream_O)	Total N	L (Length)	Rb (Bifurcation ratio)	Basin relief
1	36	21	12	5	1	0	75	45.5	2.72	368
2	8	6	0	0	0	0	14	18.6	1.33	202
3	59	31	15	9	0	0	114	52.5	1.88	541
4	14	12	3	0	2	0	31	22.2	2.22	203
5	31	14	13	0	0	0	58	22.5	1.65	493
6	16	6	0	0	10	0	32	18.1	1.63	143
7	23	9	12	0	0	0	44	20.3	1.65	154
8	24	12	9	1	2	0	48	17.2	3.21	162
9	4	1	1	0	4	0	10	4.7	1.75	47
10	5	0	1	0	4	0	10	3.6	2.63	41
11	38	21	15	0	0	0	74	30.8	1.60	170
12	19	8	8	1	6	0	42	22.6	2.89	59
13	5	0	2	0	4	0	11	15.7	1.50	62
14	40	18	4	11	0	0	73	38.8	2.36	80
15	39	16	20	0	0	0	75	44.5	1.62	206
16	20	13	3	0	6	0	42	47.3	2.12	78
17	27	9	6	6	1	0	49	31.0	2.88	135
18	7	4	3	0	0	0	14	14.6	1.54	67
19	7	4	2	0	2	0	15	23.3	1.58	40
20	4	0	5	0	0	0	9	9.2	0.80	59
21	19	12	2	3	5	0	41	34.1	2.21	83
22	1	0	0	0	1	0	2	15.5	1.00	32
23	39	19	11	8	0	0	77	47.5	1.72	73
24	11	4	4	0	1	0	20	36.4	2.58	67
25	8	7	0	0	0	0	15	12.3	1.14	65
26	1	0	1	0	3	0	5	4.2	0.67	23
27	7	3	0	0	4	0	14	11.7	1.54	65
28	2	0	0	0	0	0	2	5.3	2.00	39
29	1	0	1	0	1	1	4	20.2	1.00	44
30	10	4	1	1	9	0	25	22.9	1.90	48
31	3	1	0	1	0	0	5	8.2	2.00	42
32	1	1	0	3	0	0	5	8.6	0.67	38
33	3	3	2	0	0	0	8	12.4	1.25	43
34	28	18	5	6	1	0	58	33.8	3.00	51
35	15	6	7	0	0	0	28	16.4	1.68	30
36	6	3	0	1	0	2	12	22.1	1.83	34
37	5	4	0	1	0	0	10	17.2	2.63	50
38	2	1	0	3	0	0	6	10.8	1.17	30
39	1	0	0	1	2	0	4	7.3	0.75	25
40	1	2	0	0	0	1	4	13.7	1.25	17
41	38	22	10	5	1	0	76	33.9	2.73	50
42	0	0	1	0	2	0	3	4.0	0.50	20
43	6	2	0	0	0	3	11	13.9	1.83	40
44	10	6	0	0	4	1	21	30.5	2.39	27
45	13	7	5	0	1	0	26	15.6	2.75	34
46	3	1	0	0	0	3	7	14.4	1.67	34
47	1	0	0	0	0	2	3	5.4	0.50	10
48	3	0	0	0	0	0	3	4.5	3.00	26
49	1	0	0	0	0	0	1	4.4	1.00	38
50	2	3	0	0	0	3	8	26.8	0.83	58
51	2	1	0	0	0	0	3	5.9	2.00	35

Table 3 Parameters values

Ws No	R_b	D_d	F_u	R_t	L_o	R_f	B_s	R_e	C_c	I_f	R_c	C	R_h	R_n	STI	SPI	TWI
1	2.72	4.05	6.67	2.95	0.12	0.42	2.39	0.73	2.14	27.01	0.22	0.25	9.61	1.49	4.55	-0.03	7.81
2	1.33	2.74	2.06	0.62	0.18	0.45	2.23	0.76	2.43	5.66	0.17	0.36	12.98	0.55	2.02	-0.47	8.08
3	1.88	2.84	6.17	3.96	0.18	0.39	2.56	0.71	1.89	17.53	0.28	0.35	10.30	1.54	7.81	0.53	7.54
4	2.22	6.24	8.71	1.59	0.08	0.49	2.05	0.79	2.92	54.38	0.12	0.16	9.14	1.27	2.41	-0.25	7.70
5	1.65	3.95	10.19	3.14	0.13	0.46	2.18	0.76	2.18	40.27	0.21	0.25	21.93	1.95	9.42	1.44	6.23
6	1.63	2.03	3.59	1.75	0.25	0.43	2.32	0.74	1.73	7.28	0.34	0.49	7.90	0.29	1.33	-0.83	9.24
7	1.65	3.78	8.18	2.56	0.13	0.46	2.16	0.77	2.09	30.91	0.23	0.26	7.58	0.58	2.01	-0.32	7.78
8	3.21	3.51	9.80	3.82	0.14	0.47	2.14	0.77	1.60	34.36	0.39	0.29	9.43	0.57	2.30	0.01	7.18
9	1.75	3.65	7.74	1.40	0.14	0.56	1.78	0.85	1.77	28.22	0.32	0.27	9.97	0.17	1.52	-0.90	9.10
10	2.63	2.24	6.28	1.30	0.22	0.55	1.83	0.83	1.73	14.03	0.34	0.45	11.52	0.09	1.73	-0.50	8.81
11	1.60	4.90	11.76	3.63	0.10	0.45	2.21	0.76	2.29	57.55	0.19	0.20	5.52	0.83	2.43	0.26	6.80
12	2.89	3.18	5.92	1.98	0.16	0.45	2.25	0.75	2.25	18.85	0.20	0.31	2.61	0.19	1.39	-0.85	8.74
13	1.50	0.92	0.64	0.33	0.55	0.39	2.53	0.71	2.25	0.59	0.20	1.09	3.94	0.06	0.96	-1.48	9.82
14	2.36	3.36	6.32	2.85	0.15	0.42	2.40	0.73	2.12	21.25	0.22	0.30	2.06	0.27	0.93	-1.15	9.65
15	1.62	2.55	4.30	1.67	0.20	0.39	2.54	0.71	3.03	10.95	0.11	0.39	4.63	0.53	1.06	-1.24	9.37
16	2.12	2.46	2.18	1.25	0.20	0.39	2.57	0.70	2.15	5.36	0.22	0.41	1.65	0.19	0.84	-1.55	10.06
17	2.88	3.59	5.68	1.64	0.14	0.43	2.31	0.74	2.86	20.37	0.12	0.28	4.36	0.48	1.05	-1.23	9.18
18	1.54	2.14	2.06	0.72	0.23	0.45	2.23	0.75	2.12	4.40	0.22	0.47	4.59	0.14	0.65	-1.66	10.27
19	1.58	5.30	3.41	1.20	0.09	0.47	2.11	0.78	1.67	18.06	0.36	0.19	1.72	0.21	0.80	-1.46	9.64
20	0.80	1.55	1.52	0.53	0.32	0.46	2.19	0.76	1.98	2.36	0.25	0.64	6.43	0.09	0.69	-1.63	10.08
21	2.21	3.15	3.79	1.61	0.16	0.42	2.38	0.73	2.18	11.91	0.21	0.32	2.44	0.26	0.68	-1.72	10.10
22	1.00	2.93	0.38	0.12	0.17	0.46	2.16	0.77	2.02	1.11	0.25	0.34	2.07	0.09	1.07	-1.52	9.93
23	1.72	2.71	4.40	2.66	0.18	0.39	2.54	0.71	1.95	11.94	0.26	0.37	1.54	0.20	0.74	-1.68	10.24
24	2.58	2.17	1.19	0.68	0.23	0.40	2.53	0.71	2.04	2.59	0.24	0.46	1.84	0.15	0.66	-1.65	10.32
25	1.14	1.43	1.73	0.78	0.35	0.43	2.31	0.74	1.85	2.47	0.29	0.70	5.27	0.09	0.61	-1.79	10.13
26	0.67	1.98	2.35	0.53	0.25	0.52	1.91	0.82	1.82	4.64	0.30	0.51	5.45	0.05	0.75	-1.66	10.06
27	1.54	1.19	1.42	0.58	0.42	0.43	2.35	0.74	2.17	1.70	0.21	0.84	5.53	0.08	0.85	-1.42	10.13
28	2.00	1.20	0.45	0.12	0.42	0.47	2.11	0.78	2.22	0.54	0.20	0.84	7.34	0.05	0.54	-1.75	10.30
29	1.00	1.83	0.36	0.13	0.27	0.42	2.39	0.73	2.57	0.66	0.15	0.55	2.18	0.08	1.36	-1.56	10.41
30	1.90	2.27	2.47	1.03	0.22	0.42	2.36	0.73	2.16	5.61	0.21	0.44	2.09	0.11	0.81	-1.41	10.10
31	2.00	1.57	0.95	0.35	0.32	0.46	2.16	0.77	1.76	1.49	0.32	0.64	5.10	0.07	0.56	-1.66	10.72
32	0.67	1.69	0.98	0.24	0.30	0.47	2.15	0.77	2.64	1.66	0.14	0.59	4.40	0.06	0.91	-1.78	10.64
33	1.25	1.53	0.99	0.38	0.33	0.44	2.29	0.75	2.08	1.51	0.23	0.65	3.46	0.07	0.66	-1.51	10.49
34	3.00	1.95	3.35	1.86	0.26	0.39	2.54	0.71	2.12	6.55	0.22	0.51	1.51	0.10	0.77	-1.61	10.11
35	1.68	3.08	5.25	1.99	0.16	0.46	2.16	0.77	1.71	16.17	0.34	0.32	1.82	0.09	0.55	-1.71	10.44
36	1.83	1.61	0.87	0.49	0.31	0.41	2.46	0.72	1.86	1.40	0.29	0.62	1.54	0.05	0.67	-1.75	10.69
37	2.63	1.25	0.73	0.40	0.40	0.41	2.46	0.72	1.92	0.91	0.27	0.80	2.91	0.06	0.52	-1.74	10.81
38	1.17	2.03	1.13	0.24	0.25	0.46	2.16	0.77	3.09	2.30	0.11	0.49	2.79	0.06	0.69	-1.94	10.84
39	0.75	2.57	1.42	0.35	0.19	0.50	1.98	0.80	1.93	3.64	0.27	0.39	3.45	0.06	0.93	-1.43	10.11
40	1.25	3.79	1.11	0.33	0.13	0.49	2.05	0.79	1.81	4.19	0.31	0.26	1.24	0.06	0.92	-1.94	10.76
41	2.73	2.40	5.39	3.34	0.21	0.41	2.47	0.72	1.71	12.94	0.34	0.42	1.48	0.12	0.68	-1.78	10.11
42	0.50	3.66	2.73	0.48	0.14	0.57	1.74	0.85	1.70	10.00	0.35	0.27	4.97	0.07	1.60	-1.36	10.50
43	1.83	1.42	1.12	0.46	0.35	0.43	2.35	0.74	2.17	1.59	0.21	0.70	2.87	0.06	0.84	-1.86	10.70
44	2.39	3.59	2.48	1.31	0.14	0.43	2.30	0.74	1.55	8.89	0.41	0.28	0.89	0.10	0.77	-1.78	10.49
45	2.75	2.64	4.41	1.76	0.19	0.46	2.19	0.76	1.72	11.62	0.34	0.38	2.19	0.09	0.56	-1.92	10.43
46	1.67	2.77	1.35	0.31	0.18	0.46	2.15	0.77	2.80	3.73	0.13	0.36	2.36	0.09	0.85	-1.92	10.83
47	0.50	2.62	1.45	0.30	0.19	0.53	1.90	0.82	1.94	3.81	0.26	0.38	1.85	0.03	1.11	-2.20	10.74
48	3.00	1.06	0.71	0.22	0.47	0.48	2.09	0.78	1.85	0.76	0.29	0.94	5.80	0.03	0.38	-1.88	10.74
49	1.00	0.68	0.15	0.05	0.74	0.45	2.22	0.76	2.10	0.10	0.23	1.48	8.58	0.03	0.40	-1.79	11.02
50	0.83	2.36	0.70	0.20	0.21	0.42	2.40	0.73	3.31	1.66	0.09	0.42	2.16	0.14	0.39	-1.76	10.95
51	2.00	1.38	0.70	0.25	0.36	0.48	2.10	0.78	1.63	0.96	0.38	0.73	5.91	0.05	0.89	-0.95	9.99

Table 4 Preliminary priority ranking

Ws No	R_b	D_d	F_u	R_t	L_o	R_f	B_s	R_e	C_c	I_f	R_c	C	R_h	R_n	STI	SPI	TWI
1	8	4	7	6	48	11	38	11	32	7	19	4	6	3	3	5	7
2	37	20	27	28	31	24	26	26	43	24	9	19	2	8	7	8	8
3	21	18	10	1	32	1	50	2	18	12	34	18	4	2	2	2	4
4	14	1	4	18	51	44	7	44	48	2	4	1	8	4	5	6	5
5	28	5	2	5	45	29	21	27	37	3	14	5	1	1	1	1	1
6	30	33	19	14	17	17	33	17	9	22	42	33	10	11	14	10	13
7	29	7	5	9	46	30	16	32	27	5	24	6	11	6	8	7	6
8	1	12	3	2	40	38	13	33	2	4	50	12	7	7	6	4	3
9	24	9	6	19	41	50	2	50	12	6	40	8	5	18	11	12	11
10	9	30	9	21	22	49	3	49	10	14	43	30	3	26	9	9	10
11	32	3	1	3	49	25	24	28	42	1	10	3	17	5	4	3	2
12	4	14	11	11	36	26	28	23	40	10	11	14	32	16	12	11	9
13	36	50	47	39	2	2	45	3	41	49	12	50	26	38	19	22	17
14	13	13	8	7	39	12	40	12	29	8	20	13	40	12	20	14	16
15	31	25	17	15	26	3	47	4	49	19	2	24	22	9	17	16	14
16	16	26	26	22	27	4	51	1	33	26	21	26	45	17	27	25	20
17	5	10	12	16	42	18	31	18	47	9	5	10	25	10	18	15	12
18	34	32	28	26	20	27	27	24	30	28	22	32	23	20	42	30	31
19	33	2	20	23	50	39	11	40	4	11	48	2	44	14	30	21	15
20	46	41	30	30	11	31	22	29	23	35	28	40	13	27	35	28	22
21	15	15	18	17	37	13	37	13	38	17	15	15	33	13	37	35	23
22	42	17	49	49	35	32	17	34	24	44	29	17	39	28	16	24	18
23	25	21	16	8	33	5	48	5	22	16	30	21	46	15	34	33	30
24	11	31	35	27	21	7	46	6	25	33	27	31	42	19	40	29	33
25	41	43	29	25	8	19	32	19	15	34	35	43	19	29	43	43	28
26	48	35	25	31	18	47	5	47	14	27	38	35	18	45	33	31	21
27	35	48	32	29	4	20	34	20	35	37	16	47	16	33	25	19	29
28	17	47	48	50	5	40	12	41	39	50	13	48	12	46	47	37	32
29	43	37	50	48	15	14	39	14	44	48	8	37	36	34	13	26	34
30	20	29	24	24	23	15	36	15	34	25	17	29	38	23	29	18	24
31	18	40	41	37	12	33	18	35	11	42	41	41	20	35	44	32	43
32	49	38	40	44	14	41	14	36	45	38	7	38	24	39	23	40	40
33	38	42	39	36	10	23	29	25	26	41	25	42	27	36	41	23	37
34	2	36	21	12	16	6	49	7	31	23	23	36	48	24	31	27	25
35	26	16	14	10	38	34	19	37	6	13	44	16	43	30	46	34	36
36	22	39	42	32	13	8	42	8	17	43	36	39	47	47	39	38	41
37	10	46	43	35	6	9	43	9	19	46	32	46	29	40	48	36	47
38	40	34	36	45	19	35	20	38	50	36	3	34	31	41	36	49	49
39	47	24	33	38	28	46	6	46	20	32	33	25	28	42	21	20	26
40	39	6	38	40	47	45	8	45	13	29	39	7	50	43	22	50	46
41	7	27	13	4	24	10	44	10	7	15	45	27	49	22	38	41	27
42	50	8	22	33	43	51	1	51	5	20	47	9	21	37	10	17	39
43	23	44	37	34	9	21	35	21	36	40	18	44	30	44	28	45	42
44	12	11	23	20	44	22	30	22	1	21	51	11	51	25	32	42	38
45	6	22	15	13	29	36	23	30	8	18	46	22	35	31	45	47	35
46	27	19	34	41	34	37	15	39	46	31	6	20	34	32	26	48	48
47	51	23	31	42	30	48	4	48	21	30	31	23	41	49	15	51	44
48	3	49	44	46	3	42	9	42	16	47	37	49	15	50	51	46	45
49	44	51	51	51	1	28	25	31	28	51	26	51	9	51	49	44	51
50	45	28	45	47	25	16	41	16	51	39	1	28	37	21	50	39	50
51	19	45	46	43	7	43	10	43	3	45	49	45	14	48	24	13	19

Table 5 Weight assighned by WSA Techniques

Parameters	R_b	D_d	F_u	R_t	L_o	R_f	B_s	R_e	C_c	I_f	R_c	C	R_h	R_n	STI	SPI	TWI
R_b	1	0.143167	0.395837	0.511403	-0.13683	0.267602	-0.2705	0.298462	0.148507	0.351041	-0.15357	0.142805	-0.04688	0.327692	-0.02462	0.181086	0.264253
D_d	0.143167	1	0.734118	0.581538	-0.99765	-0.16371	0.208778	-0.15837	0.03819	0.844163	-0.02326	0.999457	-0.03348	0.675566	0.590679	0.433484	0.502805
F_u	0.395837	0.734118	1	0.919367	-0.71719	0.011765	0.022443	0.035294	0.104615	0.979095	-0.08299	0.743077	0.232036	0.784072	0.551041	0.594299	0.712217
R_t	0.511403	0.581538	0.919367	1	-0.56443	0.32543	-0.29502	0.351222	0.16371	0.877466	-0.14145	0.591403	0.095023	0.797285	0.40914	0.553846	0.679819
L_o	-0.13683	-0.99765	-0.71719	-0.56443	1	0.167149	-0.21077	0.16371	-0.039	-0.83077	0.024706	-0.99674	0.067511	-0.65466	-0.56706	-0.40081	-0.47158
R_f	0.267602	-0.16371	0.011765	0.32543	0.167149	1	-0.98724	0.991493	-0.29864	-0.02986	0.304163	-0.1552	-0.2857	0.355385	-0.09765	0.086063	0.099548
B_s	-0.2705	0.208778	0.022443	-0.29502	-0.21077	-0.98724	1	-0.98751	0.296833	0.065882	-0.299	0.200905	0.34009	-0.30199	0.16905	-0.00606	-0.01738
R_e	0.298462	-0.15837	0.035294	0.351222	0.16371	0.991493	-0.98751	1	-0.28262	-0.00977	0.287421	-0.14968	-0.28688	0.369321	-0.09176	0.087149	0.113756
C_c	0.148507	0.03819	0.104615	0.16371	-0.039	-0.29864	0.296833	-0.28262	1	0.084615	-0.99747	0.033213	-0.07557	-0.19041	-0.14271	-0.06045	-0.04217
I_f	0.351041	0.844163	0.979095	0.877466	-0.83077	-0.02986	0.065882	-0.00977	0.084615	1	-0.0638	0.851222	0.153846	0.80371	0.577376	0.580543	0.692217
R_c	-0.15357	-0.02326	-0.08299	-0.14145	0.024706	0.304163	-0.299	0.287421	-0.99747	-0.0638	1	-0.01783	0.085249	0.213937	0.153303	0.082986	0.059367
C	0.142805	0.999457	0.743077	0.591403	-0.99674	-0.1552	0.200905	-0.14968	0.033213	0.851222	-0.01783	1	-0.0248	0.687692	0.596742	0.443801	0.515837
R_h	-0.04688	-0.03348	0.232036	0.095023	0.067511	-0.2857	0.34009	-0.28688	-0.07557	0.153846	0.085249	-0.0248	1	0.23086	0.41276	0.559186	0.478552
R_n	0.327692	0.675566	0.784072	0.797285	-0.65466	0.355385	-0.30199	0.369321	-0.19041	0.80371	0.213937	0.687692	0.23086	1	0.545973	0.697647	0.765611
STI	-0.02462	0.590679	0.551041	0.40914	-0.56706	-0.09765	0.16905	-0.09176	-0.14271	0.577376	0.153303	0.596742	0.41276	0.545973	1	0.750136	0.732127
SPI	0.181086	0.433484	0.594299	0.553846	-0.40081	0.086063	-0.00606	0.087149	-0.06045	0.580543	0.082986	0.443801	0.559186	0.697647	0.750136	1	0.885339
TWI	0.264253	0.502805	0.712217	0.679819	-0.47158	0.099548	-0.01738	0.113756	-0.04217	0.692217	0.059367	0.515837	0.478552	0.765611	0.732127	0.885339	1
Some of Correlation	3.399457	5.375475	7.019095	6.855747	-5.16443	1.590588	-1.07149	1.731222	-0.25937	6.926968	0.431765	5.4619	2.90181	7.107692	5.564525	6.468235	6.970317
Total	61.3095	61.3095	61.3095	61.3095	61.3095	61.3095	61.3095	61.3095	61.3095	61.3095	61.3095	61.3095	61.3095	61.3095	61.3095	61.3095	61.3095
Weightage	0.055447	0.087678	0.114486	0.111822	-0.08424	0.025944	-0.01748	0.028237	-0.00423	0.112984	0.007042	0.089087	0.047331	0.115931	0.090761	0.105501	0.113691

Table 6 Final priority ranking based on CF(WSA)

Ws No		CF(WSA)	Rank
1	1.528264		2
2	16.01509		13
3	4.903464		6
4	3.938099		4
5	1.335835		1
6	18.4453		16
7	5.845198		7
8	4.310874		5
9	11.61175		11
10	17.83164		15
11	1.943757		3
12	10.66848		9
13	36.81942		37
14	10.70105		10
15	15.71252		12
16	22.29458		19
17	9.879781		8
18	29.97648		28
19	16.69544		14
20	33.24179		31
21	18.98612		17
22	31.16441		29
23	20.29711		18
24	29.3392		27
25	35.69458		36
26	34.10074		35
27	33.69377		32
28	45.27464		49
29	37.55711		38
30	24.4378		22
31	39.75809		43
32	40.25462		46
33	38.47929		41
34	24.55777		24
35	24.50611		23
36	40.02803		45
37	41.60316		48
38	41.5177		47
39	31.92323		30
40	33.82409		33
41	22.54344		20
42	24.59601		25
43	39.93747		44
44	24.13611		21
45	26.92503		26
46	33.90714		34
47	38.02727		40
48	47.48223		50
49	51.12991		51
50	38.61711		42
51	38.00189		39

Table 7 Weightage of Parameter According to ReliefF and Correlation Coefficient

Sr. No	Parameters	Weightage ReliefF	Weightage Correlation co efficient
1	Rb	0.00532	0.3689
2	Dd	0.02866	0.8069
3	Fu	0.05694	0.9197
4	Rt	0.04566	0.8659
5	Lo	0.02452	-0.7855
6	Rf	-0.03597	0.1328
7	Bs	-0.03246	-0.071
8	Re	-0.03282	0.1487
9	Cc	-0.0182	-0.0267
10	If	0.05803	0.9373
11	Rc	-0.0198	0.0484
12	C	0.02878	0.8161
13	Rh	0.01382	0.2701
14	Rn	0.05113	0.9104
15	STI	0.02103	0.7203
16	SPI	0.02622	0.7744
17	TWI	0.03788	0.8488

Table 8 Final priority ranking based on CF(RF)

Ws No	CF (RF)	Rank
1	0.27492	6
2	3.86089	14
3	0.91985	8
4	-0.89169	4
5	-1.35562	1
6	3.9834	15
7	0.06385	5
8	-0.92929	3
9	0.61581	7
10	2.26766	10
11	-1.03453	2
12	2.01432	9
13	11.72068	45
14	2.52398	12
15	4.34046	16
16	6.8578	25
17	2.45127	11
18	7.48339	27
19	3.32148	13
20	8.05952	30
21	5.2402	17
22	9.42732	35
23	5.57612	21
24	8.96685	32
25	9.17599	33
26	7.56216	28
27	8.90357	31
28	12.40651	47
29	11.83673	46
30	6.78556	24
31	10.70476	41
32	10.58357	39
33	10.62681	40
34	6.6908	23
35	5.31351	18
36	12.51027	48
37	12.74387	49
38	11.12328	42
39	7.92383	29
40	9.44803	36
41	5.53433	20
42	5.48176	19
43	11.28255	43
44	6.89754	26
45	5.98962	22
46	9.38026	34
47	9.71542	37
48	12.77914	50
49	14.36773	51
50	11.60586	44
51	9.95714	38

Table 9 Final priority ranking based on CF(CC)

Ws No	CF(CC)	Rank
1	6.7236	3
2	122.861	13
3	40.4664	7
4	16.962	4
5	1.6663	1
6	147.303	17
7	34.9447	6
8	26.2914	5
9	77.8723	11
10	136.1282	15
11	3.8269	2
12	77.2943	9
13	299.5558	40
14	77.4664	10
15	124.3031	14
16	174.8333	20
17	70.8866	8
18	232.9969	28
19	112.5607	12
20	261.6969	34
21	142.7939	16
22	234.603	29
23	154.4773	18
24	231.492	27
25	282.7589	36
26	261.2875	33
27	270.836	35
28	358.6792	49
29	296.0889	38
30	190.649	24
31	312.3009	44
32	311.6823	43
33	303.8441	42
34	195.7769	25
35	179.0141	23
36	316.2031	46
37	333.7207	48
38	319.6869	47
39	240.4292	30
40	244.6926	31
41	174.0157	19
42	175.2774	21
43	315.9518	45
44	176.7408	22
45	202.8793	26
46	254.4021	32
47	283.0729	37
48	375.3013	50
49	405.6693	51
50	299.0939	39
51	300.9122	41

Table 10 Priority Category based on CF (RF) values

Zone	CF(RF) value	Ws no	% Area Covered
Very High	-1.355 to 1.789	1,3,4,5,7,8,9 and 11	13.45
High	1.789 to 4.933	2,6,10,12,14,15,17 and 19	15.72
Moderate	4.933 to 8.078	16,18,20,21,23,26,30,34,35,39,41,42,44 and 45	30.24
Low	8.078 to 11.223	22,24,25,27,31,32,33,38,40,46,47 and 51	18.81
Very Low	>11.223	13,28,29,36,37,43,48,49 and 50	21.78

Table 11 Range of Comparison using SVM Ten-fold

	Ideal Values	CF(WSA)	CF(RF)	CF(CC)
Correlation coefficient	1	1	1	1
MAE	0	0.0472	0.0236	0.3684
RMSE	0	0.0565	0.032	0.4361

Table 12 Range of Comparison using MLP Ten-fold

	Ideal Values	CF(WSA)	CF(RF)	CF(CC)
Correlation coefficient	1	0.9946	0.9949	0.9938
MAE	0	0.9021	0.2856	7.6514
RMSE	0	1.4233	0.4402	12.2689

Watershed Prioritization and Decision Making Based On Weighted Sum Analysis, Feature Ranking and Machine Learning Techniques.

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