<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This investigation additionally recognizes partner cooperation issue and furthermore to moderate the real issue through subjective and quantitative appraisal of riverine wetland. Considering a floodplain wetland in rustic West Bengal, the concentration was extended to perceive the type of wetland capacities as per the idea of individuals’ contribution by group examination. Be that as it may, NDVI was connected to ponder the total weeds condition into the wetland to decide the connection between Water Quality Index (WQI) with Normalized Difference Vegatation Index (NDVI) and its impact on valuation. In ANOVA, which is computed by MINITAB programming, centrality level was lower than 0.05 for each case.</p></abstract>

This investigation additionally recognizes partner cooperation issue and furthermore to moderate the real issue through subjective and quantitative appraisal of riverine wetland. Considering a floodplain wetland in rustic West Bengal, the concentration was extended to perceive the type of wetland capacities as per the idea of individuals’ contribution by group examination. Be that as it may, NDVI was connected to ponder the total weeds condition into the wetland to decide the connection between Water Quality Index (WQI) with Normalized Difference Vegatation Index (NDVI) and its impact on valuation. In ANOVA, which is computed by MINITAB programming, centrality level was lower than 0.05 for each case.

Study to Develop a Co-Relationship between Water Quality and Land use Pattern as Index of Riverine Wetland with Statistical Approach

Department of Geography, Women’s College, School of Water Resources Engineering

Quaestiones Geographicae

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This investigation additionally recognizes partner cooperation issue and furthermore to moderate the real issue through subjective and quantitative appraisal...

WQI (CCME Method)	Description	WQI (Weighted Arithmetic method)	Description
95–100	Excellent	0–25	Excellent
80–94	Good	26–50	Good
65–79	Fair	51–75	Bad
45–64	Marginal	76–100	Very Bad
0–44	Poor	>100	Unfit.

Year	Wetland area	Water body amount	Vegetation amount
Year	[ha]
2009	85.4	51.3	34.1
2011	85.4	37.8	47.6
2012	85.4	40.2	45.2
2013	85.4	51.4	34.0

Seasons	pH	Turbidity	TDS	Nitrate	Sulphate	TH	TC	DO	BOD
Seasons	[−]	[mg dm⁻³]
Pre-monsoon	8.73	79.7	108.67	0.6	2.67	86.67	133	3.93	2.30
Monsoon	7.50	63.3	99.50	0.4	2.01	80.34	120	2.98	1.89
Post-monsoon	8.15	74.8	104.67	0.5	2.45	83.23	128	3.11	2.05
Objectives	6.5–8.5	1	500	50	200	300	<2	4	3
	F1 = (2 / 9) × 100 = 22.22				F2 = (6 / 27) × 100 = 22.22
	F3 = (nse / .01nse + 0.01) = 93.71				$WQI = 100 - \frac{\sqrt{F 1^{2} + F 2^{2} + F 3^{2}}}{1.732} = 42.93$ {\rm{WQI}} = 100 - {{\sqrt {F1^2 + F2^2 + F3^2 } } \over {1.732}} = 42.93

Year	Production [kg]	Income [Rs]
2010–11	9429	1052837
2011–12	25555	3513492
2012–13	29413	5971355
2013–14	15333	1247279
2014–15	23775	3871711

Parameter	Observed Value (Vi)	Standard Value (Si)	Unit Weights (Wi)	Quality Rating (Qi)	Wi × Qi
pH	8.73	6.5–8.5	0.160	115	18.400
TDS	108.67	500 mg/L	0.003	21.7	0.065
TH	86.67	300 mg/L	0.004	29	0.116
DO	3.93	6 mg/L	0.220	124	27.280
BOD	2.30	2 mg/L	0.660	115	75.900

Study to Develop a Co-Relationship between Water Quality and Land use Pattern as Index of Riverine Wetland with Statistical Approach

Published Online: Mar 31, 2020

Page range: 33 - 43

Received: Feb 11, 2019

DOI: https://doi.org/10.2478/quageo-2020-0003

© 2020 Malabika Biswas Roy et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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Fig. 7

Category of Water Quality.

The variation in vegetation amount and beel area, after calculating pixel value.

Determination of WQI using CCME method.

The year range wise income and production.

Determination of WQI using weighted arithmetic method.