Statistical and geostatistical modelling approach for spatio-temporal assessment of river water quality: a case study from lower stretch of River Ganga

Raman, Rohan Kumar; Bhor, Manisha; Manna, R. K.; Samanta, Srikanta; Das, Basanta Kumar

doi:10.1007/s10668-022-02472-7

Cited by 4 publications

(1 citation statement)

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“…Maps are used to depict how values change over time as a curve, and graphs are used to show how data taken from a satellite picture using the NDWI index is represented. On a map, the higher values that are getting close to +1 typically display blue and represent either a high-water content or a water surface, whereas the lower values that go all the way to -1 represent drought conditions, unless the area of interest is a non-aqueous surface (Raman et al, 2022). Table 1 includes the NDWI value interpretation.…”

Section: Figure 1 Initial and Final Point Of The Study Areamentioning

confidence: 99%

Forecasting seasonal TDS using a normalized difference water index in the Ganges River in Bangladesh

Khuku,

Sattar,

Mostafa

2023

Nep J Environ Sci

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The Ganges is one of the most remarkable rivers in Bangladesh. The river contributes to the surface water supply, which is significantly high in the northwest region of Bangladesh. The water quality of this river changed based on many environmental events, and rainfall was one of them. The research focused on finding the mathematical relation between the field observations of water quality parameters and the observed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. This study treated the Total Dissolved Solids (TDS) as one of the crucial parameters in the water quality index, which was monitored and forecasted using multiple linear regression from satellite-derived Normalized Difference Water Index (NDWI) from the Landsat imageries and Electrical Conductivity (EC) from the field observations for the pre-monsoon, monsoon, and post-monsoon periods. The predicted values returned significant correlation coefficient values compared to the field TDS values of 0.64, 0.71, and 0.52 for the three periods, respectively. The regression equations found from this study may be further utilized to predict the TDS values in the future years.

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Section: Figure 1 Initial and Final Point Of The Study Areamentioning

confidence: 99%