Abstract
In this paper, an attempt has been made to develop a statistical model based on Internet of Things (IoT) for water quality analysis of river Krishna using different water quality parameters such as pH, conductivity, dissolved oxygen, temperature, biochemical oxygen demand, total dissolved solids and conductivity. These parameters are very important to assess the water quality of the river. The water quality data were collected from six stations of river Krishna in the state of Karnataka. River Krishna is the fourth largest river in India with approximately 1400 km of length and flows from its origin toward Bay of Bengal. In our study, we have considered only stretch of river Krishna flowing in state of Karnataka, i.e., length of about 483 km. In recent years, the mineral-rich river basin is subjected to rapid industrialization, thus polluting the river basin. The river water is bound to get polluted from various pollutants such as the urban waste water, agricultural waste and industrial waste, thus making it unusable for anthropogenic activities. The traditional manual technique that is under use is a very slow process. It requires staff to collect the water samples from the site and take them to the laboratory and then perform the analysis on various water parameters which is costly and time-consuming process. The timely information about water quality is thus unavailable to the people in the river basin area. This creates a perfect opportunity for swift real-time water quality check through analysis of water samples collected from the river Krishna. IoT is one of the ways with which real-time monitoring of water quality of river Krishna can be done in quick time. In this paper, we have emphasized on IoT-based water quality monitoring by applying the statistical analysis for the data collected from the river Krishna. One-way analysis of variance (ANOVA) and two-way ANOVA were applied for the data collected, and found that one-way ANOVA was more effective in carrying out water quality analysis. The hypotheses that are drawn using ANOVA were used for water quality analysis. Further, these analyses can be used to train the IoT system so that it can take the decision whenever there is abnormal change in the reading of any of the water quality parameters.
Highlights
In the world, and especially in India, river water is the main source for all anthropogenic activities such as drinking, irrigation and agriculture (Parmar and Bhardwaj 2014; Herojeet et al 2016).The river water quality is getting degraded day by day, and water pollution is the main reason for degradation in the recent years
(b) Dissolved oxygen (DO): The p value of the parameter 0.000 was less than α value of 0.05, so null hypotheses can be rejected
The Internet of Things (IoT) system was used to collect the data from identified stations for different water quality parameters such as Potential of hydrogen (pH), turbidity, DO, BOD, N O3, temperature and conductivity to generate a data set that was used to monitor the quality of water
Summary
Especially in India, river water is the main source for all anthropogenic activities such as drinking, irrigation and agriculture (Parmar and Bhardwaj 2014; Herojeet et al 2016).The river water quality is getting degraded day by day, and water pollution is the main reason for degradation in the recent years. The different industries that are active in the river Krishna basin region are sugar, cement, iron and steel, vegetable oil extraction and rice mills (Central Water Commission 2014). These industries produce the wastes such as (a) dirt and gravel, (b) masonry and concrete, (c) scrap metals, (d) trash, (e) oil, (f) chemicals, (g) effluents and suspended solids and (h) organic matters. These pollutants alter the physio-chemical characteristics of aquatic ecosystem because it has high concentration of BOD and TDS which cause rapid depletion of oxygen in water. All the rivers including river Krishna is monitored under Monitoring of Indian National Aquatic Resource System (MINARS) (Central Pollution Control Board 2013)
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