Development Of Water Quality Prediction Model For Narmada River Using Artificial Neural Networks

The lack of a universal system for analysis, prediction, and storage of water quality and condition of rivers in Madhya Pradesh has led to uneven policy-making and poor management ultimately posing issues in health, irrigation and keep increasing pollution in rivers. This study is a part of developing a central system for river water quality assessment and prediction. The conventional method of water quality assessment is based on the calculation of the water quality index which can be very complex and time-consuming. This paper aims to develop a water quality prediction model with the help of an Artificial Neural Network (ANN) for predicting the water quality of the Narmada River using two machine learning algorithms Levenberg and Gradient Descent and the results were compared. This research uses the surface water historical data of years 2018, 2019 of the river Narmada with monthly time intervals. Data is obtained from the Central Pollution Control Board resource called Narmada Automatic Sampling Collection Stations System. For training the network 10 water quality parameters including, DO, BOD, Turbidity, pH, etc. After training the networks were accessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Coefficient of Correlation (R) out of which 2 best performing networks with 7 ( Training R = 0.80083, Testing R = 0.5767) and 19 (Training R = 0.6594, Testing R = 0.7424) Neurons in the hidden layer, were selected from Levenberg algorithm and, 5 (Training R = 0.7670, Testing R = 0.8123) & 17 (Training R = 0.8631, Testing R = 0.8981) Neurons in the hidden layer were selected from Gradient descent algorithm. This simplifies the calculation of WQI take care if any sampling station is out of service and data is not available for some reason. Further, the aim is to refine the prediction location-wise to be able to make a better decision when & where to implement the measures to reduce the pollution or the knowledge level of treatment required to make the water fit for use beforehand. This would be helpful in the treatment of water for use in Domestic or Irrigation Purposes.

Application of Soil Water Assessment Tool (SWAT) Model in Analyzing Nitrogen Transport Inside the Narmada River Basin

The Narmada River is one of the largest rivers in Western India encompassing a watershed area of 92,672 km2. It is one of the most important rivers for water needs of the state of Gujarat, Maharashtra, and Madhya Pradesh. The climate of the basin is humid and tropical but region surrounding this river watershed is predominantly dry and resembles semi-arid conditions. The population inside the states covering this watershed increased by an average of 23% from 1991 to 2011 causing multitude of water scarcity and water quality deterioration issues. These problems were caused by increase in sewage waste and untreated industrial discharge dumped into the river stream along with chemical fertilizers washing off from the farmlands flowing into the river. While there are several studies that model the watershed hydrology and water balance components, there has been no study that analyses the transport of nutrients inside the watershed. This study aims at using a semi-distributed hydrological model—Soil Water Assessment Tool (SWAT) to model the nitrogen (NO2 + NO3) transport and distribution inside the basin for 2001–2019. Nutrients and discharge data from Central Water Commission (CWC) of India were used to build this model along with other required input forcing obtained through remotely sensed datasets. We found that the subbasins near boundary of the Narmada watershed are experiencing significant increase in nitrogen concentrations at an estimated rate of 0.0001–0.002 mg/L/yr. The potential reason for such increase is high rate of conversion of forested land to agricultural land causing usage of fertilizers that are rich in nitrogen.

Mechanisms controlling major ion chemistry and its suitability for irrigation of Narmada River, India

Surface water chemistry of the upper Narmada River was investigated at 13 different locations for 4 consecutive years (2017 to 2020) during pre- and post-monsoon seasons. The main objective of the study was to identify the processes governing the water chemistry of Narmada River and evaluate its suitability for irrigation. The physical parameters estimated were; pH (7.9 ± 0.4 for pre- and 8 ± 0.4 for post-monsoon seasons), EC (322.8±93.3 μS/cm for pre- and 312.1±80.2 μS/cm for post-monsoon) and TDS (203.4±41.5 mg/L for pre-and 213.4±48 mg/L for post-monsoon). The obtained concentration of cations and anions were in the order of Ca++ > Na+ > Mg++> K+ and HCO3−> Cl−>SO4−> NO3−> PO4− respectively. Thus, the water of Narmada was found to be alkaline in nature. Piper diagram inferred that the water was dominated by Ca-Mg-HCO3− type of hydrochemical faces. Gibb's plot clarified that rock-water interaction regulates the ion chemistry of the Narmada. Various indices like sodium percentage (Na%), sodium absorption ration (SAR), Kelly index (Ki), permeability index (PI), magnesium hazard (MH) was calculated which showed that the surface water was suitable for irrigation. Lastly, one-way ANOVA (p < 0.05) confirmed no significant differences in water quality except for temperature, EC and SO4−, for pre- and post-monsoon season.

Investigation and Study of Some Angiospermic Seeds with Special References to Germination and Their Viability in Tribal District Dhar (M.P.), India

An extensive and intensive plant survey in different areas of Dhar district of Madhya Pradesh was carried out in the year 2020-2021. Dhar district is situated in the South-western part of Madhya Pradesh with highly rich floristic biodiversity of plant. The total area of district is 8153 sq. km. of which forest encompasses 1370 sq. km. covering 15.79 percent of its geographical area and covered by Vindhyan scab, Malwa plateau and Narmada basin. The tribal of this area includes Bhil, Bhilala, Barela and Pateliya are the major tribes inhabiting the area and depending on forest. The present study highlights the seeds germination and their viability in different sites of Dhar district of Madhya Pradesh, India. 30 angiospermic seed diversity of higher plants was undertaken. Preliminary study of Seeds germination and their viability survey conducted in the different 11 sites of tribal district Dhar (M.P.), India and reported 29 species under 25 genera and 13 families. Leguminosae is most dominant families with 14 species fallowed by Combretaceae with 4 species and other remains families having one species. It is also noticed that 29 species are used by the ethnic communities of the district for various purposes. In the present communications hindi name, scientific name, family, filed notes and flowering and fruiting periods have been provided. Keywords: Dhar, CAMPA, Seed germination, Seed viability, Narmada River, Malwa plateau, Vindhyan scab

Flood vulnerability assessment using data envelopment analysis – The case of Narmada river basin districts in central India

This paper presents a Data Envelopment Analysis (DEA) based framework for estimating the flood vulnerabilities in River basins. The methodology has been exemplified for the 21 districts of the Narmada River basin in central India. Sensitivity and adaptive capacity indicators have been identified and used for the development of the Flood Vulnerability Index (FVI). DEA based study was employed to assess the Scale Efficiencies and the Returns to Scale and insights drawn from the analysis have been discussed in the context of policy and planning related to reduction of flood vulnerabilities. Cluster analysis has also been deployed to classify districts in terms of flood vulnerabilities. Results from the flood vulnerability assessment model case study indicate that 76% of the districts in the Narmada River Basin remain highly vulnerable to flood-risk, while the socio-economic parameters and physical sizes of districts and their resources play a crucial role.

Hydrologic Similarity Based on Width Function and Hypsometry: An Unsupervised Learning Approach

In ungauged or data-scarce watersheds, systematic analyses of a set of proximate watersheds (for example, selected based on locational proximity or similarity in climate, morphometry, lithology, soils, and vegetation) have been shown to lend significant insights regarding hydrologic response and prediction. Current approaches often rely on: (a) statistical regression models that use measurable watershed attributes, such as area, slope, and stream length; and (b) comparative hydrology that considers watershed characteristics to assess hydrologic similarity to select analogous gauged watersheds as proxies. Newer conceptions regarding hydrologic similarity focus on hydrologic response and therefore emphasize the use of dynamical measures of the stream network and watershed terrain. For example, the width function and hypsometric curve can be readily estimated using the available global digital terrain datasets and represented as functional forms involving a small set of parameters, thus achieving significant data reduction. In this study, a new approach to hydrological similarity in watersheds, one that utilizes these functional forms to identify dynamically similar watersheds, is presented. Dissimilarity matrices are created based on divergence measures, and watersheds are classified using hierarchical clustering. The joint analysis of watershed width functions and hypsometric curves allows for the classification of watersheds into a reduced number of dynamically-similar groups. An illustrative case study for the Narmada River, with 72 sub-watersheds, is presented.