Stacked Ensemble with Machine Learning Regressors on Optimal Features (SMOF) of hyperspectral sensor PRISMA for inland water turbidity prediction.

Leveraging hyperspectral data across various domains yields substantial benefits, yet managing many spectral bands and identifying the essential ones poses a formidable challenge. This study identifies the most relevant bands within a hyperspectral data cube for turbidity prediction in inland water. Nine machine learning regressors Cat Boost, Decision Trees, Extra Trees, Gradient Boost, Light Gradient Boost (LightGBM), Recursive Feature Elimination (RFE), Random Forest, Support Vector Regressor (SVR), and Xtreme Gradient Boost (XGBoost) have been used to compute the feature importance of the hyperspectral bands for predicting turbidity.

Multi-Facet analysis of analytical and numerical models to resolve sustainable artificial recharge rates in unconfined aquifers.

Managed aquifer recharge (MAR) has emerged as a potential solution to resolve water insecurity, globally. However, integrated studies quantifying the surplus source water, suitable recharge sites and safe recharge capacity is limited. In this study, a novel methodology is presented to quantify transient injection rates in unconfined aquifers and generate MAR suitability maps based on estimated surplus water and permissible aquifer recharge capacity (PARC).

Analysing the relationship between human modification and land surface temperature fluctuation in the Ramganga basin, India.

In many regions across the world, including river basins, population growth and land development have enhanced the demand for land and other natural resources. The anthropogenic activities can be detrimental to the vital ecosystems that sustain the river basin region. This work assessed the impact of human modification on land surface temperature (LST) for the Ramganga basin in India.

Time series forecasting of temperature and turbidity due to global warming in river Ganga at and around Varanasi, India.

The fluctuation in the river ecosystem network due to climate change-induced global warming affects aquatic organisms, water quality, and other ecological processes. Assessment of climate change-induced global warming impacts on regional hydrological processes is vital for effective water resource management and planning. The global warming effect on river water quality has been analyzed in this work.

Analysis of algal bloom intensification in mid-Ganga river, India, using satellite data and neural network techniques.

River Ganga is one of the most significant rivers in the country. This river is the adobe for numerous aquatic species and microorganisms. The color of the river suddenly changed to green due to the rise of algal bloom in the Varanasi and nearby regions of the river Ganga during May-June 2021.

Drivers of functional diversity in the hyporheic zone of a large river.

The effects of regional (hydrogeology and geomorphology) and local (sediment and hydrology) characteristics on hyporheic assemblages were studied along a 40-km reach of a large gravel-bed river. Hyporheic water and fauna were sampled at the upstream and downstream positions of 15 large gravel bars. The resulting 30 stations varied in their sediment grain size, stability and direction of river-aquifer exchanges.

Flood inundation mapping and monitoring using SAR data and its impact on Ramganga River in Ganga basin.

Remote sensing-based flood inundation mapping and monitoring is very crucial input before, during, and after floods. Ganga-Ramganga doab is one of the prolonged flood-affected area in middle Ganga plain due to seasonal monsoon which leads to rise in water levels of Ganga and Ramganga rivers. The focus of the present study is to map severe flood condition captured through synthetic aperture radar (SAR) data during August-September 2018, and to explain the impact on Ramganga river morphology.