Browsing by Author "Shishir Gaur"

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Applications of remote sensing in water quality assessment
(Elsevier, 2023) Mohit Kumar Srivastava; Shishir Gaur; Anurag Ohri; Prashant K. Srivastava; Nikhilesh Singh
Water quality is undeniably an important indicator of the health of an environmental system. Its correct estimation is not only essential for human consumption but also necessary for the well-being of the entire ecosystem. Remote Sensing, coupled with Geographic Information System, has proven to be a powerful tool for monitoring water quality and water pollution. This technology has been tremendously successful in its application for management and planning despite its limitation of parameters under consideration. Remote Sensing reads and maps the spectral reflectance that it receives from a water body, and by using various wavelength bands such as visible, NIR, SWIR, and TIR, it identifies the relevant water quality characteristic. Several types of research and literature are available where the data obtained from these airborne or spaceborne remote sensors are used for planning, monitoring, management, and prediction of water characteristics across the globe. Sometimes in situ measurement is coupled with Remote Sensing to ensure the accuracy of the quantifications being made. The technology has also been used extensively to monitor groundwater quality aspects. Remotely sensed data come with the added advantage of being readily available even at the remotest of locations. While assessing water quality, remote sensing parameters such as suspended sediments, algae, turbidity, and chlorophyll concentration can easily be monitored. They can enable consultants and natural resource managers to develop management plans for a variety of natural resource management applications. © 2023 Elsevier Ltd. All rights reserved.
Evaluating groundwater depletion under natural and induced stresses: a numerical modeling approach toward aquifer sustainability
(IWA Publishing, 2024) Ankit Tewari; Prabhat Kumar Singh; Shishir Gaur; Ranveer Kumar; Shreyansh Mishra
The ever-increasing demand for freshwater has led to the overexploitation of aquifers. Despite its known importance, integrated studies reckoning the impact of external stress on budget components are limited. This study assessed the spatiotemporal impact of recharge and abstraction stresses in Lower Betwa River Basin (LBRB) aquifers, India, from 2003 to 2020, using SWAT and MODFLOW-NWT models. The simulated difference in groundwater inflow and outflow components was accounted by a net cumulative storage loss of 36.5 Mm3/year. Mann-Kendall trend analysis indicated that about 62 % of the LBRB showed a declining trend in groundwater levels (0 - 1.2 m/year), 30% of the area had no significant trend and around 8% area showed an increasing trend. Spatial storage variations indicated that 78% of basin area was under stable aquifer systems while 1.6% area was under very high storage stress. Application of management scenarios to reduce groundwater storage loss exhibited that a 20% reduction in abstraction rates would reduce storage loss by 29% and 16% in Bamaur and Gursarai blocks. An integrated approach of abstraction reduction and increased inflow through managed aquifer recharge was the most suitable management solution to offset groundwater depletion and achieve long term sustainability in the LBRB. © 2024 The Authors.
Exploring Simulation–Optimization for Sustainable Groundwater Management: A Critical Review
(John Wiley and Sons Inc, 2025) Shreyansh Mishra; Shishir Gaur; Mariem Kacem; Anurag Ohri
Simulation–optimization (S–O) is a well-regarded method for solving groundwater (GW) management problems. Although S–O has significantly improved the decision support system for GW management, it still lacks practical applicability. As a result, many researchers have been improving its components, leading to slightly or significantly better performance. To understand these challenges efficiently, this article delves into principal components of S–O that offer in-depth critical insights into GW's sustainability. The discussed segments are divided into simulation models, optimization methods, categories and conceptualization of management problems, and the formulation of real-world objective functions. This review also examines surrogate-assisted simulation models to reduce computational challenges. Methods to address model uncertainty and decision-making in applying S–O for sustained yield problems are addressed. The review outlays critical steps in S–O methodology and recommends potential research directions to aid researchers in further enhancing the practicality of S–O. © 2024 Wiley-VCH GmbH.
Globally validated non-unique inversion framework to estimate optically active water quality indicators using in situ and space-borne hyperspectral data sets
(Higher Education Press Limited Company, 2025) Shishir Gaur; Rajarshi Bhattacharjee; Shard Chander; Anurag Ohri; Prashant Kumar Srivastava
As water quality is a combination of multiple optically active parameters, there is a growing interest in probabilistic models to predict water quality. This study aims to add to the water quality prediction studies by introducing ensemble learning with deep learning-based mixture density networks with multiple probabilistic Gaussian distributions. We named the approach as Ensembled Gaussian Mixture Density Network (GMDN). Many existing water quality algorithms rely on localized data sets, which limits their applicability. This research addresses this by developing and evaluating the proposed model using the global in situ water quality data set GLORIA (Global Reflectance community data set for Imaging and optical sensing of Aquatic environments). We focused on estimating two key biogeochemical components (BPs): Total Suspended Solids (TSS) and Chlorophyll-a(Chla), along with one inherent optical property (IoP), the absorption coefficient of colored dissolved organic matter (αCDOM). The proposed approach performs quite reliably when evaluated on the data samples of individual countries. The GMDN algorithm has been fine-tuned on the satellite-matchup for the river Ganga near Varanasi city. The fine-tuning was implemented using the remote sensing reflectance (Rrs) of the spaceborne hyperspectral data set PRISMA (PRecursore IperSpettrale della Missione Applicativa). The contribution of the riverbed floor to the Rrs of PRISMA has been computed using physics-based simulations in the Water Color Simulator (WASI). Overall, the simultaneous use of multiple probabilistic distributions and ensembled architectures improves the predictive accuracy of WQ parameters compared to the existing operational algorithms. © Higher Education Press 2025.
Hyperspectral remote sensing: Potential prospects in water quality monitoring and assessment
(Elsevier, 2024) Mohit Kumar Srivastava; Shishir Gaur; Anurag Ohri; Prashant K. Srivastava; Sadashiv Chaturvedi
In recent decades, the field of remote sensing has made significant progress, especially in hyperspectral imaging, which has become an essential tool for civil, commercial, medical, and military applications. Hyperspectral sensors are capable of estimating physical parameters of complex surfaces and identifying visually similar materials with fine spectral signatures. This article focuses on the use of hyperspectral remote sensing, particularly in water quality assessment and monitoring. It highlights the importance of hyperspectral imageries in recent studies and discusses the working and types of hyperspectral data, as well as various space-borne and airborne sensors currently in use. Additionally, the article reviews various techniques and methods that researchers around the world have employed to use hyperspectral data for water quality applications. Lastly, the article discusses the advantages and challenges inherent in hyperspectral remote sensing. This chapter aims to serve as a comprehensive guide for those interested in hyperspectral remote sensing and its applications in water quality monitoring and assessment. © 2025 Elsevier Ltd. All rights reserved.
Stacked Ensemble with Machine Learning Regressors on Optimal Features (SMOF) of hyperspectral sensor PRISMA for inland water turbidity prediction
(Springer, 2024) Rajarshi Bhattacharjee; Shishir Gaur; Shard Chander; Anurag Ohri; Prashant K. Srivastava; Anurag Mishra
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. Random Forest has outperformed the other models with a mean absolute percentage error (MAPE) of 1.61%, and the R2 of the linear fit is 0.96. Band 77, with a central wavelength of 1067.61 nm, is the most dominating band regarding feature importance. We have also developed a novel framework for turbidity prediction: Stacked Ensemble with Machine Learning Regressors on Optimal Features (SMOF). It employs a stacking ensemble of the nine regressors mentioned above with Random Forest as both base and meta-model, leveraging feature selection outputs. With this framework, the MAPE (%) reached 1.21, while the R2 stood at 0.95. The present study also presents a simple statistical algorithm to detect noisy bands in the Hyperspectral Precursor of the Application Mission (PRISMA) data cube. The approach assesses quadrat-wise intra-band spatial coherence using Renyi’s entropy thresholding for noisy band segregation. Radiometric calibration error and absorption due to water vapour are the two primary sources of noise within the data cube. Moreover, this research implements the open-source Water Colour Simulator (WASI) to simulate inland water spectra with varied proportions of turbidity. Overall, the study presents an approach to identify noisy bands and integrates the potential wavelengths for turbidity prediction of inland waters. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.