Publications by authors named "Pradeep Goel"

Prediction and quantification of nutrient concentrations in surface water has gained substantial attention during recent decades because excess nutrients released from agricultural and urban watersheds can significantly deteriorate surface water quality. Machine learning (ML) models are considered an effective tool for better understanding and characterization of nutrient release from agricultural fields to surface water. However, to date, no systematic investigations have examined the implementation of different classification and regression ML models in agricultural settings to predict nutrient concentrations in surface water using a group of input variables including climatological (e.

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Excess nutrients in surface water and groundwater can lead to water quality deterioration in available water resources. Thus, the classification of nutrient concentrations in water resources has gained significant attention during recent decades. Machine learning (ML) algorithms are considered an efficient tool to describe nutrient loss from agricultural land to surface water and groundwater.

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Water quality within agricultural catchments is governed by management practices and climate conditions that control the transport, storage, and exchange of nutrients between components of the hydrologic cycle. This study aims to improve knowledge of nitrogen (N) and phosphorus (P) transport in low permeability agricultural watersheds by considering spatial and temporal trends of surface water nutrient concentrations in relation to hydroclimatic drivers, sediment quality, shallow hyporheic exchange, groundwater quality, and tile drain discharge over a 14-month field study in a clay hydrosystem of the Lake Huron basin, one of the five Great Lakes. Results found that events of varying magnitude and intensity enhanced nutrient release from overland flow and subsurface pathways.

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Proper identification of critical source areas (CSAs) is important for economic viability of any best management practices (BMPs) aimed at reducing sediment and phosphorus loads to receiving water bodies. Both continuous and event-based hydrologic and water quality models are widely used to identify and assess CSAs, however, their comparative assessment is lacking. In this study, we have used continuous Soil and Water Assessment Tool (SWAT) and event-based Agriculture Non-Point Source (AGNPS) pollution models to identify CSAs for sediment and phosphorus in a watershed in Ontario, Canada.

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Identifying critical source areas (CSAs) of a watershed by phosphorus (P) loss assessment tools is essential for optimal placement of beneficial management practices (BMPs) to address diffuse P pollution. However, lack of significant progress in tackling diffuse P pollution could be, in part, associated with inefficacy of P loss assessment tools for accurately identifying CSAs. Phosphorus loss assessment tools have been developed to simulate P loss from the landscape where runoff is mainly driven by rainfall events.

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Introduction: The severe long bone defects usually follow high-energy trauma and are often associated with a significant soft-tissue injury. The goal of management of these open long bone defects is to provide stable fixation with maintenance of limb length and soft-tissue coverage. The purpose of this article is to present the clinic-radiological outcome, complications and treatment of post-traumatic long bone defect with vascularised fibula transfer.

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