Hydroinformatics to make farms SMART

Smart farming focuses on increasing farm productivity through the use of technology - both hardware and software. Smart farming is concerned with the management of farms, plantations, and all associated farming activities through the use of IoT, drones, robotics, machinery, and artificial intelligence to determine a path to predictable farm output.

Smart farming is concerned with the management of farm activities through the use of data obtained from various sources (historical, geographical, and instrumental). Technological advancement does not necessarily imply that a system is intelligent. Smart agriculture technologies distinguish themselves by their ability to collect and analyze data. Smart farming uses hardware (Internet of Things) and software (Software as a Service or SaaS) to collect data and provide actionable insights to manage all farm operations, both pre and post-harvest.

Hydroinformatics, on the other hand, is a subject that deals with the application of data science and ICT in hydrology, hydropower, and hydraulic engineering. Data is also collected and analyzed here, and the process of water management for the intended use is optimally controlled using hard and software with the help of the collected data.

This article will demonstrate five different products that illustrate "How Hydroinformatics is making a farm "smart."

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#smartfarming #watermanagement #smartwatermanagement #hydro

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1 “Flood peaks in Mid-Atlantic U.S. watersheds show a V-shaped response to urbanization: they decrease at low urban development (below 10% PDAW), then rise sharply as urbanization increases further due to complex climate-landscape interactions. A neural network model confirmed this nonlinear behavior, underscoring the need to consider more than just urban area when predicting flood risk.”…..Hua et.al.(2025). Click here to know more. 2 “Artificial neural network (ANN) models using climate indices outperformed ARIMA models for long-term streamflow forecasting at six Victorian stations, with the best ANN achieving Pearson R = 0.90, RMSE = 0.04, and MAE = 2.50 for six-month advance prediction. ANN’s superior accuracy highlights its suitability for operational streamflow forecasts in this region.ANN models using lagged climate indices significantly outperformed ARIMA models for forecasting streamflow at Victoria’s stations, especially at Acheron where the ANN achieved Pearson R = 0.90,...

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