Introduction to Real Time Rainfall
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An integrated urban flood model for Bengaluru city, Karnataka, India has been developed with a modular approach, which includes real-time rainfall and weather monitoring, and a rainfall forecast system feeding into a comprehensive hydrologic flood model.
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The raw real-time rainfall data acquired was pertained to three regions of Karnataka North, South and Coastal.
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However, the GSMaP Gauge cannot be used to provide real-time rainfall data and is, therefore, inadequate for real-time flood forecasting.
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Near real-time rainfall monitoring at local scale is essential for urban flood risk mitigation.
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The method presented in this study can be used not only to reasonably and efficiently select target sites for flood prevention facilities, but also to pre-detect areas vulnerable to flooding by using real-time rainfall forecasting.
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Accurate and real-time rainfall estimation is a pressing need for forecasting the flood disaster and reducing the loss.
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The provision of high resolution near real-time rainfall data has made satellite rainfall products very potential for monitoring hydrological hazards.
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The flow analysis is conducted considering the shrink/swell behavior of the Yazoo clay with the real-time rainfall events, as observed in the rain gauges.
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Both real-time rainfall data from next-generation radar (NEXRAD) and gaging stations, and forecasted rainfall are needed to make gate control decisions (reservoir releases) in real-time so that at time t, rainfall is known and rainfall over the future time-period (Δt) to time t + Δt can be forecasted.
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In the domains dominated by complex topographies, quasi-periodicities, and non-linearities, deep learning (DL)–based methods provide an efficient solution in downscaling rainfall data for regional climate forecasting and real-time rainfall observation data at high spatial resolutions.
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Real-time rainfall data can be used in conjunction with streamflow monitoring to determine when extreme precipitation has occurred within the ungauged watercourses catchment which may result in flooding.
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Various forms of phosphorus and runoff process were monitored, and local real-time rainfall data were also collected.
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In particular, due to rainfall concentrated in a short period of time during the flood season, it is necessary to plan the exact amount of dam discharge using real-time rainfall forecasting information.
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In this paper, real-time rainfall conditions are incorporated into landslide prediction through a unique rainstorm-based database of reported landslides.
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In this research, a real-time nowcasting system for regional landslide-hazard assessment under extreme-rainfall conditions was established by integrating a real-time rainfall data retrieving system, a landslide-susceptibility analysis program (TRISHAL), and a real-time display system to show the stability of regional slopes in real time and provide an alert index under rainstorm conditions for disaster prevention and mitigation.
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In view of this, the effectiveness of integrating satellite-based near real-time rainfall, i.
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The newly designed predictive model utilizes statistically significant lagged <inline-formula> <tex-math notation="LaTeX">$I_{F}$ </tex-math></inline-formula>, improved by antecedent and real-time rainfall data to forecast the next daily <inline-formula> <tex-math notation="LaTeX">$I_{F}$ </tex-math></inline-formula> value.
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This study developed a real-time rainfall forecasting system that can predict rainfall in a particular area a few hours before a typhoon’s arrival.
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  With this, rainfall-landslide correlation models have been established and updated regularly through statistical means to facilitate the prediction of the severity of landslide based on real-time rainfall recorded in the raingauge network and the rainfall forecast by the Hong Kong Observatory (HKO).
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Therefore, a real-world case study was conducted in Wadi al Wala for real-time rainfall forecasting and flood control, using 38 years of daily data from 13 rain gauge stations in the region.
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Obtaining near-real time rainfall data from rain gauge stations are not easy in most developing countries like Ethiopia.
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