An efficient video-based rainfall intensity estimation employing different recurrent neural network models

Rajabi, Farshid; Faraji, Neda; Hashemi, Masoumeh

doi:10.1007/s12145-024-01290-x

Earth Sci Inform

2024

DOI: 10.1007/s12145-024-01290-x

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An efficient video-based rainfall intensity estimation employing different recurrent neural network models

Farshid Rajabi,

Neda Faraji,

Masoumeh Hashemi

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2024

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Cited by 3 publications

References 38 publications

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Improved model development and feature ranking for rock permeability prediction by coupling petrophysical log data and ensemble machine learning techniques

Miah,

Abir,

Shuvo

2024

Earth Sci Inform

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Improved model development and feature ranking for rock permeability prediction by coupling petrophysical log data and ensemble machine learning techniques

Miah,

Abir,

Shuvo

2024

Earth Sci Inform

View full text Add to dashboard Cite

Balancing Results from AI-Based Geostatistics versus Fuzzy Inference by Game Theory Bargaining to Improve a Groundwater Monitoring Network

Hashemi,

Peralta,

Yost

2024

MAKE

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An artificial intelligence-based geostatistical optimization algorithm was developed to upgrade a test Iranian aquifer’s existing groundwater monitoring network. For that aquifer, a preliminary study revealed that a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) more accurately determined temporally average water table elevations than geostatistical kriging, spline, and inverse distance weighting. Because kriging is usually used in that area for water table estimation, the developed algorithm used MLP-ANN to guide kriging, and Genetic Algorithm (GA) to determine locations for new monitoring well location(s). For possible annual fiscal budgets allowing 1–12 new wells, 12 sets of optimal new well locations are reported. Each set has the locations of new wells that would minimize the squared difference between the time-averaged heads developed by kriging versus MLP-ANN. Also, to simultaneously consider local expertise, the algorithm used fuzzy inference to quantify an expert’s satisfaction with the number of new wells. Then, the algorithm used symmetric bargaining (Nash, Kalai–Smorodinsky, and area monotonic) to present an upgradation strategy that balanced professional judgment and heuristic optimization. In essence, the algorithm demonstrates the systematic application of relatively new computational practices to a common situation worldwide.

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Measuring Precipitation via Microwave Bands with a High-Accuracy Setup

Sakkas,

Christofilakis,

Lolis

et al. 2024

Sensors

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The urgent need for timely and accurate precipitation estimations in the face of ongoing climate change and the increasing frequency and/or intensity of extreme weather events underscores the necessity for innovative approaches. Recently, several studies have focused on estimating the precipitation rate through induced attenuation of radio frequency (RF) signals, which are abundant in modern communication systems. Most research has concentrated on frequencies exceeding 10 GHz, as attenuation at lower frequencies is minimal, posing measurement challenges. This study aims to confront this limitation by introducing a high-precision experimental setup capable of detecting this subtle attenuation at frequencies under 10 GHz. The setup includes a transmitter and receiver optimized for operation at 2.07, 4.63, and 6.22 GHz, where minimal worldwide research exists. A power resolution below 10−5 dB in preliminary measurements demonstrated its effectiveness in quantifying signal attenuation due to precipitation across the specified frequencies. Moreover, a strong power law relationship was observed between signal attenuation and precipitation rate for all three frequencies, while, as expected, the higher the frequency, the more pronounced the signal attenuation was.

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An efficient video-based rainfall intensity estimation employing different recurrent neural network models

Cited by 3 publications

References 38 publications

Improved model development and feature ranking for rock permeability prediction by coupling petrophysical log data and ensemble machine learning techniques

Improved model development and feature ranking for rock permeability prediction by coupling petrophysical log data and ensemble machine learning techniques

Balancing Results from AI-Based Geostatistics versus Fuzzy Inference by Game Theory Bargaining to Improve a Groundwater Monitoring Network

Measuring Precipitation via Microwave Bands with a High-Accuracy Setup

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