AIMC Topic: Rain

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Assessment of rainfall-derived inflow and infiltration in sewer systems with machine learning approaches.

Water science and technology : a journal of the International Association on Water Pollution Research
Rainfall-derived inflow/infiltration (RDII) modelling during heavy rainfall events is essential for sewer flow management. In this study, two machine learning algorithms, random forest (RF) and long short-term memory (LSTM), were developed for sewer ...

Integrating conceptual and machine learning models to enhance daily-Scale streamflow simulation and assessing climate change impact in the watersheds of the Godavari basin, India.

Environmental research
This study examined and addressed climate change's effects on hydrological patterns, particularly in critical places like the Godavari River basin. This study used daily gridded rainfall and temperature datasets from the Indian Meteorological Departm...

Anthropogenic fingerprints in daily precipitation revealed by deep learning.

Nature
According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe. However, verifying this prediction using observations has remained a substantial challenge owing to lar...

A new rainfall prediction model based on ICEEMDAN-WSD-BiLSTM and ESN.

Environmental science and pollution research international
Precipitation, as an important indicator describing the evolution of the regional climate system, plays an important role in understanding the spatial and temporal distribution characteristics of regional precipitation. Scientific and accurate predic...

Imputation of missing monthly rainfall data using machine learning and spatial interpolation approaches in Thale Sap Songkhla River Basin, Thailand.

Environmental science and pollution research international
Missing rainfall data has been a prevalent issue and primarily interested in hydrology and meteorology. This research aimed to examine the capability of machine learning (ML) and spatial interpolation (SI) methods to estimate missing monthly rainfall...

Progressive Rain Removal Based on the Combination Network of CNN and Transformer.

Computational intelligence and neuroscience
The rain removal method based on CNN develops rapidly. However, convolution operation has the disadvantages of limited receptive field and inadaptability to the input content. Recently, another neural network structure Transformer has shown excellent...

Improved runoff forecasting based on time-varying model averaging method and deep learning.

PloS one
In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constru...

A Survey of Deep Learning-Based Image Restoration Methods for Enhancing Situational Awareness at Disaster Sites: The Cases of Rain, Snow and Haze.

Sensors (Basel, Switzerland)
This survey article is concerned with the emergence of vision augmentation AI tools for enhancing the situational awareness of first responders (FRs) in rescue operations. More specifically, the article surveys three families of image restoration met...

Development of cluster analysis methodology for identification of model rainfall hyetographs and its application at an urban precipitation field scale.

The Science of the total environment
Despite growing access to precipitation time series records at a high temporal scale, in hydrology, and particularly urban hydrology, engineers still design and model drainage systems using scenarios of rainfall temporal distributions predefined by m...

Investigation of intra - event variations of total, dissolved and truly dissolved metal concentrations in highway runoff and a gross pollutant trap - bioretention stormwater treatment train.

Water research
Metals in stormwater can be toxic to organisms, particularly when occurring in truly dissolved form (fraction <3 kDa). Here, using 153 samples collected during six rains, we investigated intra-events variations of total, dissolved and truly dissolved...