AIMC Topic: Rain

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ANN modelling of sediment concentration in the dynamic glacial environment of Gangotri in Himalaya.

Environmental monitoring and assessment
The present study explores for the first time the possibility of modelling sediment concentration with artificial neural networks (ANNs) at Gangotri, the source of Bhagirathi River in the Himalaya. Discharge, rainfall and temperature have been consid...

Microplastics assessment in the lower stretch of the Ganga River sediment from East Indian region: Influence of land use and rainfall patterns.

Chemosphere
Microplastic (MP) pollution is increasingly viewed as a serious threat to waterways. However, little is known about the effects of land use and rainfall patterns on the occurrence and distribution of MPs in the river sediments. Herein, the MP polluti...

Formation mechanism analysis and the prediction for compound flood arising from rainstorm and tide using explainable artificial intelligence.

Journal of environmental management
The compounded effect of heavy rainfall and high tide backwater significantly exacerbate the load on urban drainage systems in coastal cities, leading to an escalating risk of compound flood disasters. The formation mechanism of compound floods is of...

Hybrid Physical Mechanism and Artificial Intelligence-Based Model for Evaluating Nonpoint Source Pesticide Pollution at a Megacity Scale.

Environmental science & technology
Large-scale nonpoint source (NPS) pesticide pollution is a growing concern in urban areas; however, modeling of such pollution is constrained by challenges in acquiring urban pipeline data and the scarcity of pollutant monitoring data. This study pre...

Exploring the morpho-tectonic nature, hydrological and physical characteristics of a watershed and prioritizing sub-watersheds surface runoff potentialities by integrating MCDM and ensemble machine learning models.

Journal of environmental management
Much effective rainfall often leads to natural and human-induced hazards when unused. Therefore, monitoring and managing water resources by assessing comprehensive surface runoff (SR) potential is crucial instead of relying on broad sub-watershed (SW...

Meteorological and traffic effects on air pollutants using Bayesian networks and deep learning.

Journal of environmental sciences (China)
Traffic emissions have become the major air pollution source in urban areas. Therefore, understanding the highly non-stational and complex impact of traffic factors on air quality is very important for building air quality prediction models. Using re...

Image rain removal network based on checkerboard transformer and CNN hybrid mechanism.

PloS one
In this paper, a novel hybrid network called ChessFormer is proposed for the single image de-rain task. The network seamlessly integrates the advantages of Transformer and fitted neural network (CNN) in a checkerboard architecture, fully utilizing th...

[Multi-factor Impact Analysis of Grassland Phenology Changes on the Qinghai-Xizang Plateau Based on Interpretable Machine Learning].

Huan jing ke xue= Huanjing kexue
The vegetation phenology of the Qinghai-Xizang Plateau is changing significantly in the context of climate change. However, there are many hydrothermal factors affecting the phenology, and few studies have focused on the effects of multiple factors o...

A fuzzy TOPSIS-based approach for prioritizing low-impact development methods in high-density residential areas.

Water science and technology : a journal of the International Association on Water Pollution Research
The study successfully implemented six low-impact development (LID) methods to manage surface runoff in urban areas: green roof, infiltration trench, bio retention cell, rain barrel, green roof combined with infiltration trench, and rain barrel combi...

Enhancing rainfall-runoff model accuracy with machine learning models by using soil water index to reflect runoff characteristics.

Water science and technology : a journal of the International Association on Water Pollution Research
The advancement of data-driven models contributes to the improvement of estimating rainfall-runoff models due to their advantages in terms of data requirements and high performance. However, data-driven models that rely solely on rainfall data have l...