AIMC Topic: Rain

Clear Filters Showing 11 to 20 of 53 articles

Informing Risk Hotspots and Critical Mitigations for Rainstorms Using Machine Learning: Evidence from 268 Chinese Cities.

Environmental science & technology
Climate change is exacerbating rainstorms, increasing the risk of flooding and threatening urban sustainability, which could undermine climate action. Here, we propose a machine learning-based framework to assess heterogeneous risks and identify crit...

Deciphering the climate-malaria nexus: A machine learning approach in rural southeastern Tanzania.

Public health
OBJECTIVES: Malaria remains a critical public health challenge, especially in regions like southeastern Tanzania. Understanding the intricate relationship between environmental factors and malaria incidence is essential for effective control and elim...

A predictive fuzzy logic and rule-based control approach for practical real-time operation of urban stormwater storage system.

Water research
Predictive real-time control (RTC) strategies are usually more effective than reactive strategies for the intelligent management of urban stormwater storage systems. However, it remains a challenge to ensure the practicality of RTC strategies that us...

Multimodal Social Sensing for the Spatio-Temporal Evolution and Assessment of Nature Disasters.

Sensors (Basel, Switzerland)
Social sensing, using humans as sensors to collect disaster data, has emerged as a timely, cost-effective, and reliable data source. However, research has focused on the textual data. With advances in information technology, multimodal data such as i...

Deep learning insights into spatial patterns of stable isotopes in Iran's precipitation: a novel approach to climatological mapping.

Isotopes in environmental and health studies
Stable isotope techniques are precise methods for studying various aspects of hydrology, such as precipitation characteristics. However, understanding the variations in the stable isotope content in precipitation is challenging in Iran due to numerou...

Association of precipitation extremes and crops production and projecting future extremes using machine learning approaches with CMIP6 data.

Environmental science and pollution research international
Precipitation extremes have surged in frequency and duration in recent decades, significantly impacting various sectors, including agriculture, water resources, energy, and public health worldwide. Pakistan, being highly susceptible to climate change...

Interpretable machine learning guided by physical mechanisms reveals drivers of runoff under dynamic land use changes.

Journal of environmental management
Human activities continuously impact water balances and cycling in watersheds, making it essential to accurately identify the responses of runoff to dynamic changes in land use types. Although machine learning models demonstrate promise in capturing ...

Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements.

Environmental research
Rainwater Harvesting (RWH) is increasingly recognized as a vital sustainable practice in urban environments, aimed at enhancing water conservation and reducing energy consumption. This study introduces an innovative integration of nano-composite mate...

Machine learning ensembles, neural network, hybrid and sparse regression approaches for weather based rainfed cotton yield forecast.

International journal of biometeorology
Cotton is a major economic crop predominantly cultivated under rainfed situations. The accurate prediction of cotton yield invariably helps farmers, industries, and policy makers. The final cotton yield is mostly determined by the weather patterns th...

Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling.

Journal of environmental management
A deep learning architecture, denoted as CNNsLSTM, is proposed for hourly rainfall-runoff modeling in this study. The architecture involves a serial coupling of the one-dimensional convolutional neural network (1D-CNN) and the long short-term memory ...