AIMC Topic: Rain

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Detection of climate change signals using precipitation and temperature time series by a hybrid deep learning framework.

Environmental monitoring and assessment
Climate change is one of the most extreme challenges of the twenty-first century. Precipitation (pr) and temperature variability are key indicators of climate change detection. Whereas hybrid deep learning (DL) models have been widely applied, their ...

Multi-step ahead streamflow and uncertainty forecasting using a HyMoLAP rainfall-runoff model-based framework integrated with Bayesian neural networks in the Ouémé river basin, Benin.

PloS one
Multi-step forecasting is crucial for capturing future streamflow variations and managing water resources but remains challenging due to limited accuracy of upstream flow forecasts and meteorological predictions over lead times. While data-driven met...

Causal inference unveils how forest coverage mitigates excess snakebite cases during rainfall seasons in Colombia.

Scientific reports
Snakebite envenoming is a neglected tropical disease that affects mainly rural populations, where antivenom is scarce. Understanding environmental drivers of snakebite incidence is critical for public health preparedness. This study employs causal in...

Machine learning-based ensemble of Global climate models and trend analysis for projecting extreme precipitation indices under future climate scenarios.

Environmental monitoring and assessment
Monitoring changes in climatic extremes is vital, as they influence current and future climate while significantly impacting ecosystems and society. This study examines trends in extreme precipitation indices over an Indian tropical river basin, anal...

Modeling impacts of climate-induced yield variability and adaptations on wheat and maize in a sub-tropical monsoon climate - using fuzzy logic.

Scientific reports
Climate change is causing more frequent and extraordinary extreme weather events that are already negatively affecting crop production. There is a need for improved climate risk assessment by developing smart adaptation strategies for sustainable fut...

Enhancing the resilience of urban drainage system using deep reinforcement learning.

Water research
Real-time control (RTC) is an effective method used in urban drainage systems (UDS) for reducing flooding and combined sewer overflows. Recently, RTC based on Deep Reinforcement Learning (DRL) has been proven to have various advantages compared to tr...

Cluster-based downscaling of precipitation using Kolmogorov-Arnold Neural Networks and CMIP6 models: Insights from Oman.

Journal of environmental management
Accurate precipitation predictions are crucial for addressing climate change impacts on water resources, especially in arid regions like Oman. Therefore, this study evaluates three machine learning models-Random Forest (RF), Multilayer Perceptron Neu...

Enhancement of standardized precipitation evapotranspiration index predictions by machine learning based on regression and soft computing for Iran's arid and hyper-arid region.

PloS one
Drought is a climate risk that affects access to safe water, crop development, ecological stability, and food production. Therefore, developing drought prediction methods can lead to better management of surface and groundwater resources. Similarly, ...

Flood resilience through hybrid deep learning: Advanced forecasting for Taipei's urban drainage system.

Journal of environmental management
The escalating impacts of climate change have intensified extreme rainfall events, placing urban drainage systems under unprecedented pressure and increasing flood risks. Addressing these challenges requires advanced flood mitigation strategies, opti...

Assessing the impact of rainfall, topography, and human disturbances on nutrient levels using integrated machine learning and GAMs models in the Choctawhatchee River Watershed.

Journal of environmental management
Nutrient pollution caused by excessive total nitrogen (TN) and total phosphorus (TP) is a significant environmental challenge globally, threatening water quality and ecosystem health. This study investigates the interplay between rainfall, topography...