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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

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Open access Aug 2026

Machine Learning-Informed Hydrological Response Time Modeling in Tropical Watersheds

Flood occurrence in tropical regions is intensifying due to climate variability and land-use change, increasing the need for reliable flood response time estimation. Accurate prediction of flood lag time (TL)—the interval between the centroid of excess rainfall and peak runoff—is critical for flood early warning and wa...

Dagnenet Sultan, N. Haregeweyn, M. Tsubo et al. · 0 citations
Open access Aug 2026

Predicting Sandstorms and Drought Using Artificial Intelligence Techniques Based on Climate Data: A Case Study of Jifarah Plain, Libya

An artificial-intelligence framework for predicting drought severity and sandstorm occurrence using monthly climate data for 1985–2022 is developed and provides a reproducible approach that can be adapted to other arid regions of Libya and North Africa characterized by sparse ground-based monitoring networks.

Lutfiyah Abraham Mohammed Altarhouni · 0 citations
Open access Sep 2026

Modeling Flood Prediction Using Logistic Regression Enhanced with Machine Learning

A data-driven flood prediction model by integrating logistic regression with machine learning techniques to improve early warning systems in Nigeria and demonstrates that combining statistical modeling with machine learning improves flood prediction reliability and supports disaster management decision-making.

D. Shobanke, Happiness I. Olatunde, E. O. Ajare · 0 citations
Open access Sep 2026

A Hybrid Ensemble Learning Approach for Accurate SPI₆-Based Drought Forecasting in Semi-Arid Regions

This study proposes a hybrid machine learning framework to predict the six-month Standardized Precipitation Index (SPI₆) for meteorological drought assessment in Nanded, India, using NASA POWER data and demonstrates that ensemble learning enhances SPI prediction accuracy.

Rajesh H. Jadhav, Manisha K. Subhedar, Pradeep Kodag et al. · 0 citations
Open access Sep 2026

Multi-Temporal Satellite Observations and Machine Learning-Based Flood Susceptibility Assessment of the 2025 Punjab Flood

A balanced interaction between extreme rainfall, runoff potential, and topographic control in causing periodic floods in the Punjab plains reflects a balanced interaction between extreme rainfall, runoff potential, and topographic control.

Ankush Kumar, Ashwani Raju, Saraah Imran et al. · 0 citations
Open access 2026

A machine learning and neural network approach to flood forecasting in the Wupper river.

This study evaluates the performance of two machine learning models, - K-Nearest Neighbors (KNN) and Long Short-Term Memory (LSTM) networks - in predicting daily water levels based on hydrological and meteorological data from the Wupper River in Wuppertal, Germany.

G. Rocha, Alberto B. de Palhares, J. M. Varela et al. · 0 citations

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