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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...
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Open access
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Predicting Sandstorms and Drought Using Artificial Intelligence Techniques Based on Climate Data: A Case Study of Jifarah Plain, Libya
Sandstorms and drought are major climate hazards in North Africa, particularly across Libya's Jifarah Plain, where population, agriculture, and critical infrastructure are concentrated. This study develops an artificial-intelligence framework for predicting drought severity and sandstorm occurrence using monthly climat...
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Modeling Flood Prediction Using Logistic Regression Enhanced with Machine Learning
Flooding remains one of the most destructive natural disasters in Nigeria, particularly in Lokoja, Kogi State, due to its location at the confluence of the Niger and Benue rivers. This study develops a data-driven flood prediction model by integrating logistic regression with machine learning techniques to improve earl...
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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 (1994–2024). Four models Ridge Regression, Random Forest, Multi-Layer Perceptron (MLP) and a stacking ensemble (Ensemble_...
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Multi-Temporal Satellite Observations and Machine Learning-Based Flood Susceptibility Assessment of the 2025 Punjab Flood
Over the past decade, the Punjab plains of Northern India have experienced recurrent flooding driven by hydroclimatic variability, specifically shifts in western disturbances that have intensified monsoon precipitation. Following a devastating flood in 2025, the region remains highly vulnerable to hydrological extremes...
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Open access
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A machine learning and neural network approach to flood forecasting in the Wupper river.
Floods are among the most destructive natural disasters, necessitating accurate and timely prediction systems to mitigate their impact. 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...
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