[HTML][HTML] Feature extraction from satellite-derived hydroclimate data: Assessing impacts on various neural networks for multi-step ahead streamflow prediction
Enhancing the generalization capability of time-series models for streamflow prediction
using dimensionality reduction (DR) techniques remains a major challenge in water …
using dimensionality reduction (DR) techniques remains a major challenge in water …
[HTML][HTML] River stream flow prediction through advanced machine learning models for enhanced accuracy
Abstract The Narmada River basin, a significant water resource in central India, plays a
crucial role in supporting agricultural, industrial, and domestic water supply. Effective …
crucial role in supporting agricultural, industrial, and domestic water supply. Effective …
[HTML][HTML] Water distribution system modelling of GIS-remote sensing and EPANET for the integrated efficient design
Urban settlement depends on water distribution networks for clean and safe drinking water.
This research incorporates geographic information systems (GIS), remote sensing (RS), and …
This research incorporates geographic information systems (GIS), remote sensing (RS), and …
Unveiling the nexus between atmospheric visibility, remotely sensed pollutants, and climatic variables across diverse topographies: A data-driven exploration …
S Javed, MI Shahzad, I Shahid - Atmospheric Pollution Research, 2024 - Elsevier
Deteriorating visual range (VR) can cause challenges for the transportation sector, resulting
in economic and life losses. Air pollutants, smoke, fog, and many meteorological parameters …
in economic and life losses. Air pollutants, smoke, fog, and many meteorological parameters …
[HTML][HTML] Comparative analysis of different rainfall prediction models: A case study of Aligarh City, India
This research paper delves into creating and comparing rainfall prediction models,
employing diverse machine learning algorithms, including Logistic Regression, Decision …
employing diverse machine learning algorithms, including Logistic Regression, Decision …
[HTML][HTML] STAM-LSGRU: a spatiotemporal radar echo extrapolation algorithm with edge computing for short-term forecasting
H Cheng, M Cui, Y Shi - Journal of Cloud Computing, 2024 - Springer
With the advent of Mobile Edge Computing (MEC), shifting data processing from cloud
centers to the network edge presents an advanced computational paradigm for addressing …
centers to the network edge presents an advanced computational paradigm for addressing …
Statistical downscaling of high-resolution precipitation in India using convolutional long short-term memory networks
Statistical downscaling of the General Circulation Model (GCM) simulations are widely used
for accessing climate changes in the future at different spatiotemporal scales. This study …
for accessing climate changes in the future at different spatiotemporal scales. This study …
Enhanced machine learning models development for flash flood mapping using geospatial data
Flash floods are dangerous and unpredictable. This study aimed to improve flash flood
prediction in Algeria's Hodna Basin using advanced AI models and GIS (GeoAI). Each …
prediction in Algeria's Hodna Basin using advanced AI models and GIS (GeoAI). Each …
[HTML][HTML] Machine learning techniques for flood forecasting
FAA Hadi, L Mohd Sidek, GH Ahmed Salih… - Journal of …, 2024 - iwaponline.com
Climate change resulted in dramatic change in the monsoon precipitation rates in Malaysia,
contributing to repetitive flooding events. This research examines different substantial …
contributing to repetitive flooding events. This research examines different substantial …
Spatial enhancement of Landsat-9 land surface temperature imagery by Fourier transformation-based panchromatic fusion
ABSTRACT Landsat-9 Panchromatic (PAN) band images are 7 times finer than land surface
temperature (LST) photos of the Thermal Infrared (TIR) band. PAN bands have superior …
temperature (LST) photos of the Thermal Infrared (TIR) band. PAN bands have superior …