[HTML][HTML] Predicting river water quality: an imposing engagement between machine learning and the QUAL2Kw models (case study: Aji-Chai, river, Iran)

J Sarafaraz, FA Kaleybar, JM Karamjavan… - Results in …, 2024 - Elsevier
Rivers play an essential role in supplying high-quality water to diverse sectors.
Understanding water quality indicators and systematic monitoring is crucial for water …

Enhancing Flood susceptibility modeling: a hybrid deep neural network with statistical learning algorithms for Predicting Flood Prone Areas

M Ghobadi, M Ahmadipari - Water Resources Management, 2024 - Springer
Flooding, with its environmental impact, represents a naturally destructive process that
typically results in severe damage. Consequently, accurately identifying flood-prone areas …

Surface Subsidence over a Coastal City Using SBAS-InSAR with Sentinel-1A Data: A Case of Nansha District, China

H Yu, B Li, Y ** in the heterogeneous Annaba aquifer system (SE Algeria)
S Hani, S Boudibi, N Bougherira, B Sakaa… - Modeling Earth Systems …, 2024 - Springer
The heterogeneity of the Annaba plain aquifer, located in northeastern Algeria and
characterized by a temperate Mediterranean climate with hot and dry summers, coupled with …

Uncertainty assessment of aquifer hydraulic parameters from pum** test data

AM Bashandy, HM Bekhit, HG Radwan - Applied Water Science, 2024 - Springer
Data from pum** tests is a noisy process, and therefore, performing the pum** test
numerous times will not get the same drawdown values. As a consequence, various …