A deep learning convolutional neural network algorithm for detecting saline flow sources and mapping the environmental impacts of the Urmia Lake drought in Iran

B Feizizadeh, MK Garajeh, T Lakes, T Blaschke - Catena, 2021 - Elsevier
Abstract Urmia Lake in Northern Iran is drying up, which is causing significant environmental
problems in the region, including saline storms that devastate agricultural land. We …

Flood hazard mapping in western Iran: assessment of deep learning vis-à-vis machine learning models

E Satarzadeh, A Sarraf, H Hajikandi, MS Sadeghian - Natural Hazards, 2022 - Springer
Abstract On March 25, 2019, widespread flood events occurred across Iran's provinces and
set a new record for socioeconomic losses and casualties. In hindsight, it opened an …

[HTML][HTML] Hydrological drought forecasting using machine learning—Gidra river case study

W Almikaeel, L Čubanová, A Šoltész - Water, 2022 - mdpi.com
Drought is one of many critical problems that could arise as a result of climate change as it
has an impact on many aspects of the world, including water resources and water scarcity. In …

A comprehensive investigation of the causes of drying and increasing saline dust in the Urmia Lake, northwest Iran, via ground and satellite observations, synoptic …

NH Hamzeh, K Shukurov, K Mohammadpour… - Ecological …, 2023 - Elsevier
Nowadays, dried lakes have turned into important dust sources with serious environmental,
climatic and socio-economic impacts. In this study, climatic, terrestrial and anthropogenic …

An automated deep learning convolutional neural network algorithm applied for soil salinity distribution mapping in Lake Urmia, Iran

MK Garajeh, F Malakyar, Q Weng, B Feizizadeh… - Science of the Total …, 2021 - Elsevier
Traditional soil salinity studies are time-consuming and expensive, especially over large
areas. This study proposed an innovative deep learning convolutional neural network (DL …

Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran

K Khosravi, M Panahi, A Golkarian, SD Keesstra… - Journal of …, 2020 - Elsevier
Iran experiences frequent destructive floods with significant socioeconomic consequences.
Quantifying the accurate impacts of such natural hazards, however, is a complicated task …

Hydrological drought forecasting using multi-scalar streamflow drought index, stochastic models and machine learning approaches, in northern Iran

P Aghelpour, H Bahrami-Pichaghchi… - … Research and Risk …, 2021 - Springer
Hydrological drought is an environmental event that affects surface water resources such as
surface runoff and reservoir levels and its prediction, can help the water managers to be …

Flood prediction based on weather parameters using deep learning

S Sankaranarayanan, M Prabhakar… - Journal of Water and …, 2020 - iwaponline.com
Today, India is one of the worst flood-affected countries in the world, with the recent disaster
in Kerala in August 2018 being a prime example. A good amount of work has been carried …

Impact of climate change on future flood susceptibility: an evaluation based on deep learning algorithms and GCM model

R Chakrabortty, SC Pal, S Janizadeh… - Water Resources …, 2021 - Springer
Floods are common and recurring natural hazards which damages is the destruction for
society. Several regions of the world with different climatic conditions face the challenge of …

Deep learning based drought assessment and prediction framework

A Kaur, SK Sood - Ecological Informatics, 2020 - Elsevier
Natural calamities like drought cause misery to human lives as well as environment in a
variety of ways. The huge adverse consequences and globally predicted climate change …