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Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon's entropy models
This work focuses on comparing results of flood susceptibility modelling in the part of Middle
Ganga Plain, Ganga foreland basin. Following inclusivity rule, 12 major flood explanatory …
Ganga Plain, Ganga foreland basin. Following inclusivity rule, 12 major flood explanatory …
Climate and hydrological models to assess the impact of climate change on hydrological regime: a review
Quantitative knowledge about the impacts of climate change on the hydrological regime is
essential in order to achieve meaningful insights to address various adverse consequences …
essential in order to achieve meaningful insights to address various adverse consequences …
Flood susceptibility modeling based on new hybrid intelligence model: Optimization of XGboost model using GA metaheuristic algorithm
Flood is the most common natural hazard that causing unprecedented loss of life and
property in the world. In recent years, flood damage has increased due to human …
property in the world. In recent years, flood damage has increased due to human …
Comparison of machine learning techniques and spatial distribution of daily reference evapotranspiration in Türkiye
Reference evapotranspiration (ET0) estimates are commonly used in hydrologic planning
for water resources and agricultural applications. Last 2 decades, machine learning (ML) …
for water resources and agricultural applications. Last 2 decades, machine learning (ML) …
Neural network techniques for time series prediction: A review
It is important to predict a time series because many problems that are related to prediction
such as health prediction problem, climate change prediction problem and weather …
such as health prediction problem, climate change prediction problem and weather …
Estimation of tile drainage contribution to streamflow and nutrient loads at the watershed scale based on continuously monitored data
Nitrogen losses from artificially drained watersheds degrade water quality at local and
regional scales. In this study, we used an end-member mixing analysis (EMMA) together …
regional scales. In this study, we used an end-member mixing analysis (EMMA) together …
Comparison of the performance of SWAT, IHACRES and artificial neural networks models in rainfall-runoff simulation (case study: Kan watershed, Iran)
M Ahmadi, A Moeini, H Ahmadi… - … of the Earth, Parts A/B/C, 2019 - Elsevier
The catchment area is essentially a heterogeneous dynamic, time and space hydrological
system, and so the process of rainfall-runoff transmission in the catchment area is a very …
system, and so the process of rainfall-runoff transmission in the catchment area is a very …
Evaluation of TRMM 3B42V7 and CHIRPS satellite precipitation products as an input for hydrological model over Eastern Nile Basin
H Abdelmoneim, MR Soliman, HM Moghazy - Earth Systems and …, 2020 - Springer
Nowadays, precipitation estimations based on satellite products (PESPs) have become an
alternative source of sparse rainfall gauge data for various hydrologic applications …
alternative source of sparse rainfall gauge data for various hydrologic applications …
Temporal and spatial comparison of agricultural drought indices from moderate resolution satellite soil moisture data over Northwest Spain
During the last decade, a variety of agricultural drought indices have been developed using
soil moisture (SM), or any of its surrogates, as the primary drought indicator. In this study, a …
soil moisture (SM), or any of its surrogates, as the primary drought indicator. In this study, a …
Application of statistical and geostatistical approaches in temporal and spatial estimations of rainfall
H Karami, H Ghazvinian… - Journal of Water and …, 2023 - iwaponline.com
This study is the first study that worked on the temporal and spatial distributions of annual
rainfall (P year) and maximum 24-h rainfall (P max24h) in the Semnan province. For this …
rainfall (P year) and maximum 24-h rainfall (P max24h) in the Semnan province. For this …