Volume 78 (2025)
Volume 77 (2024)
Volume 76 (2023)
Volume 75 (2022)
Volume 74 (2021)
Volume 73 (2020)
Volume 72 (2019)
Volume 71 (2018)
Volume 70 (2017)
Volume 69 (2016)
Volume 68 (2015)
Volume 67 (2014)
Volume 66 (2013)
Volume 65 (2012)
Volume 63 (2010)
Volume 62 (2009)
Predicting and detecting the trend of temporal and spatial changes of land use using land change modeler

Ali Azareh; Elham Rafiei Sardooi

Volume 74, Issue 3 , December 2021, , Pages 483-500

https://doi.org/10.22059/jrwm.2021.327646.1601

Abstract
  The purpose of this study is to investigate land use changes in the past and predict future land use using land change modeler in Halil River watershed. The detection of land use changes was performed using Landsat satellite images (L5-TM-1991, L7- ETM+-2003 and L8-OLI-2020). Transition potential modeling ...  Read More

Reconstruction of Daily Discharge using Artificial Neural Network and Neuro-Fuzzy Methods (Case Study: Upstream of Karoun Watershed)

Mojtaba Nassaji zavareh; Bagher Ghermezcheshmeh; Fatemeh Rahimzadeh

Volume 69, Issue 2 , July 2016, , Pages 503-514

https://doi.org/10.22059/jrwm.2016.61699

Abstract
  Daily constant discharges are needed estimating daily discharge in the hydrological model. The different number of statistical years, statistical deficiencies, and measurement error leads to the formation of time series with an uncommon time base. Hence the reconstruction of daily discharge data is of ...  Read More

Comparison of Neuro Fuzzy, Neural Network Artificial and Statistical Methods for Estimating Suspended Load Rivers (Case Study: Taleghan Basin Upstream)

Amin Zoratipour

Volume 69, Issue 1 , June 2016, , Pages 65-78

https://doi.org/10.22059/jrwm.2016.61734

Abstract
  Abstract Estimation of fine suspended load rivers is important in designing reserves, transition volume ofsediment, and estimating lake pollution. Thus, some methods are needed for determining damagescaused by sedimentations in environment and determining its effects on the watersheds. There aremany ...  Read More

Flood Forecasting Using Artificial Neural Networks and Nonlinear Multivariate Regression (Case Study: Taleghan Watershed)

Maryam Khosravi; Ali Salajegheh; Mohammad Mahdavi; Mohsen Mohseni Saravi

Volume 65, Issue 3 , December 2012, , Pages 341-349

https://doi.org/10.22059/jrwm.2012.30022

Abstract
  It is necessary to use empirical models for estimating of instantaneous peak discharge because of deficit of gauging stations in the country. Hence, at present study, two models including Artificial Neural Networks and nonlinear multivariate regression were used to predict peak discharge in Taleghan ...  Read More

Investigation on the efficiency of neuro-fuzzy method and statistical models in simulation of rainfall-runoff process

A. Salajegheh; A. Fathabadi; M. Mahdavi

Volume 62, Issue 1 , June 2009

Abstract
  Rainfall-runoff is one of complex hydrological processes that is affected by a variety of physical and hydrological factors. In this study statistical method ARMAX model, neural network, neuro-fuzzy (ANFIS subtractive clustering and grid partition) and two hybrid models of this methods were used to simulate ...  Read More