Nasrin Fathollahzadeh AttarUniversity of Padova | UNIPD · Department of Statistical Sciences
Nasrin Fathollahzadeh Attar
Founder of R-ladiesUrmia. Interested in extremes and stochastic models in environmental sciences.
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Citations since 2017
13 Research Items
I am interested in Hydrological Modelling and Prediction. I am the coordinator of online education in data science using R software. Furthermore, I was awarded as a visiting scholar by the Iranian Ministry of Foreign Affairs to the University of Adelaide, Australia. The quality of publication is my first priority. For your questions about any of my papers or projects please Contact: https://linktr.ee/nasrinattar Email: firstname.lastname@example.org
Accurate prediction of reference evapotranspiration (ET0) is very valuable since it directly affects the amount of agricultural water needed, the allocation of water consumption and the management of irrigation systems. This research consists of two parts; in the first part, ET0 was predicted by two machine learning models (artificial neural networ...
An alternative energy source such as solar is one of the most important renewable resources. A reliable solar radiation prediction is essential for various applications in agriculture, industry, transport, and the environment because they reduce greenhouse gases and are environmentally friendly. Solar radiation data series have embedded fluctuation...
The behavior of hydrological processes is periodic and stochastic due to the influence of climatic factors. Therefore, it is crucial to develop the models based on their periodicity and stochas-tic nature for prediction. Furthermore, forecasting the streamflow, as one of the main components of the hydrological cycle, is a primary subject. In this s...
Snow cover area on a river basin, affects so many meteorologic and environmental parameters. By growing remote sensing technology, nowadays snow cover area could be measured on a regular basis for scientific purposes. In this study, the monthly average of snow cover area of the Baranduz river basin from West Azerbaijan in Iran had been used for mod...
A gradient boosting regression tree (GBT) approach is introduced for one- and three-month ahead standardized precipitation-evapotranspiration index (SPEI) classification for Antalya and Ankara in Turkey. First, the numerical target series of SPEI-6 was converted into the categorical vectors of extreme wet, wet, near normal, dry, and extremely dry l...
Implementing a reliable computational model for predicting the reference evapotranspiration (ET 0 ) process is essential for several agricultural and hydrological applications, especially for the rural water resource systems, water use allocations, utilization and demand assessments, and the management of irrigation systems. In this research, two a...
Accurate streamflow prediction is essential in reservoir management, flood control, and operation of irrigation networks. In this study, the deterministic and stochastic components of modeling are considered simultaneously. Two nonlinear time series models are developed based on autoregressive conditional heteroscedasticity and self-exciting thresh...
Water quality has a crucial impact on human health; therefore, water quality index modeling is one of the challenging issues in the water sector. The accurate prediction of water quality index is an essential requisite for water quality management, human health, public consumption, and domestic uses. A comprehensive review as an initial attempt is...
this is my doctorate thesis proposal. i am trying to analyze the trend of the snow cover area in Baranduz river basin. also model the snow cover area by artificial intelligence models.
Hydrological modeling is one of the important subjects in managing water resources and the processes of predicting stochastic behavior. Developing Data-Driven Models (DDMs) to apply to hydrological modeling is a very complex issue because of the stochastic nature of the observed data, like seasonality, periodicities, anomalies, and lack of data. As...
https://figshare.com/articles/neural-network-add-in-1-5-4-setup_exe/7460756 And a good news that the new version of this add-in is coming in winter.
Owing to the importance of dew point temperature (Tdew) as a determining factor in hydrological parameters, especially water vapor and evaporation, we aim for the estimation of Tdew by three different computational models including gene expression programming (GEP), multivariate adaptive regression splines (MARS), and support vector machine (SVM) m...
I have a question about dealing with wind speed and wind direction data. How to find the distribution and frequency of wind direction data which are in angles. I have tried wind rose, but I wonder if there are other ways.
I want to model river flow by self threshold autoregressive model, but I don't know how should I consider these three (p,r,d)?
please help me.
I have 30 years,daily river flow data.
I want to model these time series with data driven methods like GEP.
My question is how to select my lags with discharge data
Is there any method for this purpose?
Working with these wonderful people (Vahid Naghshin, Ph. D. Marya Alizadeh Homayoon Khadivi) was my best experience as teamwork. We are fortune teller team and we participated in Global AI Innovation Challenge Series 2021 Intelligent Weather Forecast for Better Life. We are forecasting the Drought Level by using the open data offered by the NASA POWER Project and the authors of the US Drought Monitor. We have already used Alibaba Cloud services for implementing our AI Architecture on DSW Development Environment. We have developed an AI Architecture which forecasts the Drought Level with high-level accuracy. Our system is capable of Monitoring the environment, specifying the drought areas, forecasting the drought level, and alarming the authorities for the extreme events. More information Can be found at: https://lnkd.in/eCpZnRbR
This project is about optimizing cropping pattern of selected crops in Urmia lake basin with considering climate change effects and virtual water.
Developing of time series modeling (linear and nonlinear) and also new artificial intelligence techniques in Water Resources Engineering.