Výsledky vyhledávání

Akademický článek

A robust multi-model framework for groundwater level prediction: The BFSA-MVMD-GRU-RVM model

Autor: Akram Seifi, Sharareh Pourebrahim, Mohammad Ehteram, Hanieh Shabanian

Publikováno v: Results in Engineering, Vol 24, Iss , Pp 103250- (2024)

Groundwater level prediction is critical for environmental protection and agricultural planning. Accurate predictions help manage risks associated with excessive groundwater extraction and land subsidence. This study introduces a novel model combinin

Externí odkaz: https://doaj.org/article/ae459bfa8df24998af68af00a0738b35

Zobrazit plný text záznamu

Akademický článek

Potential assessment of water harvesting from local wastewater treatment plants (Case study: Rotating Biological Contactor, RBC)

Autor: hossien riahi, Akram Seifi

Publikováno v: Water Harvesting Research, Vol 4, Iss 2, Pp 210-216 (2021)

In dry regions the reuse of treated wastewater plays a significant role in management, operation, scheduling and utilization of water resources. In design and operate of the sewage treatment plants , it is essential to measure and forecast the harves

Externí odkaz: https://doaj.org/article/ebad79c517e74ff4a76e174ac587aef7

Zobrazit plný text záznamu

Akademický článek

Uncertainty Assessment of WinSRFR Furrow Irrigation Simulation Model Using the GLUE Framework under Variability in Geometry Cross Section, Infiltration, and Roughness Parameters

Autor: Akram Seifi, Soudabeh Golestani Kermani, Amir Mosavi, Fatemeh Soroush

Publikováno v: Water, Vol 15, Iss 6, p 1250 (2023)

Quantitatively analyzing models’ uncertainty is essential for agricultural models due to the effect of inputs parameters and processes on increasing models’ uncertainties. The main aim of the current study was to explore the influence of input pa

Externí odkaz: https://doaj.org/article/d147676f1d644168953c86b6a48ea01a

Zobrazit plný text záznamu

Akademický článek

Inferring Damage Effects of Subsurface Water Level Local Uplifting on Water and Wastewatwr Systems Using Analytical Hierarchy Process (Casy Study: Kerman City)

Autor: Hosain Riahi-Madvar, Akram Seifi

Publikováno v: علوم و مهندسی آبیاری, Vol 42, Iss 1, Pp 31-45 (2019)

With urban developments and the aging of urban water distribution pipes their demand for repair and maintenance is rapidly grown. There are several factors that affect the performance and leakage in water and wastewater distribution networks (Ameyaw

Externí odkaz: https://doaj.org/article/0a7882af88af4f459788ef3324649f93

Zobrazit plný text záznamu

Akademický článek

Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry

Autor: Hossien Riahi-Madvar, Majid Dehghani, Akram Seifi, Ely Salwana, Shahaboddin Shamshirband, Amir Mosavi, Kwok-wing Chau

Publikováno v: Engineering Applications of Computational Fluid Mechanics, Vol 13, Iss 1, Pp 529-550 (2019)

The main aims and contributions of the present paper are to use new soft computing methods for the simulation of scour geometry (depth/height and locations) in a comparative framework. Five models were used for the prediction of the dimension and loc

Externí odkaz: https://doaj.org/article/57dddc70fe0a4650a3ee6cf5b204bd45

Zobrazit plný text záznamu

Akademický článek

Qualitative Zoning of Shahr-e-Babak Aquifer Based on its Corrosiveness, Sedimentation, and Applicability for Agricultural, Drinking, and Pressure Irrigation Uses

Autor: Akram Seifi, Hossien Riahi-Madvar

Publikováno v: آب و فاضلاب, Vol 28, Iss 5, Pp 92-105 (2017)

Water quality management in groundwater aquifers requires accurate water quality monitoring to ensure they meet a variety of relevant standards. Given the rather few reported studies in the field, the present study was designed and implemented to inv

Externí odkaz: https://doaj.org/article/721abc321168491c9e27a29a620c8182

Zobrazit plný text záznamu

Akademický článek

Accuracy and uncertainty analysis of artificial neural network in predicting saffron yield in the south Khorasan province based on meteorological data

Autor: Hossien Riahi Modavar, Abbas Khashei-Siuki, Akram Seifi

Publikováno v: Zirā̒at va Fanāvarī-i Za̒farān, Vol 5, Iss 3, Pp 255-271 (2017)

Because of saffron yield sensitivity and the effects of climate on its performance, and also due to the nonlinear nature of crop yield functions, the Artificial Neural Network (ANN) model is employed in this study for prediction and uncertainty analy

Externí odkaz: https://doaj.org/article/913d40246dd549e89abcb161246b1f48

Zobrazit plný text záznamu

Uncertainty and spatial analysis in wheat yield prediction based on robust inclusive multiple models

Autor: Fatemeh, Soroush, Mohammad, Ehteram, Akram, Seifi

Publikováno v: Environmental Science and Pollution Research. 30:20887-20906

Reliable prediction of wheat yield ahead of harvest is a critical challenge for decision-makers along the supply chain. Predicting wheat yield is a real challenge for better agriculture and food security management. Modeling wheat yield is complex an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::15d7e7b549b9d0006f7d19affb82aa30
https://doi.org/10.1007/s11356-022-23653-x

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání