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Developing a New Class-based Probabilistic Hybrid Model for Monthly Precipitation Forecasting

کلیدواژه: Monthly Precipitation Forecasting,Hybrid Model,Kernel Function,Classification,Karkheh,CPHM

نویسندگان: MODARESI F.

ناشر: تحقیقات منابع آب ایران - Iran-Water Resources Research (IWRR)

High accuracy forecasting of Monthly Precipitation is one of the major challenges in hydrology and meteorology and is of great importance in water resources planning. In the current research a Class-Based Probabilistic Hybrid Model (CPHM) has been developed on the basis of a hybrid of classification... ادامه

سال:2021

نوع سند: Article

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16 بازدید

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Development of Wavelet-Kstar Algorithm Hybrid Model for the Monthly Precipitation Prediction (Case Study: Synoptic Station of Ahvaz)

کلیدواژه: Decomposition Level,Mother Wavelet,Willmott Index,Violin Plot

نویسندگان: AHMADI FARSHAD, MADDAH MOHAMMAD AMIN

ناشر: تحقیقات آب و خاک ایران - Iranian Journal of Soil and Water Research

Predicting hydrological parameters, especially rainfall, has played a very important role in water resources management and planning. Therefore, the development of methods giving accurate estimates has always been of interest to researchers. In this study, Precipitation data from the Ahvaz synoptic ... ادامه

سال:2021

نوع سند: Article

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9 بازدید

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Prediction of Stream Flow Using Intelligent Hybrid Models in Monthly Scale (Case study: Zarrin roud River)

کلیدواژه: Hybrid Algorithms,Particle Swarm,Entropy,Discharge,Simulated Annealing

نویسندگان: Mohammadi Babak, MOAZENZADEH ROOZBEH

ناشر: علوم و تکنولوژی محیط زیست - Journal of Environmental Science and Technology

Background and Objective: Selecting appropriate inputs for intelligent models are important because it reduces the cost and saves time and increases accuracy and efficiency of its models. The aim of the present study is the use of Shannon entropy to select the optimum combination of input variables ... ادامه

سال:2019

نوع سند: Article

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15 بازدید

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Modeling Of Monthly Evaporation Using Single and Hybrid-Wavelet Data-Driven Methods in Basins of Iran with Climate Variety

کلیدواژه: Climate, Evaporation, Data mining, Wavelet

نویسندگان: Emadi Ali Reza, Zamanzad-Ghavidel Sarvin, Zareie Soheila, Rashid-Niaghi Ali

ناشر: مهندسی آبیاری و آب ایران - Journal of Irrigation and Water Engineering

Evaporation as one of the natural parameters has always been considered by researchers. In this study, the Monthly evaporation variable was modeled in two different climates of Iran using artificial neural network, adaptive fuzzy-neural inference system and gene expression programming methods and co... ادامه

سال:2022

نوع سند: Article

4 دانلود

15 بازدید

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Modeling Of Monthly Evaporation Using Single and Hybrid-Wavelet Data-Driven Methods in Basins of Iran with Climate Variety

کلیدواژه: Climate, Evaporation, Data mining, Wavelet

نویسندگان: Emadi Ali Reza, Zamanzad Ghavidel Sarvin, Fazeli Sina, Zareie Soheila, Rashid Niaghi Ali

ناشر: مهندسی آبیاری و آب ایران - Journal of Irrigation and Water Engineering

سال:2022

نوع سند: Article

4 دانلود

11 بازدید

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Prediction of Monthly River Flow Using Hybridization of Linear Time Series Models and Bayesian network (Case Study: Bakhtiari River)

کلیدواژه: Bayesian network,Input pattern,Model development,Stationary test

نویسندگان: AHMADI FARSHAD, VALINIA MIR MAHMOOD

ناشر: مدیریت آب و آبیاری - Journal of Water and Irrigation Management

One of the most important issues in water resources management is the preparation and development of appropriate models in order to predict the streamflow more accurately. For this purpose, in the present study, linear time series models (ARMA), intelligent Bayesian network (BN) and BN-ARMA hybrid m... ادامه

سال:2020

نوع سند: Article

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6 بازدید

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Evaluation of Hybrid Model ANN-PSO and Different Data Mining Methods in Estimating Monthly Evapotranspiration in Two Different Climates

کلیدواژه: Dry Regions,Evaporation and Transpiration,Machine Learning,Soft Computing

نویسندگان: Hosseini Vardanjani S.M.R., KHOSHRAVESH M., Hosseini Kakolaki S.E., Pourgholam Amiji M.

ناشر: آبیاری و زهکشی ایران - Iranian Journal of Irrigation and Drainage

Evapotranspiration is one of the main components of any region's water balance. Its accurate estimation is very necessary for hydrological studies, designing irrigation and drainage systems, and planning irrigation systems. In this research, the M5 tree model, M5 Rules, K Star, Rep Tree, artificial ... ادامه

سال:2023

نوع سند: Article

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15 بازدید

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Introducing a Nonlinear Model Based on Hybrid Machine Learning for Modeling and Prediction of Precipitation and Comparison with SDSM Method (Cases Studies: Shahrekord, Barez, and Yasuj)

کلیدواژه: Climate change,Downscaling,Machine learning,Precipitation

نویسندگان: Valikhan Anaraki Mahdi, Mousavi Sayed Farhad, FARZIN SAEED, KARAMI HOJAT

ناشر: تحقیقات آب و خاک ایران - Iranian Journal of Soil and Water Research

In the present study, a nonlinear hybrid model, based on multivariate adaptive regression splines (MARS), artificial neural networks (ANN) and K-nearest neighbor (KNN) has been presented for downscaling the Precipitation of Shahrekord, Barez, and Yasuj under climate change conditions. This model, si... ادامه

سال:2020

نوع سند: Article

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14 بازدید

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