AClimate change causes change in temperature and rainfall and consequently affects river discharge. Changes in rainfall can be simulated by global circulation models under different climatic scenarios but investigations of changes in river discharge require rainfall-run More
AClimate change causes change in temperature and rainfall and consequently affects river discharge. Changes in rainfall can be simulated by global circulation models under different climatic scenarios but investigations of changes in river discharge require rainfall-runoff models. The Galikesh basin as one of most flood prone basins in Gloestan Province is considered for determination of changes in river discharge under climate change effect. Temperature and rainfall is produced for future climatic period (2011-2030) based on global circulation model HADCM3, using LARS-WG data generator model. The produced data under different climatic scenario are used as inputs of calibrated IHACRES model to simulate river discharge for future climatic periods. The climate change analysis shows under different scenarios in the study area air temperature increase in different months, which a rise in warm months is more than other months of the year, but the annual precipitation decreases. The Mann-Kendall test is used to detect monotonic trend of seasonal and semiannual river discharge series. The results indicate no trend for spring and first semiannual and negative trend for other seasons and second semiannual series in 5 percent significance level.
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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 c More
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 in the simulation of monthly flow by meteorological parameters.
Method: In this study, meteorological data and monthly time series of discharge of Zarrinrood River (Safavankeh Station) in East Azarbaijan from 1336 to 2015 were used. The meteorological parameters and the month of the year were considered as inputs in the entropy method to determine the effective composition.
Results: Shannon entropy results showed that the rainfall parameters, month of year and temperature provide better results for modeling. The simulations were performed using intelligent hybrid models of particle swarm hybrid algorithm and hybrid simulation hybrid algorithm.
Discussion and Conclusion: The results showed that among these models with the same input structure, the hybrid algorithm simulation based on the support vector machine had better performance for simulating the flow rate compared to other intelligent hybrid models. The results also show that the entropy method is good for selecting the best input combination in smart models.
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Abstract- Water discharge is a term in the water industry that refers to the amount of water that passes through a certain point per unit of time. Discharge rate is the amount of water that passes through a specific point such as a river,, water channel, dam valve, pipe More
Abstract- Water discharge is a term in the water industry that refers to the amount of water that passes through a certain point per unit of time. Discharge rate is the amount of water that passes through a specific point such as a river,, water channel, dam valve, pipe or any other structure such as a faucet cartridge in a unit of time. In the metric system, water discharge rate is expressed in terms of cubic meters per second, cubic meters per hour, or liters per second. The unit of cubic meters per second is used for large flows such as rivers and large canals, and the unit of liters per second is used for the flow of water in wells and water that enters leaks. Measuring the discharge of the river has many effects on people's lives. Knowing the amount of water entering the areas of a river's catchment area is very important in agriculture, potential risks to human and animal life, industries, etc. Therefore, predicting river discharge can lead to effective management and prevent serious damage in the mentioned areas. According to the mentioned cases, the purpose of the presented paper is to predict the river discharge using deep learning techniques. In order to do this, the discharge of the Zayandeh Rood River at Qala Shahrokh station has been investigated and predicted using two techniques - ANFIS and LSTM. The simulation results show 93% to 94% accuracy in predicting the discharge of the studied river.
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