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Showing 6 results for Artificial Neural Network
Babak Mohammadi, Samad Emamgholizadeh,
Volume 4, Issue 4 (3-2017)
Abstract
Atmospheric precipitation management and efficient use of these resources are extremely helpful to the management of water resources and it has the basic role in the management of the water resources of hydrological parameter estimate as well. In this research, rainfall estimate of three synoptic station situated in Astara, Lahijan and Jirandeh in Gilan province has been done using artificial neural network (ANN) and support vector machines (SVM). The principal component analysis method (PCA) was applied to determine the data pre-processing and input data. According to the results, the main component analysis method for synoptic stations as well as for the five main components of Astara and jirandeh, and for the four main components of Lahijan station is selected. The results of model-making indicate that the artificial neural network model based on principal component analysis (PCA-ANN) in Astara and Jirandeh stations in order with average 2.74 mm squares of 2.62and mm backup-based support vector machine model analysis of the main components (PCA-SVM) mean squares of squares in Lahijan station with error 2.53 mm can be selected as selected models for the aforementioned stations. Finally, with respect to the results can be fitted such that the methods used for data pre-processing in this research to predicate the rainfall as well as the SVM in Lahijan station and model Ann model in Astara and Jirandeh stations have been acceptable.
Babak Mohammadi, Ruzbe Moazenzadeh,
Volume 5, Issue 1 (6-2017)
Abstract
Abstract
In this research, we tried to determine the input composition and model for estimation of precipitation in Shahrood. To achieve this objective, monthly weather data including evaporation, temperature, relative humidity, solar radiation, wind speed during the period of 1963 to 1915, and artificial neural network and support vector machines have been used. 75% of the data was used for calibration and 25% for validation of the models. In this research, an artificial neural network of laminated perceptron with a sigmoid tangent function and 1 to 30 neurons in the hidden layer was used and a support vector machine model with radial base kernel function was used to estimate rainfall in Shahrood district. The performance of each model was evaluated using the statistical mean square error and correlation coefficient. The uncertainty of the models was also determined for two parameters, d-factor and p-factor. Considering that both models have good performance in rainfall estimation, the support vector machine model with less error and uncertainty than artificial neural network model has better performance in predicting rainfall in Shahrood. Therefore, a support vector machine model can be used as a very suitable model for precipitation estimation.
Babak Mohammadi, Seyed Mustafa Biazar, Esmaeil Asadi,
Volume 5, Issue 2 (9-2017)
Abstract
Groundwater and water resource management play key roles in water resource sustainability in arid and semi-arid areas. Forecasting groundwater level is very important for water resource management and planning. In this study, an artificial neural network and a particle swarm algorithm based on artificial neural network models have been used to estimate groundwater level in the Ardebil plain. Water table level data for the 1972 -2011 period was used as our data in this study. Model inputs were water table level of various months. Results of both models were evaluated by root-mean-square error, the correlation coefficient and Nash-Sutcliffe coefficient. Results showed the performance of the particle swarm algorithm based on artificial neural network models to be superior. Root-mean-square error results for the particle swarm algorithm model in spring, summer, autumn and winter were 0.476, 0.507, 0.309, and 0.386 respectively. These results show that the hybrid structure of the network in training leads to increased accuracy. Thus, the particle swarm algorithm based on artificial neural network models can be used to estimate groundwater level in the Ardebil plain with acceptable accuracy.
Arash Jael,
Volume 9, Issue 3 (12-2021)
Abstract
Dams control most of the sediment entering the reservoir by creating static environments. However, sediment leaving the dam depends on various factors such as dam management method, inlet sediment, water height in the reservoir, the shape of the reservoir, and discharge flow. In this research, the amount of suspended sediment of Doroodzan Dam based on a statistical period of 25 years has been investigated using three learning methods based on the data-driven algorithm, namely the K nearest neighbors, regression, and neural network. The results show that among different structures of the K nearest neighbors, the selection of 6 neighborhoods has more precise outcomes than other structures. Also, among different structures of neural networks, a structure with two hidden layers and 4 and 7 nodes in each hidden layer respectively, predicted suspended sediment more accurately than other neural network structures. Comparison of different algorisms was indicated that neural networks have more accurate results than other mentioned methods.
Behnoush Farokhzadeh, , ,
Volume 11, Issue 2 (8-2023)
Abstract
Land use change is an important global and local ecological trend and is one of the major challenges in the 21st century. The purpose of this study was to model land use changes in the Gahvareh region, Kermanshah province using the LCM model. This model has the ability to simulate several land use changes by utilizing and integrating Markov chain models, multi-layer perceptron neural network, logistic regression, and MLOP. In this research, Landsat 4, Landsat 5, and Landsat 8 satellite images were used to prepare land cover maps. After initial corrections, the images were classified using the Maximum likelihood method, and land use maps for the years 1986, 2000, and 2018 were prepared. After monitoring the changes of different land uses during two periods (1986-2000) and (2000-2018) and assessing the validation of the model, finally, the land use map of 2028 was predicted using the LCM model. The results showed that during the first and second calibration periods, 30% and 42% of forest land were reduced, 26 and 37 ha were added to agricultural land and 80% and 32% were added to residential land respectively. The most changes during the study period were the conversion of forest lands to pasture and thin forest (80%). Forecasts for the year 2028 showed that the dense forest would be destroyed and would be decreased in half in comparison to 2018. Evaluation of the accuracy of transmission potential modeling using artificial neural networks showed high accuracy in most of the scenarios.
Mr. Mohammad Jahani, Dr. Mohammad Taghi Dastorani, Dr. Alireza Rashki,
Volume 12, Issue 4 (12-2024)
Abstract
Principal component analysis, Sarzab watershed, Bayesian linear regression, artificial neural network Flooding is one of the unfortunate events in nature, which, if not predicted in time, can cause severe financial and life damages. Therefore, estimating flood peak discharge is a crucial issue in hydrological studies today. However, research on the use of remote sensing tools for predicting, modeling, and managing floods in most of the country's watersheds has received less attention. This research aims to determine the factors affecting the flood flow discharge of the Sarbaz watershed and also evaluate the role of artificial intelligence methods, including the artificial neural network (ANN) model, to predict the flood flow of this watershed. In this research, rainfall data, soil moisture and temperature, evapotranspiration, base water flow, and Enhanced Vegetation Index (EVI) from the Google Earth Engine system, as well as observational data of flood event discharges of the studied area from 1380-1401, were used. Principal component analysis was then used to determine the factors affecting flood discharge. These factors were modeled using Bayesian linear regression to implement the artificial neural network models. Finally, artificial neural network modeling was performed for flood flow analysis. The results showed that the total rainfall of the current day and the previous day, soil moisture at a depth of 0 to 10 cm of the previous day, and soil temperature of the previous day were selected as the most appropriate input patterns for modeling. The artificial neural network designed had an efficiency factor of 0.90, a determination coefficient (R2) of 0.89, and a root mean square error (RMSE) of 50.37 for the training stage. For the validation stage, it had an efficiency factor of 0.76, an R2 of 0.83, and an RMSE of 46.86, demonstrating a good ability to estimate peak flood discharge. The results indicated that the calibrated model for predicting flood flow using remote sensing data is practical and has acceptable accuracy. Therefore, it can be an efficient tool to help managers predict floods on time and reduce the resulting damages.
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