Forecasting the potential hydrological response to future climate change is an effective way of a... more Forecasting the potential hydrological response to future climate change is an effective way of assessing the adverse effects of future climate change on water resources. Data-driven models based on machine learning algorithms have great application prospects for hydrological response forecasting as they require less developmental time, minimal input, and are relatively simple compared to dynamic or physical models, especially for data scarce regions. In this study, we employed an ensemble of eight General Circulation Models (GCMs) and two artificial intelligence-based methods (Support Vector Regression, SVR, and Extreme Learning Machine, ELM) to establish the historical streamflow response to climate change and to forecast the future response under Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5 in a mountainous watershed in northwest China. We found that the artificial-intelligence-based SVR and ELM methods showed very good performances in the projection of future...
The available studies for estimating the characteristics of hydraulic jump are only for artificia... more The available studies for estimating the characteristics of hydraulic jump are only for artificial or natural beds, and very limited researches have simultaneously considered artificial and natural beds. The aim of this study is to present comprehensive equations and models for predicting the characteristics of hydraulic jump in artificial and natural rough beds with various dimensions, arrangement and roughness forms. The experimental data of different researches on two artificial and natural rough beds (containing 559 data series) were collected. After randomization, the data were used in combination of 75-25 for training and testing the two intelligent models of K-nearest neighbors (KNN) and M5 model tree with various scenarios and their performance were evaluated in estimation of hydraulic jump characteristics (including sequent depth, energy loss and shear force coefficient). Then, the existing empirical equations examined and calibrated and new optimized equations were derived...
Climatic conditions have a major influence in attracting tourists to a city in different months. ... more Climatic conditions have a major influence in attracting tourists to a city in different months. In this study, the potential of Isfahan and Rasht as arid and humid cities, respectively, was investigated in terms of attracting tourists during a year. For this purpose, the Holiday Climate Index (HCI), which has been designed based on daily climate information, was used. The results showed that in Isfahan, with rising air temperature and reducing air humidity in March, April and May, the mean value of HCI is more than 69 and climatic condition is "very good". Also, from September 14, the value of HCI reaches above 69 and shows "very good" condition and this condition continues until the end of October. Therefore, these two periods are the best times for presence of tourists in Isfahan. In Rasht, in April and May, because of climate variables suitability (sunshine hours, cloudiness, and weather temperature) in comparison to other months, the mean value of HCI is equ...
Forecasting the potential hydrological response to future climate change is an effective way of a... more Forecasting the potential hydrological response to future climate change is an effective way of assessing the adverse effects of future climate change on water resources. Data-driven models based on machine learning algorithms have great application prospects for hydrological response forecasting as they require less developmental time, minimal input, and are relatively simple compared to dynamic or physical models, especially for data scarce regions. In this study, we employed an ensemble of eight General Circulation Models (GCMs) and two artificial intelligence-based methods (Support Vector Regression, SVR, and Extreme Learning Machine, ELM) to establish the historical streamflow response to climate change and to forecast the future response under Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5 in a mountainous watershed in northwest China. We found that the artificial-intelligence-based SVR and ELM methods showed very good performances in the projection of future...
The available studies for estimating the characteristics of hydraulic jump are only for artificia... more The available studies for estimating the characteristics of hydraulic jump are only for artificial or natural beds, and very limited researches have simultaneously considered artificial and natural beds. The aim of this study is to present comprehensive equations and models for predicting the characteristics of hydraulic jump in artificial and natural rough beds with various dimensions, arrangement and roughness forms. The experimental data of different researches on two artificial and natural rough beds (containing 559 data series) were collected. After randomization, the data were used in combination of 75-25 for training and testing the two intelligent models of K-nearest neighbors (KNN) and M5 model tree with various scenarios and their performance were evaluated in estimation of hydraulic jump characteristics (including sequent depth, energy loss and shear force coefficient). Then, the existing empirical equations examined and calibrated and new optimized equations were derived...
Climatic conditions have a major influence in attracting tourists to a city in different months. ... more Climatic conditions have a major influence in attracting tourists to a city in different months. In this study, the potential of Isfahan and Rasht as arid and humid cities, respectively, was investigated in terms of attracting tourists during a year. For this purpose, the Holiday Climate Index (HCI), which has been designed based on daily climate information, was used. The results showed that in Isfahan, with rising air temperature and reducing air humidity in March, April and May, the mean value of HCI is more than 69 and climatic condition is "very good". Also, from September 14, the value of HCI reaches above 69 and shows "very good" condition and this condition continues until the end of October. Therefore, these two periods are the best times for presence of tourists in Isfahan. In Rasht, in April and May, because of climate variables suitability (sunshine hours, cloudiness, and weather temperature) in comparison to other months, the mean value of HCI is equ...
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