Adele Alijanpour Shalmani; Alireza Vaezi; Mahmoudreza Tabatabaei
Abstract
Analysis of suspended sediment load data in rivers is the basis for understanding the trend of erosion and sediment in the management and planning of soil and water resources. Due to lack of access to daily suspended sediment loading data with direct measurement, it is important to use methods for modeling ...
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Analysis of suspended sediment load data in rivers is the basis for understanding the trend of erosion and sediment in the management and planning of soil and water resources. Due to lack of access to daily suspended sediment loading data with direct measurement, it is important to use methods for modeling and estimating it in watersheds. One of the best methods used in this field is the use of artificial neural networks. To evaluate daily suspended sediment load, Sira hydrometric station was studied in Karaj River watershed. The number of data used in this study included 624 information records of 31 years (1981–2011) statistical period .Input data to the artificial neural network models included instantaneous flow discharge, average daily flow discharge, average daily flow discharge with a delay of three days, average daily precipitation and average daily precipitation with a delay of three days. Output data to models was daily suspended sediment load. In this research, gamma test and genetic algorithm were used to obtain optimal variables and best combination of variables for entering the model. Then, these combinations with some combination of test and error variables were entered to artificial neural network models. The self-organizing map neural network was used for data clustering and all data were divided into three homogeneous groups: 70 percentage training data, 15 percentage validation data and 15 percentage test data. Then, the combination of variables entered to neural network models with activation functions log sigmoid and tangent sigmoid. The results showed that the neural networks using the optimal variable combinations in comparison with manual combinations have a more accurate estimate for suspended sediment load. In all combinations of inputs to neural network models, a model with tangent sigmoid activation function, with input variables combination including, instantaneous flow discharge (Q), average daily flow discharge (Qi), average daily flow discharge for two day ago (Qi-2), average daily flow discharge for three day ago (Qi-3), average daily precipitation (Pi), average daily precipitation for two day ago (Pi-2) and average daily precipitation for three day ago (Pi-3), was the best model for estimating daily suspended sediment load. This model has the lowest of error (MAE=500.05 (ton/day), RMSE=1995.33(ton/day) and Erel=7%), the highest accuracy (R2=0.96), the highest performance model (NSE=0.96) and has the lowest general standard deviation (GSD=0.97) compared to other models. Also, this model is the best combination with the most influential input variables derived from gamma test and genetic algorithm for estimating SSL.
Marziye Sadat Mirahsani; Abdolrasol Salman Mahini; Reza Moddares; Alireza Soffianian; Reza Jafari; Jahangir Mohhamadi
Abstract
Zayandeh Rood Basin has a vital role in Iran's poetry, biomass, agriculture, industry and tourism, faced with drought problems. Clustering approach can be a management approach to reduce drought risk impacts which groups the members with regard to the division based on the Euclidean distance of stations. ...
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Zayandeh Rood Basin has a vital role in Iran's poetry, biomass, agriculture, industry and tourism, faced with drought problems. Clustering approach can be a management approach to reduce drought risk impacts which groups the members with regard to the division based on the Euclidean distance of stations. In this research, the approach of determining the spatial-temporal distribution of drought clusters in watersheds is used to express variations based on precipitation precipitation index (SPI) parameters of stations, which depends on the probability of precipitation for any time scale. Since the maximum spatial distribution of the meteorological stations in the region and the maximum time period of the long-term and possible long-term statistical period were considered the same, the data of 26 stations from 12 years (2003 to 2014) was used as reference data. In this regard, the12 months SPI index was first calculated. Then, the 12-month SPI index, which ended in December, was used for cluster analysis of the SPI, and then 144 data were clustered into four groups. Further, zoning analysis was performed on data clusters. Then, the relationship between elevations as an effective landform factor in drought with SPI drought index cluster was investigated using correlation of variables. SPI correlation with mean height of each cluster stations was studied and the results were compared and analyzed. The results of the SPI drought fluctuation chart showed a very severe drought in 2008 and 2009 and 2010, and severe drought in 2010 and mild drought in 2003, 2005 and 2013. Also, 12-month SPI drought data showed a high and negative correlation with height data. Consequently, spatial-temporal monitoring of drought indicators clusters is recommended as a way to manage the impacts of drought risk.
Mehdi Sepehri; Seyyed Abbas Atapourfard; Alireza Ildoromi; Hamid Nori; Saba Goodarz; Mohammadmehdi Artimani; Morteza Solgi
Abstract
Peak flow estimation is one of the major issues in water resources and flood management that have basic role in the design of hydraulic structures and biomechanics activities in basins. So that a proper assessment has a basic role in the success of administrative works. In this paper, using artificial ...
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Peak flow estimation is one of the major issues in water resources and flood management that have basic role in the design of hydraulic structures and biomechanics activities in basins. So that a proper assessment has a basic role in the success of administrative works. In this paper, using artificial intelligence methods (MLP Neural Network, the mixture of SOFM with MLP, the mixture of FCM with ANFIS) to estimate Yalfan River’s peak discharge in hydrometer local station. For these models, eight variables have been considered as the inputs that includes rainfall amount in the occurrence time of flood, rainfall of five days ago from occurrence of flood, curve number of the basin (CN), basic discharge and finally peak discharge are considered as the output. In the artificial intelligences after preprocessing of the data, the optimal structure of the models are determined with input and output data, evaluation criteria and trial and error. At the end, the MLP model had better performance compared to ANFIS+FCM, MLP+SOFM, GRNN models.
Rahim Kazemi; Jahangir Porhemmat
Abstract
Estimating the runoff coefficient that is influenced by morphometric, geologic and hydro-climatologically factors are the most important issues in hydrology and information of its role in the planning and management of water resources is more important. Clustering catchments is the best method for the ...
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Estimating the runoff coefficient that is influenced by morphometric, geologic and hydro-climatologically factors are the most important issues in hydrology and information of its role in the planning and management of water resources is more important. Clustering catchments is the best method for the analysis of hydrological parameters in the absence of full coverage of hydrological data. In this research, twenty two hydrometric stations with common period from 1974 to 1999 were selected. Physiographic parameters of the catchments were extracted. Runoff coefficient was calculated and then base flow was extracted from using one parameter recursive digital filters. Lithological units using digital geological map, with the scale of 1: 250,000, based on expert opinion divided on two classes and area covered by each unit in each catchment were calculated. Factor analysis using 15 parameters were conducted. Catchments using independent factors in different hierarchy methods includes: nearest neighbor, furthest neighbor, median clustering, centroid clustering and Ward method were classified. Then, the regional equations using linear regression at 1% significant level were determined. To compare and evaluate the accuracy and efficiency of the models, independence errors, colinerity and normal distribution of error were tested. The results of factor analysis showed that all variables are to be classified in terms of five factors which 85.9% of the variance was included. Results of homogeneity showed that the basins in homogeneous methods of nearest neighbor, furthest neighbor, centroid clustering and median clustering, were all the same and classified in two groups with the similar components. The results of accuracy assessment showed that the accuracy of nearest neighbor methods was more accurate, and because of low relative error (25.4%) and MAE of 7.85 and RMSE of 9.62 was diagnosed as the best method for regional analyzing of runoff coefficient in the study area.
Zahra Nik; Kouros Yazdjerdi; Hadi Abdolazimi
Abstract
Morphometric analysis is considered as quantitative evaluation of geometric features landforms and landscape. In the study of basin tectonic features, the use of some morphometric parameters can provide very substantial information. Gavkoshak Basin with an area of 46.73 km2 is located in Zagros simply ...
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Morphometric analysis is considered as quantitative evaluation of geometric features landforms and landscape. In the study of basin tectonic features, the use of some morphometric parameters can provide very substantial information. Gavkoshak Basin with an area of 46.73 km2 is located in Zagros simply folded belt. The objective of this research is to use the morphometric indices such as hypsometric integral, basin shape, stream length-gradient, asymmetric factor, and the valley width/height ratio. As a result, these indices are converted to the tectonic activity index. This index can be used to assess the overall performance of the region's tectonic activity.Morphometric indices of area are studied by dividing this area into 43 sub-basins, using the Digital-Elevation Model (DEM) and Geographic Information System) GIS). Morphotectonics index method with the use of geographic information systems provide procedures and a powerful tool for estimating tectonic activity in the region. It should be borne in mind that the results of these indices can show different values in different sectors. By examining the relative index of tectonic activity, the basin in this research is divided into two parts namely active (25.6% of the watershed) and semi-active (74.4 % of the watershed). Through statistical analysis, the area under investigation includes four clusters: cluster one with 91. 83% similarity, cluster two with 95.19% similarity, cluster three with 96.18% similarity, and cluster four with 91.09% similarity. In this way, homogeneous regions were determined based on clustering algorithm ward. The active tectonic basin in this research can be studied in another research project, using other morphometric parameters.
Mehran Lashanizand; Kianfar Payamani; Shahla Ahmadi; Iraj Veyskarami
Abstract
There is a very close relationship between two distributions of climate and vegetation maps. But, the relation between climate and vegetation is not clear. Therefore, the main objective of this research is to determine the effect of main climatic factors on distribution of main vegetal species and its ...
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There is a very close relationship between two distributions of climate and vegetation maps. But, the relation between climate and vegetation is not clear. Therefore, the main objective of this research is to determine the effect of main climatic factors on distribution of main vegetal species and its zonation in Iran. For this purpose, 51 synoptic stations with 30-year data (1976-2005) or more were selected and their meteorological data were extracted. Using principal components techniques, 269 climatic variables were considered and the numbers of variables reduced to 14 factors with eigenvalues more than one that represent 96.4% of the total variance. The first three factors (the thermal component, moisture and precipitation, respectively) had the most influence on the climate, so, they were considered for climate zonation criteria. These three components were rasterized within ArcGIS using Kriging interpolation method. Resulted raster map was used to create the climatic zonation map by unsupervised clustering technique. Finally, ecological climate zonationmap was derived using two climate parameters (temperature and humidity) and elevation map of country and by using unsupervised clustering technique. This map shows that the boundaries of ecological climate regions are mainly based on climatic (temperature and humidity) and landform factors (elevation). Another result of the research was that most ecological climatic zones were related to the low elevation areas of the country (below 1200 m) and the rate of high ecological climatic areas (above 2400 m) were very low.
