Search

Rivers are important water resources for human life, but sometimes cause irreparable damages. The flood plains are fertile terrains which are endangered by flood. Flood hazard mapping is one of the basic methods in flood fighting. In order to decline flood damages, the simulation of the hydraulic behavior of the rivers during flood occurrence is very important. In this study, areas that are flooded were zoned along Siahrood and Goharrood rivers (Rasht City, northern Iran). The rivers bed and banks terrains of Goharrood and Siahrood were simulated using HEC-GeoRAS extension and digital map (scale: 1000). Pick discharges with different return periods were estimated using stochastic analysis. HEC-RAS software and geographical information system were applied for simulating the hydraulic behavior of the rivers and providing flood zoning map. The GIS was an efficient tool for data-processing and mapping stages. Finally, the flood zones associated with 2, 10, 25, 50, 100 and 200 years return periods were mapped and necessary analysis were conducted during the present research. The results showed that some parts of Rasht City (river bank terrains) are endangered flood hazard.

Artificial neural networks (ANNs) have become one of the most promising tools for rainfall simulation since a few years ago. However, most of the researchers have focused on rainfall intensity records as well as on watersheds, which generally are utilized as input records of other hydro-meteorological variables. The present study was conducted in Kechik station, Golestan Province (northern Iran). The normal multi-layer perceptron form of ANN (MLP–ANN) was selected as the baseline ANN model. The efficiency of GDX, CG and L–M training algorithms were compared to improve computed performances. The inputs of ANN included temperature, evaporation, air pressure, humidity and wind velocity in a 10 minute increment The results revealed that the L–M algorithm was more efficient than the CG and GDX algorithm, so it was used for training six ANN models for rainfall intensity forecasting. The results showed that all of the parameters were proper inputs for simulating rainfall, but temperature, evaporation and moisture were the most important factors in rainfall occurrence.

The process of water quality testing is money/time-consuming, quite important and difficult stage for routine measurements. Therefore, use of models has become commonplace in simulating water quality. In this study, the coactive neuro-fuzzy inference system (CANFIS) was used to simulate groundwater quality. Further, geographic information system (GIS) was used as the pre-processor and post-processor tool to demonstrate spatial variation of groundwater quality. All important factors were quantified and groundwater quality index (GWQI) was developed. The proposed model was trained and validated by taking a case study of Mazandaran Plain located in northern part of Iran. The factors affecting groundwater quality were the input variables for the simulation, whereas GWQI index was the output. The developed model was validated to simulate groundwater quality. Network validation was performed via comparison between the estimated and actual GWQI values. In GIS, the study area was separated to raster format in the pixel dimensions of 1 km and also by incorporation of input data layers of the Fuzzy Network-CANFIS model; the geo-referenced layers of the effective factors in groundwater quality were earned. Therefore, numeric values of each pixel with geographical coordinates were entered to the Fuzzy Network-CANFIS model and thus simulation of groundwater quality was accessed in the study area. Finally, the simulated GWQI indices using the Fuzzy Network-CANFIS model were entered into GIS, and hence groundwater quality map (raster layer) based on the results of the network simulation was earned. The study's results confirm the high efficiency of incorporation of neuro-fuzzy techniques and GIS. It is also worth noting that the general quality of the groundwater in the most studied plain is fairly low. [ABSTRACT FROM AUTHOR]

While there are different methods and models that can be applied to estimate the qualitative and quantitative parameters of water resources, unfortunately, no comprehensive qualitative and quantitative data exist about water resources in Iran. The present study is to compare the performance of the artificial neural network (ANN) and the multivariate regression methods in simulating spring discharge in the Caspian Southern Watersheds. Multivariate regression method was used by using SPSS software. Springs average discharge was considered as the dependent variable and other affecting factors as independent variables. Two linear models were presented for estimating the alluvial and karst springs discharge. Then, the models' performance was evaluated and confirmed. Also, the artificial neural network was applied to simulate the alluvial and karst springs discharge. ANN performance was evaluated through two parameters: median root of square of the error and Pearson's R-squared statistics. The results showed that the most important factors of karst springs discharge were the porosity of aquifer formation and the site elevation; in case of the alluvial springs, the transmissivity of aquifer formation and the aquifer depth were the most important factors. Moreover, ANN efficiency in estimating springs discharge was higher than that of the multivariate regression method. [ABSTRACT FROM AUTHOR]