13 research outputs found

    Estimation of peak outflow in dam failure using neural network approach under uncertainty analysis

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    This paper presents two Artificial Neural Network (ANN) based models for the prediction of peak outflow from breached embankment dams using two effective parameters including height and volume of water behind the dam at the time of failure. Estimation of optimal weights and biases in the training phase of the ANN is analysed by two different algorithms including Levenberg—Marquardt (LM) as a standard technique used to solve nonlinear least squares problems and Imperialist Competitive Algorithm (ICA) as a new evolutionary algorithm in the evolutionary computation field. Comparison of the obtained results with those of the conventional approach based on regression analysis shows a better performance of the ANN model trained with ICA. Investigation on the uncertainty band of the models indicated that LM predictions have the least uncertainty band whilst ICA’s have the lowest mean prediction error. More analysis on the models’ uncertainty is conducted by a Monte Carlo simulation in which 1000 randomly generated sets of input data are sampled from the database of historical dam failures. The result of 1000 ANN models which have been analysed with three statistical measures including p-factor, d-factor, and DDR confirms that LM predictions have more limited uncertainty band

    Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System

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    Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations

    Satellite-Based Monitoring of Growing Agricultural Water Consumption in Hyper-Arid Regions

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    Land-use change has a key role in hydrologic processes and biodiversity. Although many satellite-based studies have been conducted to reveal the interaction between land-use changes in hydrological processes worldwide, the land-use change impact on agricultural water consumption in hyper-arid regions is poorly understood. Here, we investigate increased agricultural water consumption in the Qom province, a hyper-arid region in Iran, using derived imageries from Landsat 5 Tm and Landsat 8 OLI during the last three decades. We used maximum likelihood classification (MLC) and decision tree classification (DTC) to analyze the satellite images. The MLC method showed that croplands have increased from 30,547 ha in 1989 to 39,255 ha in 2019 (i.e., a 29% increase). In this period, the total orchard area increased from 3904 ha to 6307 ha, revealing a growth of 61%. In the DTC method, the increases in the cropland and orchard areas were, respectively, 34% and 60%. Although both MLC and DTC satisfied the accuracy criteria, the former was more consistent than the latter concerning ground data and documented statistics. Satellite-based and MLC results showed an increase in agricultural water consumption from 152 million cubic meters (MCM) in 1989 to 209 MCM in 2019, showing a 38% increase (i.e., 1.27% annually). Our findings send an alarming message for policymakers to reduce the water demand through sustainable agricultural practices in the Qom province, where the agricultural sector uses approximately 90% of annual water demand

    ThSSim:a novel tool for simulation of reservoir thermal stratification

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    Abstract This study presents a novel tool, ThSSim, for simulation of thermal stratification (ThS) in reservoirs. ThSSim is a simple and flexible reduced-order model-based the basis function (RMBF) that combines CE-QUAL-W2 (W2) and proper orthogonal decomposition (POD). In a case study, it was used to simulate water temperature in the Karkheh Reservoir (KR), Iran, for the period 2019–2035. ThSSim consists of two space- and time-dependent components that add predictive ability to the RMBF, a major refinement that extends its practical applications. Water temperature simulations by the W2 model at three-hour time intervals for the KR were used as input data to the POD model to develop ThSSim. To add predictive ability to ThSSim and considering that space-dependent components are not a function of time, we extrapolated the first three time-dependent components by September 30, 2035. We checked the predictive ability of ThSSim against water temperature profiles measured during eight sampling campaigns. We then applied ThSSim to simulate water temperature in the KR for 2019–2035. Simulated water temperature values matched well those measured and obtained by W2. ThSSim results showed an increasing trend for surface water temperature during the simulation period, with a reverse trend observed for water temperature in the bottom layers for three seasons (spring, summer and autumn). The results also indicated decreasing and increasing trends in onset and breakdown of thermal stability, respectively, so that the duration of ThS increased from 278 days in 2019 to 293 days in 2035. ThSSim is thus useful for reservoir temperature simulations. Moreover, the approach used to develop ThSSim is widely applicable to other fields of science and engineering

    PODMT3DMS-Tool : proper orthogonal decomposition linked to the MT3DMS model for nitrate simulation in aquifers

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    The PODMT3DMS-Tool, which consists of a proper orthogonal decomposition (POD) linked to the Modular Transport 3-Dimensional Multi Species (MT3DMS) code for nitrate simulation in groundwater, is introduced. POD, as a statistical technique, reduces a large amount of information produced by the MT3DMS model to provide the main components of the PODMT3DMS-Tool, i.e., space- and time-dependent terms of nitrate. The low-dimensional components represent time- and space-dependent factors in the aquifer response such as hydraulic, hydrogeological and water quality variables represented in the simulation using the MT3DMS model. The PODMT3DMS-Tool is thus a combined statistical and conceptual model with a simple structure and comparable accuracy to MT3DMS. Practical application of the PODMT3DMS-Tool to the Karaj Aquifer in Iran during 6 years revealed agreement between nitrate concentrations simulated by the PODMT3DMS-Tool and MT3DMS, with a mean absolute error of less than 0.5 mg/L in most parts of the aquifer. Moreover, the PODMT3DMS-Tool needed only about 10% of the calculation time required by MT3DMS. The PODMT3DMS-Tool can be used in predict nitrate concentration in the Karaj Aquifer, while its simplicity also makes it highly interesting for other water resources problems

    Reliability of functional forms for calculation of longitudinal dispersion coefficient in rivers

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    Abstract Although dimensional analysis suggests sound functional forms (FFs) to calculate longitudinal dispersion coefficient (Kx), no attempt has been made to quantify both reliability of the estimated Kx value and its sensitivity to variation of the FFs' parameters. This paper introduces a new index named bandwidths similarity factor (bws–factor) to quantify the reliability of FFs based on a rigorous analysis of distinct calibration datasets to tune the FFs. We modified the bootstrap approach to ensure that each resampled calibration dataset is representative of available datapoints in a rich, global database of tracer studies. The dimensionless Kx values were calculated by 200 FFs tuned with the generalized reduced gradient algorithm. Correlation coefficients for the tuned FFs varied from 0.60 to 0.98. The bws–factor ranged from 0.11 to 1.00, indicating poor reliability of FFs for Kx calculation, mainly due to different sources of error in the Kx calculation process. The calculated exponent of the river's aspect ratio varied over a wider range (i.e., −0.76 to 1.50) compared to that computed for the river's friction term (i.e., −0.56 to 0.87). Since Kx is used in combination with one-dimensional numerical models in water quality studies, poor reliability in its estimation can result in unrealistic concentrations being simulated by the models downstream of pollutant release into rivers

    A finite volume method for a 2D dam-break simulation on a wet bed using a modified HLLC scheme

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    his study proposes a numerical model for depth-averaged Reynolds equations (shallow-water equations) to investigate a dam-break problem, based upon a two-dimensional (2D) second-order upwind cell-center finite volume method. The transportation terms were modelled using a modified approximate HLLC Riemann solver with the first-order accuracy. The proposed 2D model was assessed and validated through experimental data and analytical solutions for several dam-break cases on a wet and dry bed. The results showed that the error values of the model are lower than those of existing numerical methods at different points. Our findings also revealed that the dimensionless error parameters decrease as the wave propagates downstream. In general, the new model can model the dam-break problem and captures the shock wave superbly.No sponso

    Metal contamination assessment in water column and surface sediments of a warm monomictic man-made lake:Sabalan Dam Reservoir, Iran

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    Abstract In this study, metal concentrations in the water column and surface sediment of the Sabalan Dam Reservoir (SDR) were determined. Moreover, heavy metal pollution index (HPI), contamination index (CI), heavy metal evaluation index (HEI), enrichment factor (EF), geo-accumulation index (Igeo), sediment quality guidelines (SQGs), consensus-based SQGs (C-BSQGs), and mean probable effect concentration quotients (mPECQs) were evaluated for water and sediments of SDR. It was observed that metal concentrations in river entry sediment were lower, but those in river entry water were higher than corresponding values in the vicinity of the dam structure. The HPI values of water samples taken from 10 m depth in the center of SDR exceeded the critical limit, due to high concentrations of arsenic. However, according to CI, the reservoir water was not contaminated. The HEI values indicated contamination of SDR water with metals at 10 m depth. A comparison of water quality indices revealed that HEI was the most reliable index in water quality assessment, while CI and HPI were not sufficiently accurate. For SQGs, As and Cu concentrations in sediments were high, but mPECQ, Igeo, and EF revealed some degree of sediment pollution in SDR. The calculated EF values suggested minor anthropogenic enrichment of sediment with Fe, Co, V, and Ni; moderate anthropogenic enrichment with As and Mn; and moderate to severe anthropogenic enrichment with Cu. A comparison of SQG values revealed that the threshold effect and probable effect levels were the most reliable metrics in the assessment of sediment toxicity. Statistical analysis indicated similarities between metal concentrations in the center of the reservoir and near to the dam structure, as a result of similar sediment deposition behavior at these points, while higher flow velocity at the river entry point limited deposition of fine particles and associated metals

    Caspian Sea is eutrophying:the alarming message of satellite data

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    Abstract The competition over extracting the energy resources of the Caspian Sea together with the major anthropogenic changes in the coastal zones have resulted in increased pollution and environmental degradation of the sea. We provide the first evaluation of the spatiotemporal variation of chlorophyll-a (Chl-a) across the Caspian Sea. Using remotely sensed data from 2003 to 2017, we found that the Caspian Sea has suffered from a growing increase in Chl-a, especially in warmer months. The shallow parts of the sea, near Russia and Kazakhstan, especially where the Volga and Terek rivers discharge large nutrient loads (nitrogen- and phosphorus-rich compounds) into the sea, have experienced the highest variations in Chl-a. The Carlson's trophic state index showed that during the study period, on average, about 12%, 26%, and 62% of the Caspian Sea's area was eutrophic, mesotrophic, and oligotrophic, respectively. The identified trends reflect an increasing rate of environmental degradation in the Caspian Sea, which has been the subject of conflict among its littoral states that since the collapse of the Soviet Union have remained unable to agree on a legal regime for governing the sea and its resources
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