Development of Hybrid Adaptive Neuro Fuzzy Inference System - Harris Hawks Optimizer (ANFIS-HHO) for Monthly Inlet Flow to Dam Reservoirs Prediction

Nowadays, machine learning models are able to make good predictions based on pattern extraction between data. In this study, a neural-fuzzy network (ANFIS) was used to predict the inflow to the reservoirs of a dam namely, the Mahabad dam located in the northwestern part of Iran. A new Harris Hawk (HHO) optimization algorithm was also used to improve the ANFIS (HHO-ANFIS) structure. Monthly precipitation and temperature and inlet flow data to the reservoir one to three months ago were used as input parameters as 6 different input patterns. About 70% of the data was used for training and 30% to test the models. The results showed that the ANFIS model has good accuracy in training data although, for test data, its accuracy was greatly reduced. The development of the HHO-ANFIS model improved the accuracy of the prediction. The patterns with all input parameters had the highest prediction accuracy. In this pattern, values ​​of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Nash Sutcliffe Efficiency coefficient (NSE) for test data were 3.9 MCM, 2.41 MCM, and 0.86, respectively. Due to the good performance of the model used, it can be recommended for time series predictions

Investigating and Modeling of the Scour Downstream of a Tree Trunk Deflector in a Straight Channel

Scouring depends on several factors, including the water flow of artificial obstacles, sections, piers, and foundations, the disturbance of bed materials, and soil permeability. The other factors are the non-parallelism between piers and the water flow, the type of river activity (static or dynamic), and the existence of a waterfall or an obstacle that forms a waterfall in natural bed materials, causing the underlying bed materials to be washed away. This study fully investigated how the movement of a tree trunk affects a river’s flow by considering different flow conditions using the artificial neural network (ANN) model. A feedforward optimal network with the error back-propagation training algorithm and sigmoid transfer functions was used for four models. To determine the number of neurons in the hidden layer, one and ten neurons were selected in the hidden layer according to verification indicators. In addition, a physical model was utilized to measure data. To verify and test the models, our data were gathered in a laboratory using the physical model. Considering the network structure of one neuron in the hidden layer, a comparison was made between dimensional and dimensionless parameter models that are effective in terms of the dimensions of the scour hole. The comparison between the results of the ANN and the measured data using nonlinear regression models demonstrated that the ANN was more accurate and capable of simulating phenomena. Additionally, R and RMSE values were between 0.93 and 0.98, as well as 0.18 and 0.013, respectively. Finally, the results related to the width, height, length, and depth of the scour revealed that the modified DOT model had the best agreement with Mahdavizadeh’s measured data

Studying Unimodal, Bimodal, PDI and Bimodal-PDI Variants of Multiple Soil Water Retention Models: I. Direct Model Fit Using the Extended Evaporation and Dewpoint Methods

This study focuses on the reliable parametrization of the full Soil Water Retention Curve (SWRC) from saturation to oven-dryness using high resolution but limited range measured water retention data by the Hydraulic Property Analyzer (HYPROP) system. We studied the performance of five unimodal water retention models including the Brooks and Corey model (BC model), the Fredlund and Xing model (FX model), the Kosugi model (K model), the van Genuchten constrained model with four free parameters (VG model), and the van Genuchten unconstrained model with five free parameters (VGm model). In addition, eleven alternative expressions including Peters–Durner–Iden (PDI), bimodal, and bimodal-PDI variants of the original models were evaluated. We used a data set consisting of 94 soil samples from Turkey and the United States with high-resolution measured data (a total of 9264 measured water retention data pairs) mainly via the HYPROP system and supplemented for some samples with measured dry-end data using the WP4C instrument. Among unimodal expressions, the FX and the K models with the Mean Absolute Error (MAE) values equal to 0.005 cm3 cm−3 and 0.015 cm3 cm−3 have the highest and the lowest accuracy, respectively. Overall, the alternative variants provided a better fit than the unimodal expressions. The unimodal models, except for the FX model, fail to provide reliable dry-end estimations using HYPROP data (average MAE: 0.041 cm3 cm−3, average r: 0.52). Our results suggested that only models that account for the zero water content at the oven dryness and properly shift from the middle range to dry-end (i.e., the FX model and PDI variants) can adequately represent the full SWRC using typical data obtained via the HYPROP system

The Sensitivity Analysis of the Drainage Unsteady Equations against the Depth of Drain Placement and Rainfall Time at the Shallow Water-Bearing Layers: A Case Study of Markazi Province, Iran

This research investigated various drainage parameters for unsteady conditions, including depth of installation, reflection coefficient, and depth of water table. For this purpose, Bouwer & Van Schilfgarrd, Dumm, Glover, Hemmad, and Bouwer equations were used. For the distance of computed drainage compared with measured data in central Iran, the results showed that the Bouwer & Van Schilfgaarde equation is better than others. Additionally, the installed depth was obtained 130 cm below the exiting underground, and this depth was applicable more than other depths; 1, 3, and 5-day precipitation were used to determine water table changes. The results illustrated that a 5-day duration had a better effect, which appeared in the existing condition drainage area. The reflection coefficient for the superior equation was also obtained as 0.65, which was very close to the measured data in the area. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Standard deviation (σ) were used to evaluate the results. MAE, RMSE, and σ were computed as 1.78, 2.02, and 0.02, for the superior equation respectively, and the appropriate distance between the two drains was determined as 51.26 m. The obtained results have a close agreement with other researchers in this regard

Performance optimization of water distribution network using meta-heuristic algorithms from the perspective of leakage control and resiliency factor (case study: Tehran water distribution network, Iran)

In the context of sustainable development and considering water distribution networks (WDNs) as vital infrastructure systems, designing a resilient and efficient network to deliver water demand to consumption nodes while adhering to engineering standards is of utmost importance. This study specifically focused on the complex structure of the north-west Tehran's WDN, encompassing 1124 pipes totaling 92552 m in length, along with four gravity reservoirs and 988 nodes. Genetic Algorithm (GA) and Nonlinear Programming (NLP) were employed as optimization techniques to enhance the WDN by minimizing leakage and improving its resilience. The study involved determining leakage coefficients for nodes using measured data and GA. Subsequently, the WDN was optimized by defining an objective function, constraints, and decision variables using both GA and NLP. The results demonstrated the superiority of GA in terms of pressure reduction, achieving a significant decrease of 23.7%. Additionally, GA outperformed NLP in enhancing the resiliency index, underscoring its effectiveness in optimizing the network's performance and ensuring its robustness against potential disruptions

Multistage Models for Flood Control by Gated Spillway: Application to Karkheh Dam

The paper demonstrates a simulation optimization framework for enhancing the real-time flood control with gated spillways at places where no flood forecasting data are available. A multiobjective modeling scheme is presented for the flood management in a gated spillway in which the operator may specify the priorities on floods based on their different return periods. Two different operation strategies were devised. Both operating strategies employ ten-stage policies, which rely on the reservoir water level as the input data. The second strategy benefits from both the observed reservoir water level and the flood peak. The optimal values of the models’ parameters were obtained using a genetic algorithm. This is a novel approach because none of its policies needs flood forecasting data, thus, making them adaptable to any flood with any return period. To evaluate the performances of the proposed models, the flood control through a gated spillway of the Karkheh reservoir was considered, where flood hydrographs with different return periods were routed through the reservoir

Post-Treatment of Reclaimed Municipal Wastewater through Unsaturated and Saturated Porous Media in a Large-Scale Experimental Model

In recent decades, groundwater overexploitation has caused an important aquifer level decline in arid zones each year. In addition to this issue, large volumes of effluent are produced each year in metropolitan areas of these regions. In this situation, an aquifer storage and recovery system (ASR) using the reclaimed domestic wastewater can be a local solution to these two challenges. In this research, a post-treatment of reclaimed municipal wastewater has been investigated through unsaturated–saturated porous media. A large-scale, L-shaped experimental model was set up near the second-stage wastewater treatment plant (WWTP) in the west of greater Tehran. The water, soil, and treated wastewater of the experimental model were supplied from the aquifer, site, and WWTP, respectively. The 13 physicochemical parameters, temperature and fecal coliform were analyzed every 10 days in seven points for a period of four months (two active periods of 40 days with a 12-h on–off rate (wet cycles) and a rest period of 40 days (dry cycle) between the two wet cycles). The results showed that the effects of the saturated zone were twice as great as those of the unsaturated zone and two-thirds of the total treatment efficiency. Furthermore, a discontinuous wet–dry–wet cycle had a significant effect on effluent treatment efficiency and contaminants’ reduction. In conclusion, an aquifer storage and recovery system using treated wastewater through the unsaturated–saturated zones is a sustainable water resource that can be used for agriculture, environmental and non-potable water demands

DESIGNING HYDRAULIC AIR CHAMBER IN WATER TRANSMISSION SYSTEMS USING GENETIC ALGORITHM

Transient flow control in Water Transmission Systems (WTS) is one of the requirements of designing these systems. Hence, among control equipment, air chambers offer the best solution to control transient flow effects, i.e. both prevents water column separation and absorbs pressure increase. It is essential to carry out an accurate and optimized design of air chambers, not only due to high costs of their manufacturing but also their important protective role. Accordingly, hydraulic design parameters comprise tank volume, diameter of nozzle and coefficients of inflow and outflow of nozzle. In this paper, it is intended to optimize these parameters in order to minimize manufacturing costs. On the other hand, maximum and minimum pressures in main pipeline are considered as constraints which shall fall in allowed range. Therefore, a model has been developed which is a combination of a hydraulic simulation model of WTS and an optimization model based on genetic algorithm. This model is first applied to WTS of Dehgolan-Ghorveh plain as a case study. Results of this research demonstrate that based on suggested model, negative wave creation and pressure increase in pipeline is prevented as well as decrease in manufacturing costs of air chamber

The Sensitivity Analysis of the Drainage Unsteady Equations against the Depth of Drain Placement and Rainfall Time at the Shallow Water-Bearing Layers: A Case Study of Markazi Province, Iran

This research investigated various drainage parameters for unsteady conditions, including depth of installation, reflection coefficient, and depth of water table. For this purpose, Bouwer & Van Schilfgarrd, Dumm, Glover, Hemmad, and Bouwer equations were used. For the distance of computed drainage compared with measured data in central Iran, the results showed that the Bouwer & Van Schilfgaarde equation is better than others. Additionally, the installed depth was obtained 130 cm below the exiting underground, and this depth was applicable more than other depths; 1, 3, and 5-day precipitation were used to determine water table changes. The results illustrated that a 5-day duration had a better effect, which appeared in the existing condition drainage area. The reflection coefficient for the superior equation was also obtained as 0.65, which was very close to the measured data in the area. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Standard deviation (σ) were used to evaluate the results. MAE, RMSE, and σ were computed as 1.78, 2.02, and 0.02, for the superior equation respectively, and the appropriate distance between the two drains was determined as 51.26 m. The obtained results have a close agreement with other researchers in this regard