Meta-heuristic Optimization Algorithms for Predicting the Scouring Depth Around Bridge Piers

An accurate estimation of bridge pier scour has been considered as one of the important parameters in designing of bridges. However, due to the numerous involved parameters and convolution of this phenomenon, many existing approaches cannot predict scour depth with an acceptable accuracy. Obtained results from the empirical relationships show that these relationships have low accuracy in determining the maximum scour depth and they need a high safety factor for many cases, which leads to uneconomic designs of bridges. To cover these disadvantages, three new models are provided to estimate the bridge pier scour using an adaptive network-based fuzzy inference system. The parameters of the system are optimized by using the colliding bodies optimization, enhanced colliding bodies optimization and vibrating particles system methods. To evaluate the efficiency of the proposed methods, their results were compared with those of simple adaptive network-based fuzzy inference system and its improved versions by using the particle swarm optimization and genetic algorithm as well as the empirical equations. Comparison of results showed that the new vibrating particles system based algorithm could find better results than other two ones. In addition, comparison of the results obtained by the proposed methods with those of the empirical relations confirmed the high performance of the new methods

DENOISING- JITTERING DATA PRE-PROCESSING TECHNIQUE TO IMPROVE ARTIFICIAL INTELLIGENCE BASED RAINFALL- RUNOFF MODELING

Successful modeling of hydro-environmental processes widely rel ies on quantity and quality of accessible data and noisy data might effect on the f unctioning of the modeling. On the other hand in training phase of any Artificial Intelligence (AI) based model, each training data set is usually a limited sample of po ssible patterns of the process and hence, might not show the behavior of whole populat ion. Accordingly in the present article first, wavelet-based denoising method was u sed in order to smooth hydrological time series and then small normally distributed no ises with the mean of zero and various standard deviati ons were generated and added t o the smoothed time series to form different denoised-jittered training data sets, for Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling of daily rainfall – runoff process of the Oconee River watershed l ocated in USA. To evaluate the modeling performance, the outcomes were compared w ith the results of multi linear regression (MLR) and Auto Regressive Integrated Mo ving Average (ARIMA) models. Comparing the achieved results via the trained ANN and ANFIS models using denoised-jittered data showed that the proposed da ta processing approach which serves both denoising and jittering techniques could impr ove performance of the ANN and ANFIS based rainfall-runoff modeling of the Oconee Rive r Watershed up to 13% and 11% in the verification phase

Bi-objective Optimization of the Water Distribution Networks (Case Study: Sahand City)

To design an urban water network in addition to minimizing the cost, improving the water pressure is very important. Then in this paper a bi-objective optimization model for the new city of Sahand in Northwestern Iran is developed.  Due to its non-linearity and the huge number of variables, the genetic algorithm has been utilized to solve it. Several Pareto solutions have been obtained and then based on the game theory approach (the area monotonic solution), the most efficient point was provided. The solution is simulated by the WaterGems software and the elements of the network are designed. This optimum solution shows a decrease of 13% in total cost in addition to the improved water pressure

SM2RAIN-ASCAT satellite-based spatiotemporal drought monitoring using multiscale WT-VMD-ENERGY method

Drought is one of the severe natural disasters that has devastating effects on various parts of the environment. Therefore, spatiotemporal monitoring of drought using reliable methods is very important. In this study, using a new Discrete Wavelet Transform (DWT)-Variational Mode Decomposition (VMD)-Energy multi-scale approach, the efficiency of the new Soil Moisture to Rain-Advanced SCATterometer (SM2RAIN-ASCAT) precipitation product was investigated in monitoring the spatiotemporal patterns of short- to long-term droughts for the Northwest part of Iran. In this regard, after validating the accuracy of the SM2RAIN-ASCAT datasets, a multi-scale method was developed based on the serial decomposition of the drought signals. The mean energy amounts of the obtained subseries were imposed into the K-means (K signifies the number of clusters) to identify the drought-prone regions. Results showed that the highest Correlation Coefficient (R) between SM2RAIN-ASCAT and in-situ observations are found at monthly time scales, in which 73% of the stations had R higher than 0.7. Investigating the reliability of this product for drought monitoring showed that for short- and mid-term timescales in 75% of the stations, the values of R were higher than 0.7. The applied methodology successfully showed the drought-prone regions and a direct relationship was found between energy and drought intensity. The capability of the applied methodology was verified via the CPC Merged Analysis of Precipitation (CMAP) and Moderate Resolution Imaging Spectroradiometer (MODIS) products and the obtained results proved the desirable performance of the SM2RAIN-ASCAT datasets. It was found that SOI, Nino3, and Nino3.4 had major effects on the drought index in the selected area, while AO had the least effect. The relationship between the LST and SPIs was negative, while there was a positive relationship between NDVI and SPIs

Monitoring and prediction of land use/land cover changes and water requirements in the basin of the Urmia Lake, Iran

As one of the largest super-saline lakes in the world, Lake Urmia in northwestern Iran has been facing severe drying in recent years. Drought and rapid expansion of agricultural activities are considered to be the main driving factors in the shrinking of the lake. To address this problem, an analysis of the spatiotemporal dynamics of land use/land cover (LULC) is important. This research implemented a multi-source satellite image analysis through support vector machine (SVM) for mapping LULC distributions for the years 2000, 2010, and 2020. Cellular automata (CA)–Markov was prepared for modeling the future landscape changes for 2030 and 2040. In the last step, the water requirement of agriculture in the catchment area of the Urmia Lake was simulated through the NETWAT model. Through the employed future LULC modeling, it was found that the areas covered by irrigated agriculture and gardens will grow from 1,450 and 395 km2 to 3,600 and 1,650 km2 in 2040, respectively, as deduced from the changes that occurred from 2000 to 2020. This will increase the water requirement of agriculture from 1.5 billion cubic metres in 2000 to more than 4.1 billion cubic metres in 2040. HIGHLIGHTS LULC modeling is implemented through a CA–Markov model to predict future LULC for 2030 and 2040.; The NETWAT model was used to simulate the water requirement of agriculture.; A significant increase in the areas covered by agricultural activities were identified.; A 200% increase in water requirement of agriculture was observed in the period of 40 years (2000–2040).

Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River

Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO4), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH4-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d-factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems