Convolutional neural network-multi-kernel radial basis function neural network-salp swarm algorithm: a new machine learning model for predicting effluent quality parameters.

A wastewater treatment plant (WWTP) is an essential part of the urban water cycle, which reduces concentration of pollutants in the river. For monitoring and control of WWTPs, researchers develop different models and systems. This study introduces a new deep learning model for predicting effluent quality parameters (EQPs) of a WWTP. A method that couples a convolutional neural network (CNN) with a novel version of radial basis function neural network (RBFNN) is proposed to simultaneously predict and estimate uncertainty of data. The multi-kernel RBFNN (MKRBFNN) uses two activation functions to improve the efficiency of the RBFNN model. The salp swarm algorithm is utilized to set the MKRBFNN and CNN parameters. The main advantage of the CNN-MKRBFNN-salp swarm algorithm (SSA) is to automatically extract features from data points. In this study, influent parameters (if) are used as inputs. Biological oxygen demand (BODif), chemical oxygen demand (CODif), total suspended solids (TSSif), volatile suspended solids (VSSif), and sediment (SEDef) are used to predict EQPs, including CODef, BODef, and TSSef. At the testing level, the Nash-Sutcliffe efficiencies of CNN-MKRBFNN-SSA are 0.98, 0.97, and 0.98 for predicting CODef, BODef, and TSSef. Results indicate that the CNN-MKRBFNN-SSA is a robust model for simulating complex phenomena.

Comparative Study of Chemically Treated Sugarcane and Kevlar Fiber to Develop Brake Resistance Composites.

Recently, much research has revealed the increasing importance of natural fiber in modern applications. Natural fibers are used in many vital sectors like medicine, aerospace and agriculture. The cause of increasing the application of natural fiber in different fields is its eco-friendly behavior and excellent mechanical properties. The study's primary goal is to increase the usage of environmentally friendly materials. The existing materials used in brake pads are detrimental to humans and the environment. Natural fiber composites have recently been studied and effectively employed in brake pads. However, there has yet to be a comparison investigation of natural fiber and Kevlar-based brake pad composites. Sugarcane, a natural fabric, is employed in the present study to substitute trendy materials like Kevlar and asbestos. The brake pads have been developed with 5-20 wt.% SCF and 5-10 wt.% Kevlar fiber (KF) to make the comparative study. SCF compounds at 5 wt.% outperformed the entire NF composite in coefficient of friction (µ), (%) fade and wear. However, the values of mechanical properties were found to be almost identical. Although it has been observed that, with an increase in the proportion of SCF, the performance also increased in terms of recovery. The thermal stability and wear rate are maximum for 20 wt.% SCF and 10 wt.% KF composites. The comparative study indicated that the Kevlar-based brake pad specimens provide superior outcomes compared to the SCF composite for fade (%), wear performance and coefficient of friction (Δµ). Finally, the worn composite surfaces were examined using a scanning electron microscopy technique to investigate probable wear mechanisms and to comprehend the nature of the generated contact patches/plateaus, which is critical for determining the tribological behavior of the composites.

A comparative study of black-box and white-box data-driven methods to predict landfill leachate permeability.

Due to the dynamic and complexity of leachate percolation within municipal solid waste (MSW), planning and operation of solid waste management systems are challenging for decision-makers. In this regard, data-driven methods can be considered robust approaches to modeling this problem. In this paper, three black-box data-driven models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and support vector regression (SVR), and also three white-box data-driven models, including the M5 model tree (M5MT), classification and regression trees (CART), and group method of data handling (GMDH), were developed for modeling and predicting landfill leachate permeability ([Formula: see text]). Based on a previous study conducted by Ghasemi et al. (2021), [Formula: see text] can be formulated as a function of impermeable sheets ([Formula: see text]) and copper pipes ([Formula: see text]). Hence, in the present study, [Formula: see text] and [Formula: see text] were adopted as input variables for the prediction of [Formula: see text] and evaluated for the performance of the suggested black-box and white-box data-driven models. Scatter plots and statistical indices such as coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) were used for qualitative and quantitative evaluations of the effectiveness of the suggested methods. The outcomes indicated all of the provided models successfully predicted [Formula: see text]. However, ANN and GMDH had higher accuracy between the proposed black-box and white-box data-driven models. ANN with R2 = 0.939, RMSE = 0.056, and MAE = 0.017 was marginally better than GMDH with R2 = 0.857, RMSE = 0.064, and MAE = 0.026 in the testing stage. Nevertheless, an explicit mathematical expression provided by GMDH to predict k was easier and more understandable than ANN.

Principal of environmental life cycle assessment for medical waste during COVID-19 outbreak to support sustainable development goals.

Disposal of medical waste (MW) must be considered as a vital need to prevent the spread of pandemics during Coronavirus disease of the pandemic in 2019 (COVID-19) outbreak in the globe. In addition, many concerns have been raised due to the significant increase in the generation of MW in recent years. A structured evaluation is required as a framework for the quantifying of potential environmental impacts of the disposal of MW which ultimately leads to the realization of sustainable development goals (SDG). Life cycle assessment (LCA) is considered as a practical approach to examine environmental impacts of any potential processes during all stages of a product's life, including material mining, manufacturing, and delivery. As a result, LCA is known as a suitable method for evaluating environmental impacts for the disposal of MW. In this research, existing scenarios for MW with a unique approach to emergency scenarios for the management of COVID-19 medical waste (CMW) are investigated. In the next step, LCA and its stages are defined comprehensively with the CMW management approach. Moreover, ReCiPe2016 is the most up-to-date method for computing environmental damages in LCA. Then the application of this method for defined scenarios of CMW is examined, and interpretation of results is explained regarding some examples. In the last step, the process of selecting the best environmental-friendly scenario is illustrated by applying weighting analysis. Finally, it can be concluded that LCA can be considered as an effective method to evaluate the environmental burden of CMW management scenarios in present critical conditions of the world to support SDG.

Machine learning based marine water quality prediction for coastal hydro-environment management.

During the past three decades, harmful algal blooms (HAB) events have been frequently observed in marine waters around many coastal cities in the world including Hong Kong. The increasing occurrence of HAB has caused acute influences and damages on water environment and marine aquaculture with millions of monetary losses. For example, the Tolo Harbour is one of the most affected areas in Hong Kong, where more than 30% HAB occurred. In order to forewarn the potential HAB incidents, the machine learning (ML) methods have been increasingly resorted in modelling and forecasting water quality issues. In this study, two different ML methods - artificial neural networks (ANN) and support vector machine (SVM) - are implemented and improved by introducing different hybrid learning algorithms for the simulations and comparative analysis of more than 30-year measured data, so as to accurately forecast algal growth and eutrophication in Tolo Harbour in Hong Kong. The application results show the good applicability and accuracy of these two ML methods for the predictions of both trend and magnitude of the algal growth. Specifically, the results reveal that ANN is preferable to achieve satisfactory results with quick response, while the SVM is suitable to accurately identify the optimal model but taking longer training time. Moreover, it is demonstrated that the used ML methods could ensure robustness to learn complicated relationship between algal dynamics and different coastal environmental variables and thereby to identify significant variables accurately. The results analysis and discussion of this study also indicate the potentials and advantages of the applied ML models to provide useful information and implications for understanding the mechanism and process of HAB outbreak and evolution that is helpful to improving the water quality prediction for coastal hydro-environment management.

Data supporting midpoint-weighting life cycle assessment and energy forms of cumulative exergy demand for horticultural crops.

With an increasing demand of horticultural crops, it is critical to examine environmental damages and exergy impacts and evaluate their potential in producing sustainable products of agricultural systems. As such, environmental midpoints of five dominated horticultural products, namely, hazelnut, watermelon, tea, kiwifruit, and citrus, are scrutinized using life cycle assessment approach in Guilan province, Iran. Each crop is considered under a separate scenario and 10 tons of yield is determined as the functional unit. ReCiPe2016, as a new approach, is used for computation of 17 midpoints. Moreover, a weighting analysis is undertaken to find the share of each input in environmental damages with dimensionless notation. In the second part of this paper, cumulative exergy demand (CExD) is applied for evaluation of energy forms in each scenario. Data are presented under two sectors in the main article. The first part is midpoint results of each crop and the second part depicts energy forms of CExD with input rate in each category. Besides, the supplementary files contain raw material of each input, midpoint physical rate, share of each input to contribute midpoint, raw data of weighted damages and share of each input in total weighted damages.

Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production.

This study aims to employ two artificial intelligence (AI) methods, namely, artificial neural networks (ANNs) and adaptive neuro fuzzy inference system (ANFIS) model, for predicting life cycle environmental impacts and output energy of sugarcane production in planted or ratoon farms. The study is performed in Imam Khomeini Sugarcane Agro-Industrial Company (IKSAIC) in Khuzestan province of Iran. Based on the cradle to grave approach, life cycle assessment (LCA) is employed to evaluate environmental impacts and study environmental impact categories of sugarcane production. Results of this study show that the consumed and output energies of sugarcane production are in average 172,856.14 MJ ha-1, 120,000 MJ ha-1 in planted farms and 122,801.15 MJ ha-1, 98,850 MJ ha-1 in ratoon farms, respectively. Results show that, in sugarcane production, electricity, machinery, biocides and sugarcane stem cuttings have the largest impact on the indices in planted farms. However, in ratoon farms, electricity, machinery, biocides and nitrogen fertilizers have the largest share in increasing the indices. ANN model with 9-10-5-11 and 7-9-6-11 structures are the best topologies for predicting environmental impacts and output energy of sugarcane production in planted and ratoon farms, respectively. Results from ANN models indicated that the coefficient of determination (R2) varies from 0.923 to 0.986 in planted farms and 0.942 to 0.982 in ratoon farms in training stage for environmental impacts and outpt energy. Results from ANFIS model, which is developed based on a hybrid learning algorithm, showed that, for prediction of environmental impacts, R2 varies from 0.912 to 0.978 and 0.986 to 0.999 in plant and ratoon farms, respectively, and for prediction of output energy, R2 varies from 0.944 and 0.996 in planted and ratoon farms. Results indicate that ANFIS model is a useful tool for prediction of environmental impacts and output energy of sugarcane production in planted and ratoon farms.

Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production.

Prediction of agricultural energy output and environmental impacts play important role in energy management and conservation of environment as it can help us to evaluate agricultural energy efficiency, conduct crops production system commissioning, and detect and diagnose faults of crop production system. Agricultural energy output and environmental impacts can be readily predicted by artificial intelligence (AI), owing to the ease of use and adaptability to seek optimal solutions in a rapid manner as well as the use of historical data to predict future agricultural energy use pattern under constraints. This paper conducts energy output and environmental impact prediction of paddy production in Guilan province, Iran based on two AI methods, artificial neural networks (ANNs), and adaptive neuro fuzzy inference system (ANFIS). The amounts of energy input and output are 51,585.61MJkg-1 and 66,112.94MJkg-1, respectively, in paddy production. Life Cycle Assessment (LCA) is used to evaluate environmental impacts of paddy production. Results show that, in paddy production, in-farm emission is a hotspot in global warming, acidification and eutrophication impact categories. ANN model with 12-6-8-1 structure is selected as the best one for predicting energy output. The correlation coefficient (R) varies from 0.524 to 0.999 in training for energy input and environmental impacts in ANN models. ANFIS model is developed based on a hybrid learning algorithm, with R for predicting output energy being 0.860 and, for environmental impacts, varying from 0.944 to 0.997. Results indicate that the multi-level ANFIS is a useful tool to managers for large-scale planning in forecasting energy output and environmental indices of agricultural production systems owing to its higher speed of computation processes compared to ANN model, despite ANN's higher accuracy.

Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models.

This study explores two ideas to made an improvement on the artificial neural network (ANN)-based models for suspended sediment forecasting in several time steps ahead. In this regard, both observed and forecasted time series are incorporated as input variables of the models when applied for more than one lead time. Secondly, least-square ensemble models employing multiple wavelet-ANN models are developed to increase the performance of the single model. For this purpose, different wavelet families are linked with the ANN model and performance of each model is evaluated using error measures. The Skagit River near Mount Vernon in Washington county is selected as the case study. The daily flow discharge and suspended sediment concentration (SSC) in the current day are considered as input variables to predict suspended sediment concentration in the next day. For more lead times, the input structure is updated by adding the forecast of SSC in the previous time step. Results of this study demonstrate that incorporating both observed and predicted variables in the input structure improves performance of conventional models in which those only employ observed time series as input variables. Moreover, ensemble model developed for each lead time outperforms the best single wavelet-ANN model which indicates superiority of the ensemble model over the other one. Findings of this study reveal that acceptable forecasts of daily suspended sediment concentration up to 3 days in advance can be achieved using the proposed methodology.

A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States.

Accurate and reliable suspended sediment load (SSL) prediction models are necessary for planning and management of water resource structures. More recently, soft computing techniques have been used in hydrological and environmental modeling. The present paper compared the accuracy of three different soft computing methods, namely, artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), coupled wavelet and neural network (WANN), and conventional sediment rating curve (SRC) approaches for estimating the daily SSL in two gauging stations in the USA. The performances of these models were measured by the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (CE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) to choose the best fit model. Obtained results demonstrated that applied soft computing models were in good agreement with the observed SSL values, while they depicted better results than the conventional SRC method. The comparison of estimation accuracies of various models illustrated that the WANN was the most accurate model in SSL estimation in comparison to other models. For example, in Flathead River station, the determination coefficient was 0.91 for the best WANN model, while it was 0.65, 0.75, and 0.481 for the best ANN, ANFIS, and SRC models, and also in the Santa Clara River, amounts of this statistical criteria was 0.92 for the best WANN model, while it was 0.76, 0.78, and 0.39 for the best ANN, ANFIS, and SRC models, respectively. Also, the values of cumulative suspended sediment load computed by the best WANN model were closer to the observed data than the other models. In general, results indicated that the WANN model could satisfactorily mimic phenomenon, acceptably estimate cumulative SSL, and reasonably predict peak SSL values.

Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition.

Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for the effective reservoir management. In this research, an artificial neural network (ANN) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting medium and long-term runoff time series. First, the original runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and a residual series using EEMD technique for attaining deeper insight into the data characteristics. Then all IMF components and residue are predicted, respectively, through appropriate ANN models. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Two annual reservoir runoff time series from Biuliuhe and Mopanshan in China, are investigated using the developed model based on four performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and the proposed EEMD-ANN model can attain significant improvement over ANN approach in medium and long-term runoff time series forecasting.

A review on integration of artificial intelligence into water quality modelling.

With the development of computing technology, numerical models are often employed to simulate flow and water quality processes in coastal environments. However, the emphasis has conventionally been placed on algorithmic procedures to solve specific problems. These numerical models, being insufficiently user-friendly, lack knowledge transfers in model interpretation. This results in significant constraints on model uses and large gaps between model developers and practitioners. It is a difficult task for novice application users to select an appropriate numerical model. It is desirable to incorporate the existing heuristic knowledge about model manipulation and to furnish intelligent manipulation of calibration parameters. The advancement in artificial intelligence (AI) during the past decade rendered it possible to integrate the technologies into numerical modelling systems in order to bridge the gaps. The objective of this paper is to review the current state-of-the-art of the integration of AI into water quality modelling. Algorithms and methods studied include knowledge-based system, genetic algorithm, artificial neural network, and fuzzy inference system. These techniques can contribute to the integrated model in different aspects and may not be mutually exclusive to one another. Some future directions for further development and their potentials are explored and presented.