Multi-Expression Programming (MEP): Water Quality Assessment Using Water Quality Indices

Aldrees, Ali; Khan, Mohsin Ali; Tariq, Muhammad Atiq Ur Rehman; Mohamed, Abdeliazim Mustafa; Ng, A. W. M; Taha, Abubakr Taha Bakheit

doi:10.3390/w14060947

Cited by 21 publications

(6 citation statements)

References 79 publications

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“…The algorithm of MEP begins by creating computer programs of a random population. To construct the best computer program, MEP continue to follow the below-mentioned steps until it achieves the termination condition [52,104]:…”

Section: Overview Of Soft-computing Approachesmentioning

confidence: 99%

Prediction of Marshall Stability and Marshall Flow of Asphalt Pavements Using Supervised Machine Learning Algorithms

et al. 2022

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The conventional method for determining the Marshall Stability (MS) and Marshall Flow (MF) of asphalt pavements entails laborious, time-consuming, and expensive laboratory procedures. In order to develop new and advanced prediction models for MS and MF of asphalt pavements the current study applied three soft computing techniques: Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multi Expression Programming (MEP). A comprehensive database of 343 data points was established for both MS and MF. The nine most significant and straightforwardly determinable geotechnical factors were chosen as the predictor variables. The root squared error (RSE), Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error (RRMSE), coefficient of determination (R2), and correlation coefficient (R), were all used to evaluate the performance of models. The sensitivity analysis (SA) revealed the rising order of input significance of MS and MF. The results of parametric analysis (PA) were also found to be consistent with previous research findings. The findings of the comparison showed that ANN, ANFIS, and MEP are all reliable and effective methods for the estimation of MS and MF. The mathematical expressions derived from MEP represent the novelty of MEP and are relatively reliable and simple. Roverall values for MS and MF were in the order of MEP > ANFIS > ANN with all values over the permissible range of 0.80 for both MS and MF. Therefore, all the techniques showed higher performance, possessed high prediction and generalization capabilities, and assessed the relative significance of input parameters in the prediction of MS and MF. In terms of training, testing, and validation data sets and their closeness to the ideal fit, i.e., the slope of 1:1, MEP models outperformed the other two models. The findings of this study will contribute to the choice of an appropriate artificial intelligence strategy to quickly and precisely estimate the Marshall Parameters. Hence, the findings of this research study would assist in safer, faster, and more sustainable predictions of MS and MF, from the standpoint of time and resources required to perform the Marshall tests.

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Section: Overview Of Soft-computing Approachesmentioning

confidence: 99%

Prediction of Marshall Stability and Marshall Flow of Asphalt Pavements Using Supervised Machine Learning Algorithms

et al. 2022

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“…The preservation of water quality is a major issue for the sustainable management of the environment, but also for that of biodiversity [1,2]. Indeed, aquatic ecosystems are greatly threatened because of their vulnerability due to strong and increasing anthropogenic pressures [3,4].…”

Section: Introductionmentioning

confidence: 99%

Benthic Macroinvertebrate Communities as Indicator of the Water Quality of a Suburban Stream in the Littoral Region of Cameroon

Nyamsi Tchatcho,

Nana,

Koji

et al. 2024

Pollutants

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As bioindicators, benthic macroinvertebrates are often used to assess stream quality. Based on standard hydrobiological study techniques, the physicochemical and biological health status of the Missolé stream was assessed. Waters of the Missolé stream were found to be slightly acidic (pH: 6.23–6.26) and well-oxygenated (O2: 69.80–76.80%), with low values of temperature (T°: 23.60–24° C), turbidity (49.40–88.40 FTU) and mineralized ions (NH4+: 0–1.19 mg/L; NO2-: 0–1.61 mg/L; NO3-: 0.02–6.80 mg/L). Concerning aquatic invertebrate communities, a total of 489 individuals, grouped in two classes, eight orders and 35 families, all belonging to the phylum Arthropoda, were collected and identified. The class of Insecta was the most diversified, with seven orders and 32 families, while that of Crustacea had only one order and three families. Overall, Insecta accounted for 52.35% of the total abundance, and Decapod Crustacea was 47.65%. The three predominant families were Palaemonidae, Dytiscidae and Atyidae. Shannon and Weaver (H’) and Piélou’s evenness (J) indices were high at all stations and showed a slight decrease from upstream to downstream. In the same vein, the Hilsenhoff Biotic Index (HBI) classified the water quality of the Missolé stream as medium. Overall, this suburban aquatic ecosystem offers moderately favorable living conditions for aquatic biota.

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“…Considering new advances in artificial intelligence (AI) technology, its applications have grown significantly in numerous industrial domains [13][14][15][16][17]. Some other techniques used in the field of material engineering provide precise formulations for strength prediction; however, the accuracy is compromised [18][19][20][21][22]. The accuracy of the developed model depends on the optimization parameters, the number of input variables, and the number of entries being used while modeling [23,24].…”

Section: Introductionmentioning

confidence: 99%

Control of Static and Dynamic Parameters by Fuzzy Controller to Optimize Friction Stir Spot Welding Strength

et al. 2022

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Solid-state welding is a derivative of the friction stir spot welding (FSSW) technique, which has been developed as a new method for joining aluminum alloys. FSSW is a variant of linear friction stir welding intended to deal with lightweight alloy resistance spot welding (RSW) and riveting. Tensile strength refers to a material’s ability to withstand excessive stress when being stretched or pulled before necking; it is expressed in terms of force per unit area. The tensile strength in stir spot welding is affected by dynamic and static parameters. The control of dynamic parameters and static parameters is studied in this paper to optimize the friction stir spot welding strength. A fuzzy logic control system is used to optimize the process as a new approach that can be used in this field. The obtained results prove that the fuzzy logic control system is an easy and inexpensive technology that can be used in prediction and optimization for the strength of FSSW. Furthermore, the results show the efficacy and adequacy of the proposed fuzzy logic control system.

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Multi-Expression Programming (MEP): Water Quality Assessment Using Water Quality Indices

Cited by 21 publications

References 79 publications

Prediction of Marshall Stability and Marshall Flow of Asphalt Pavements Using Supervised Machine Learning Algorithms

Prediction of Marshall Stability and Marshall Flow of Asphalt Pavements Using Supervised Machine Learning Algorithms

Benthic Macroinvertebrate Communities as Indicator of the Water Quality of a Suburban Stream in the Littoral Region of Cameroon

Control of Static and Dynamic Parameters by Fuzzy Controller to Optimize Friction Stir Spot Welding Strength

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