Pouyan PressJournal of Soft Computing in Civil Engineering2588-28726220220401Hourly Flood Forecasting Using Hybrid Wavelet-SVM12014750410.22115/scce.2022.317761.1383ENBaheerah ShadaPost Graduate Student, National Institute of Technology, Calicut, India0000-0002-0077-8854N.R. ChithraAssistant Professor, Department of Civil Engineering, National Institute of Technology Calicut, India0000-0003-2555-0263Santosh G.ThampiProfessor, Department of Civil Engineering, National Institute of Technology Calicut, India0000-0003-0638-0887Journal Article20211201The floods of 2018 and 2019 have underlined the urgent need for development and implementation of efficient and robust flood forecasting models for the major rivers in the State of Kerala, India. In this paper, the development and application of two hourly flood forecasting models are presented – one using Support Vector Machine (SVM) and the other based on hybrid wavelet-support vector machine (WSVM). The study was performed on the Achankovil River in Kerala. Wavelet technique was used to denoise the input signal (rainfall and water level) and the effective components of the input signal obtained after denoising were input to the SVM/ WSVM models for forecasting. These models' performance was assessed using standard performance rating criteria. Further, the performance of these models was compared with that of a flood forecasting model based on hybrid wavelet-artificial neural network (WANN) developed for this river in a previous study. Results of this study demonstrated the ability of the WSVM model to predict floods reasonably well. It was observed that the WSVM model performed better when compared to the WANN model. The WSVM model was able to accurately estimate peak discharge magnitude and time to peak, both of which are critical inputs in many water resource design and management applications.Pouyan PressJournal of Soft Computing in Civil Engineering2588-28726220220401A Study of Bank Line Shifting of the Selected Reach of Jamuna River Using Multi-Variant Regression Model213414752010.22115/scce.2022.319360.1387ENAkramul HaqueM.Sc. Student, Department of Water Resources Engineering, BUET, Bangladesh0000-0001-6584-9265Md. AbdulMatinProfessor, Department of Water Resources Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka-1000, BangladeshJournal Article20211211Jamuna river is a morphologically very dynamic river. It carries a vast sediment load from the erosive foothills of Himalaya mountain. The length of the Jamuna River is 220 km. For this research work Jamalpur district is selected to assess morphological changes using hydrodynamic, Artificial intelligence and google satellite images. First, the hydrodynamic model was calibrated and validated at Kazipur station for the years 2018 and 2019 respectively. Then, left overbank maximum discharge, water level, velocity, the slope was extracted from HEC-RAS 1D at 300 m interval interpolated cross-section. Then, this cross-section was exported as a shapefile. In google earth, the erosion rate was measured corresponding to this interpolated cross-section. The results of the hydrodynamic model were given as input variable and erosion rate as an output variable in Machine learning and deep learning technique. Calibration and validation of the regression model was done for the years 2018 and 2019 respectively. This research work can be helpful to locate the area which are vulnerable to bank erosion.Pouyan PressJournal of Soft Computing in Civil Engineering2588-28726220220401An Explicit Formulation for Estimation of Structural Number (SN) of Flexible Pavements in 1993 AASHTO Design Guide using Response Surface Methodology (RSM)355014750310.22115/scce.2022.306425.1372ENAli Reza GhanizadehAssociate Professor, Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran0000-0002-6618-1049Seyed Saber NaseralaviAssistant Professor, Department of Civil Engineering, Shahid Bahnar University of Kerman, Kerman, Iran0000-0002-5392-910XJournal Article20211010In the 1993 AASHTO flexible pavement design equation, the structural number (SN) cannot be calculated explicitly based on other input parameters. Therefore, in order to calculate the SN, it is necessary to approximate the relationship using the iterative approach or using the design chart. The use of design chart reduces the accuracy of calculations and, on the other hand, the iterative approach is not suitable for manual calculations. In this research, an explicit equation has been developed to calculate the SN in the 1993 AASHTO flexible pavement structural design guide based on response surface methodology (RSM). RSM is a collection of statistical and mathematical methods for building empirical models. Developed equation based on RMS makes it possible to calculate the SN of different flexible pavement layers accurately. The coefficient of determination of the equation proposed in this study for training and testing sets is 0.999 and error of this method for calculating the SN in most cases is less than 5%. In this study, sensitivity analysis was performed to determine the degree of importance of each independent parameter and parametric analysis was performed to determine the effect of each independent parameter on the SN. Sensitivity analysis shows that the log(W<sub>8.2</sub>) has the highest degree of importance and the Z<sub>R</sub> parameter has the lowest one.Pouyan PressJournal of Soft Computing in Civil Engineering2588-28726220220401Assessment of Statistical Models for Rainfall Forecasting Using Machine Learning Technique516714780810.22115/scce.2022.304260.1363ENL. GowriSchool of Computing, SASTRA Deemed to be University, Thanjavur, IndiaK.R. ManjulaSchool of Computing, SASTRA Deemed to be University, Thanjavur, IndiaK. SasirekaSchool of Civil Engineering, SASTRA Deemed to be University, Thanjavur, IndiaDurairaj DeepaSchool of Civil Engineering, SASTRA Deemed to be University, Thanjavur, IndiaJournal Article20210911As heavy rainfall can lead to several catastrophes; the prediction of rainfall is vital. The forecast encourages individuals to take appropriate steps and should be reasonable in the forecast. Agriculture is the most important factor in ensuring a person's survival. The most crucial aspect of agriculture is rainfall. Predicting rain has been a big issue in recent years. Rainfall forecasting raises people's awareness and allows them to plan ahead of time to preserve their crops from the elements. To predict rainfall, many methods have been developed. Instant comparisons between past weather forecasts and observations can be processed using machine learning. Weather models can better account for prediction flaws, such as overestimated rainfall, with the help of machine learning, and create more accurate predictions. Thanjavur Station rainfall data for the period of 17 years from 2000 to 2016 is used to study the accuracy of rainfall forecasting. To get the most accurate prediction model, three prediction models ARIMA (Auto-Regression Integrated with Moving Average Model), ETS (Error Trend Seasonality Model) and Holt-Winters (HW) were compared using R package. The findings show that the model of HW and ETS performs well compared to models of ARIMA. Performance criteria such as Akaike Information Criteria (AIC) and Root Mean Square Error (RMSE) have been used to identify the best forecasting model for Thanjavur station.Pouyan PressJournal of Soft Computing in Civil Engineering2588-28726220220401Providing SSPCO Algorithm to Construct Static Protein-Protein Interaction (PPI) Networks689114780610.22115/scce.2022.301262.1355ENElham AzarmM.Sc., Department of Computer Engineering, Shiraz Branch, Islamic Azad University, Shiraz Branch, IranTarlan Motamedi NiaM.Sc., Institute for Management and Planning Studies of Tehran, IranRohollah OmidvarPh.D., Department of Computer Engineering, Shiraz Branch, Islamic Azad University, Shiraz Branch, IranJournal Article20210824Protein-Protein Inter-action Networks are dynamic in reality; i.e. Inter-actions among different proteins may be ineffective in different circumstances and times. One of the most crucial parameters in the conversion of a static network into a temporal graph is the well-tuning of transformation threshold. In this part of the article, using additional data, like gene expression data in different times and circumstances and well-known protein complexes, it is tried to determine an appropriate threshold. To accomplish this task, we transform the problem into an optimization one and then we solve it using a meta-heuristic algorithm, named Particle Swarm Optimization (SSPCO). One of the most important parts in our work is the determination of interestingness function in the SSPCO. It is defined as a function of standard complexes and gene co-expression data. After producing a threshold per each gene, in the following section we will discuss how using these thresholds, active proteins are determined and then temporal graph is created. For final assessment of the produced graph quality, we use graph clustering algorithms and protein complexes determination algorithms. For accomplishing this task, we use MCL, Cluster One, MCODE algorithms. Due to high number of the obtained clusters, the obtained results, if they have some special conditions, will filter out or be merged with each other. Standard performance criteria like Recal, Precision, and F-measure are employed. There is a new proposed criterion named Smoothness. Our experimental results show that the graphs produced by the proposed method outperform the previous methods.Pouyan PressJournal of Soft Computing in Civil Engineering2588-28726220220401Use of ANN, C4.5 and Random Forest Algorithm in the Evaluation of Seismic Soil Liquefaction9210614780710.22115/scce.2022.314762.1380ENRavi BhushanBhardwajM.Tech Student, Department of Civil Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India0000-0003-0594-2737S.R. ChaurasiaProfessor, Department of Civil Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, IndiaJournal Article20211112Liquefaction is one of the disasters caused due to earthquake. In 1999, Chi-Chi, Taiwan, earthquake is an example of liquefaction prone disasters induced due to M<sub>w</sub> 7.6 earthquake. This becomes major cause for prediction of the liquefaction in the soil with respect to geotechnical property. In this paper, we have use Artificial Neural Networks (ANN) model based on Resilient Back propagation (Rprop), Decision tree model (DT) and classifier are C 4.5 and Random Forest is done for comparing the performance and evaluation of liquefaction potential based on the obtained field CPT data (Juang et al.,2002) consisting 125 datasets over the simplified procedures that are being traditionally use for the classification of liquefaction of the soil by different researchers. It is observe that Resilient Back propagation Algorithm prediction is 100% whereas C 4.5 algorithm and Random forest Algorithm are 97.6% and 98.4% accurate for the evaluation of seismic soil liquefaction potential.Pouyan PressJournal of Soft Computing in Civil Engineering2588-28726220220401A Grey-Fuzzy Based Approach for the Optimization of Corrosion Resistance of Rebars Coated with Ternary Electroless Nickel Coatings10712715084410.22115/scce.2022.326903.1401ENArkadeb MukhopadhyayAssistant Professor, Department of Mechanical Engineering, Birla Institute of Technology, Mesra, Ranchi – 835215, India0000-0002-4942-722XSarmila SahooAssociate Professor, Department of Civil Engineering, Heritage Institute of Technology, Kolkata – 700107, India0000-0001-7000-3758Journal Article20220128Corrosion is an important phenomenon that occurs at concrete-rebar interface and affects the life of structures in coastal environments. Fe-600 grade steel is used in India for construction purposes especially in seismic zones. Hence, the corrosion of the rebars and its optimization is necessary to increase the lifetime of structures. In this regard, the present investigation examines the applicability of electroless Ni-P based ternary coatings as candidates for corrosion protection and obtains an optimal bath formulation. Investigation of electrochemical corrosion phenomenon (potentiodynamic polarization) was carried out in 3.5% NaCl to simulate saline coastal environment. Ni-P coatings with Cu and W inclusion were considered due to their proven corrosion resistance. The bath constituents such as nickel sulphate (Ni source), sodium hypophosphite (reducing agent and source of P) and the tungsten / copper concentration were varied to get various elemental composition following a sequential experimental design i.e. Taguchi’s L<sub>9</sub> orthogonal array. A grey based fuzzy reasoning approach was proposed to optimize the bath and achieve enhanced corrosion resistance. The optimized coatings exhibited initiation of passivation which could prove to be beneficial for the health of the structure in the long-run. A noble corrosion potential and lower corrosion current density could be obtained in the coated rebars from the grey fuzzy methodology.