Enhanced soft computing for ensemble approach to estimate the compressive strength of high strength concrete

High strength concrete (HCS) define as the concrete that meets unique mixture of performance uniformity requirements that cannot be reached routinely using conventional constituents and regular mixing, placing, and curing events. The modeling of such type of concrete is very difficult. In this investigation, the performance of the gaussian process (GP) regression, support vector Machine (SVM) and artificial neural network (ANN) were compared to estimate the 28th day compressive strength of the HSC. Total data set consists of 83 data out of which 70 % of total dataset used to train the model and residual 30% used to test the models. The model accuracy was depend upon the five performance evaluation parameter which were correlation coefficient (R), Bias, mean square error (MAE), root mean square error (RMSE) and Nash-Sutcliffe model efficiency (E). The results recommend that ANN model is more accurate to predict the compressive strength as compare to GP and SVM based models. Sensitivity analysis indicated that Cement (C), Silica fume (SF), Fly ash (FA) and Water (W) are the most valuable constituents in which compressive strength of the HCS is mainly depend for this data set

Estimation of compressive strength of high-strength concrete by random forest and M5P model tree approaches

High strength concrete (HSC) define as the concrete that meets a unique mixture of performance uniformity requirements that cannot be reached routinely using conventional constituents and regular mixing, placing, and curing events. The modeling of such type of concrete is very difficult. In this investigation, the performance of the random forest regression and M5P model tree were compared to estimate the 28th day compressive strength of the HSC. Total data set consists of 83 data out of which 70 % of the total dataset used to train the model and residual 30 % used to test the models. The accuracy of the models was depending upon the three performance evaluation parameters which are correlation coefficient (R), root mean square error (RMSE) and maximum absolute error (MAE). The results recommend that random forest regression is more accurate to predict the compressive strength as compare to M5P model tree. Sensitivity analysis indicates that water (W) and Silica fumes (SF) are the most valuable constituents of the HSC and compressive strength mainly depends on these constituents

Estimation of compressive strength of high-strength concrete by random forest and M5P model tree approaches

High strength concrete (HSC) define as the concrete that meets a unique mixture of performance uniformity requirements that cannot be reached routinely using conventional constituents and regular mixing, placing, and curing events. The modeling of such type of concrete is very difficult. In this investigation, the performance of the random forest regression and M5P model tree were compared to estimate the 28th day compressive strength of the HSC. Total data set consists of 83 data out of which 70 % of the total dataset used to train the model and residual 30 % used to test the models. The accuracy of the models was depending upon the three performance evaluation parameters which are correlation coefficient (R), root mean square error (RMSE) and maximum absolute error (MAE). The results recommend that random forest regression is more accurate to predict the compressive strength as compare to M5P model tree. Sensitivity analysis indicates that water (W) and Silica fumes (SF) are the most valuable constituents of the HSC and compressive strength mainly depends on these constituents

Analysis of sentinel-1 data for regional crop classification: a multi-data approach for rabi crops of district Hisar (Haryana)

Generation of spatio-temporal information such as land use system and management practices is one of the key ingredients for carrying out the regional level agro-ecosystem modelling. However, at the regional level availability of such data is scarce, where analysis of a cropping system is essential and a pre-requisite for studying the overall sustainability of the agricultural production system. The present investigation was carried out to identify the actually practised cropping pattern and their mapping in Hisar district of Haryana (India) using Multi-Data Approach (MDA). Multi-date sentinel-1 for the rabi season of 2019 was classified using multi-phase unsupervised classification approach and classes of interest were identified. Finally, classified images were subjected to logical combinations which helped in generating crop classification maps and statistics. Results showed that cropping pattern of the district exhibited huge variations and area under wheat was observed to be highest (204.76 thousand ha) in comparison to mustard crop (64.42 thousand ha) and least was under the sugarcane crop (0.97 thousand ha). Some other crops like vegetables and horticultural crops were also identified during this period, but the major crops that were identified during rabi 2019 were wheat and mustard. Hence, regional crop classification using sentinel-1 data appears to be a valuable tool for predicting a specific regions cropping pattern, which is considered to be the most significant aspect of an agricultural production system

Enhanced soft computing for ensemble approach to estimate the compressive strength of high strength concrete

High strength concrete (HCS) define as the concrete that meets unique mixture of performance uniformity requirements that cannot be reached routinely using conventional constituents and regular mixing, placing, and curing events. The modeling of such type of concrete is very difficult. In this investigation, the performance of the gaussian process (GP) regression, support vector Machine (SVM) and artificial neural network (ANN) were compared to estimate the 28th day compressive strength of the HSC. Total data set consists of 83 data out of which 70 % of total dataset used to train the model and residual 30% used to test the models. The model accuracy was depend upon the five performance evaluation parameter which were correlation coefficient (R), Bias, mean square error (MAE), root mean square error (RMSE) and Nash-Sutcliffe model efficiency (E). The results recommend that ANN model is more accurate to predict the compressive strength as compare to GP and SVM based models. Sensitivity analysis indicated that Cement (C), Silica fume (SF), Fly ash (FA) and Water (W) are the most valuable constituents in which compressive strength of the HCS is mainly depend for this data set

Prediction of daily water level using new hybridized GS-GMDH and ANFIS-FCM models

Accurate prediction of water level (WL) is essential for the optimal management of different water resource projects. The development of a reliable model for WL prediction remains a challenging task in water resources management. In this study, novel hybrid models, namely, Generalized Structure�Group Method of Data Handling (GS-GMDH) and Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM) were proposed to predict the daily WL at Telom and Bertam stations located in Cameron Highlands of Malaysia. Different percentage ratio for data division i.e. 50%–50% (scenario�1), 60%–40% (scenario-2), and 70%–30% (scenario-3) were adopted for training and testing of these models. To show the efficiency of the proposed hybrid models, their results were compared with the standalone models that include the Gene Expression Programming (GEP) and Group Method of Data Handling (GMDH). The results of the investigation revealed that the hybrid GS-GMDH and ANFIS-FCM models outperformed the standalone GEP and GMDH models for the prediction of daily WL at both study sites. In addition, the results indicate the best performance for WL prediction was obtained in scenario-3 (70%–30%). In summary, the results highlight the better suitability and supremacy of the proposed hybrid GS-GMDH and ANFIS-FCM models in daily WL prediction, and can, serve as robust and reliable predictive tools for the study regio

Assessment of infiltration models developed using soft computing techniques

In this study, predicting ability of support vector machines (SVM), Gaussian process (GP), artificial neural network (ANN), and Random forests (RF) based regression approaches was tested on the infiltration data of soil samples having different compositions of sand, silt, clay, and fly ash. In addition to this, their performances were compared with the Kostiakov model (KM) and Philip’s model (PM). Dataset containing a total of 392 observations was collected from the experimental measurements of soil infiltration rate on different soil samples. Out of the total dataset, 272 recordings were randomly selected for training and the residual 120 observations were selected for validation of the developed models. Standard statistical parameters were used to measure the predicting ability of various developed models. The result suggests that the best performance could be achieved by Polynomial kernel function-based GP regression (GP_Poly) with coefficient of correlation values as 0.9824, 0.9863, Bias values as 0.0006, −2.3542, root-mean-square error values as 47.7336, 40.3026, and Nash Sutcliffe model efficiency values as 0.9655, 0.9727 using training and testing dataset, respectively. Furthermore, time is found as the most influencing input variable for predicting the infiltration rate when GP_Poly-based model is used to predict the infiltration rate

Estimation and inter-comparison of infiltration models

Infiltration models are very helpful in designing and evaluating surface irrigation systems. The main objective of the present work is estimation and inter-comparison of infiltration models which are used to evaluate the infiltration rates of National Institute of Technology (NIT)-campus in district of Kurukshetra, Haryana (India) and for this study, field infiltration tests were carried out at ten different locations comprising of 109 observations by use of double ring infiltrometer. The potential of three infiltration models (Kostiakov, Modified Kostiakov and US- Soil Conservation Service (SCS)) were evaluated by least–square fitting to observed infiltration data. Three statistical comparison criteria including maximum absolute error (MAE), Bias and root mean square error (RMSE) were used to determine the best performing infiltration models. In addition, a novel infiltration model was developed from field tests data using nonlinear regression modeling which suggests improved performance out of other three models. In case of nonexistence of observed infiltration data, this novel model can be used to artificially generate infiltration data for NIT campus

Comparison of infiltration models in NIT Kurukshetra campus

Abstract The aim of the present investigation is to evaluate the performance of infiltration models used to calculate the infiltration rate of the soils. Ten different locations were chosen to measure the infiltration rate in NIT Kurukshetra. The instrument used for the experimentation was double ring infiltrometer. Some of the popular infiltration models like Horton’s, Philip’s, Modified Philip’s and Green–Ampt were fitted with infiltration test data and performance of the models was determined using Nash–Sutcliffe efficiency (NSE), coefficient of correlation (C.C) and Root mean square error (RMSE) criteria. The result suggests that Modified Philip’s model is the most accurate model where values of C.C, NSE and RMSE vary from 0.9947–0.9999, 0.9877–0.9998 to 0.1402–0.6913 (mm/h), respectively. Thus, this model can be used to synthetically produce infiltration data in the absence of infiltration data under the same conditions

Modelling of the impact of water quality on the infiltration rate of the soil

Abstract The concept behind of this paper is to check the potential of the three regression-based techniques, i.e. M5P tree, support vector machine (SVM) and Gaussian process (GP), to estimate the infiltration rate of the soil and to compare with two empirical models, i.e. Kostiakov model and multi-linear regression (MLR). Totally, 132 observations were obtained from the laboratory experiments, out of which 92 observations were used for training and residual 40 for testing the models. A double-ring infiltrometer was used for experimentation with different concentrations (1%, 5%, 10% and 15%) of impurities and different types of water quality (ash and organic manure). Cumulative time (T f), type of impurities (I t), concentration of impurities (C i) and moisture content (W c) were the input variables, whereas infiltration rate was considered as target. For SVM and GP regression, two kernel functions (radial based kernel and Pearson VII kernel function) were used. The results from this investigation suggest that M5P tree technique is more precise as compared to the GP, SVR, MLR approach and Kostiakov model. Among GP, SVR, MLR approach and Kostiakov model, MLR is more accurate for estimating the infiltration rate of the soil. Thus, M5P tree is a technique which could be used for modelling the infiltration rate for the given data set. Sensitivity analyses suggest that the cumulative time (T f) is the major influencing parameter on which infiltration rate of the soil depends