Siraj Pandhiani
JUBAIL UNIVERSITY COLLEGE, General Studies, Faculty Member
- Artificial Intelligence, Civil Engineering, Machine Learning, Informatics, Computational Fluid Dynamics, Rainfall-Runoff modelling, and 8 morePrincipal Component Analysis, Hydrologic Modeling, Hydrologic Modelling, Water Resources (Environment), SVM classifier, Agricultural Water Management, Water Resources engineering, and Hydrologyedit
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The natural streamflow of the River is encouraged to forecast through multiple methods. The impartiality of this study is the comparison of the forecast accuracy rates of the time-series (TS) hybrid model with the conventional model. The... more
The natural streamflow of the River is encouraged to forecast through multiple methods. The impartiality of this study is the comparison of the forecast accuracy rates of the time-series (TS) hybrid model with the conventional model. The behavior of the natural monthly statistical chaotic streamflow to use in the forecasting models has been compiled by projecting two distinguished rivers, the Indus and Chenab of Pakistan. Therefore, this article is based on the monthly streamflow forecast analysis that has been reported using the group method of data handling with wavelet decomposition (WGMDH) as a new forecasting attribute. Discrete wavelets decompose the perceived data into sub-series and forecast hydrological variables; these fittingly have been endorsed as inputs in the hybrid model. The forecast efficiency and estimations of the hybrid model are measured by the appropriate statistical techniques such as mean absolute error (RME), root mean square error (RMSE), and correlation c...
The natural streamflow of the River is encouraged to forecast through multiple methods. The impartiality of this study is the comparison of the forecast accuracy rates of the time-series (TS) hybrid model with the conventional model. The... more
The natural streamflow of the River is encouraged to forecast through multiple methods. The impartiality of this study is the comparison of the forecast accuracy rates of the time-series (TS) hybrid model with the conventional model. The behavior of the natural monthly statistical chaotic streamflow to use in the forecasting models has been compiled by projecting two distinguished rivers, the Indus and Chenab of Pakistan. Therefore, this article is based on the monthly streamflow forecast analysis that has been reported using the group method of data handling with wavelet decomposition (WGMDH) as a new forecasting attribute. Discrete wavelets decompose the perceived data into sub-series and forecast hydrological variables; these fittingly have been endorsed as inputs in the hybrid model. The forecast efficiency and estimations of the hybrid model are measured by the appropriate statistical techniques such as mean absolute error (RME), root mean square error (RMSE), and correlation c...
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Over the years, many organizations across the globe have conducted various studies pertaining to air pollution and its ill effects. The results of these studies substantially conclude that a plethora of people succumbs to the adversities... more
Over the years, many organizations across the globe have conducted various studies pertaining to air pollution and its ill effects. The results of these studies substantially conclude that a plethora of people succumbs to the adversities caused by the ever-increasing air pollutants. In this investigation, M5P, random forest (RF)- and Gaussian process (GP)-based approaches are used to predict the tropospheric ozone for Amritsar, Punjab state of India, metropolitan area. The models proposed were based on ten input parameters viz. particulate matter PM2.5, particulate matter PM10, sulphur dioxide (SO2), nitrogen dioxide (NO2), nitric oxide (NO), ammonia (NH3), temperature (T), solar radiation (SR), wind direction (WD) and wind speed (WS), while the tropospheric ozone (O3) was an output parameter. Three most popular statistical parameters such as correlation coefficient (CC), mean absolute error (MAE) and root mean square error (RMSE) were used for the assessment of the developed models. In comparison, it was found that better results were achieved with random forest-based model with CC value as 0.8850, MAE value as 0.0593 and RMSE value as 0.0772 for testing stage. The suggested models are expected to save cost of instrument, cost of labour work, time and contribute to greater accuracy. A result of sensitivity investigation concludes that the solar radiation is the most influencing parameter in estimating the actual values of O3 based on the current data set.
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All existing methods regarding time series forecasting have always been challenged by the continuous climatic change taking place in the world. These climatic changes influence many unpredictable indefinite factors. This alarming... more
All existing methods regarding time series forecasting have always been challenged by the continuous climatic change taking place in the world. These climatic changes influence many unpredictable indefinite factors. This alarming situation requires a robust forecasting method that could efficiently work with incomplete and multivariate data. Most of the existing methods tend to trap into local minimum or encounter over fitting problems that mostly lead to an inappropriate outcome. The complexity of data regarding time series forecasting does not allow any one single method to yield results suitable in all situations as claimed by most researchers. To deal with the problem, a technique that uses hybrid models has also been devised and tested. The applied hybrid methods did bring some improvement compared to the individual model performance. However, most of these available hybrid models exploit univariate data that requires huge historical data to achieve precise forecasting results....
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Fiber-reinforced plastic (FRP) rebars can be the futuristic potential reinforcing material in place of mild steel (MS) rebars which are highly prone to corrosion. However, the bond properties of the FRP rebars are not consistent with... more
Fiber-reinforced plastic (FRP) rebars can be the futuristic potential reinforcing material in place of mild steel (MS) rebars which are highly prone to corrosion. However, the bond properties of the FRP rebars are not consistent with those of mild steel rebars. Therefore, determination of bond strength properties of FRP rebars becomes essential. In this study, an investigation was conducted on 222 samples for bond strength data set for FRP rebars using various soft computing techniques such as multilinear regression, random forests, random tree, M5P, bagged-M5P tree, stochastic-M5P, and Gaussian process. Outcomes of accuracy assessment parameters, i.e., CC, MAE, and RMSE, suggest that bagged-M5P tree-based model is outperforming than other developed models CC, MAE, and RMSE whose values are 0.9530, 0.8970, and 1.2531, respectively, for testing stages. On assessing the data and the results, it was found that GP_PUK model is more appropriate than GP_RBF-based model for predicting the bond strength of FRP (MPa). On comparison of the RF and RT models, it was concluded that RF-based model performs better than RT models with CC, MAE, and RMSE values of 0.9427, 0.8674, and 1.3424, respectively, for testing stages. The results of the study also suggest that bagged-M5P model attains higher correlation with lesser RMSE values. Taylor diagram also verifies that bagged-M5P model performs better than other developed models. Sensitivity analysis suggests that bar embedment length to bar diameter (l/d) is the most influencing parameter for the prediction of bond strength of FRP.
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AbstractA reliable and continuous streamflow simulation capability is essential for systematic management of water resource systems. Thus, predicting streamflow is important for water management an...
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The aim of the present study was to explore the correlation between the land-use/land cover change and the flash-flood potential changes in Zăbala catchment (Romania) between 1989 and 2019. In this regard, the efficiency of GIS, remote... more
The aim of the present study was to explore the correlation between the land-use/land cover change and the flash-flood potential changes in Zăbala catchment (Romania) between 1989 and 2019. In this regard, the efficiency of GIS, remote sensing and machine learning techniques in detecting spatial patterns of the relationship between the two variables was tested. The paper elaborated upon an answer to the increase in flash flooding frequency across the study area and across the earth due to the occurred land-use/land-cover changes, as well as due to the present climate change, which determined the multiplication of extreme meteorological phenomena. In order to reach the above-mentioned purpose, two land-uses/land-covers (for 1989 and 2019) were obtained using Landsat image processing and were included in a relative evolution indicator (total relative difference-synthetic dynamic land-use index), aggregated at a grid-cell level of 1 km2. The assessment of runoff potential was made with...
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Particulate matter has a detrimental consequence on the health of living organisms throughout the world and predicting their concentration is very imperative to assess their impact on human health. Faridabad is the most populated and... more
Particulate matter has a detrimental consequence on the health of living organisms throughout the world and predicting their concentration is very imperative to assess their impact on human health. Faridabad is the most populated and largest city of Haryana, India, and the current study was designed to foresee the PM2.5 content by different modeling techniques: (1) support vector machine (SVM), (ii) random forest (RF), (iii) artificial neural network (ANN), (iv) M5P model, and (v) Gaussian process regression (GP). Collected data (659 observations) from May 2015 to May 2018 were used to develop the models. Parameters such as temperature (T), ground-level ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), nitric oxide (NO), NOx, wind speed (WS), wind direction (WD), relative humidity (RH), bar pressure (BP), and solar radiation (SR) are used as input parameters for prediction of PM2.5. The results of all the models suggested that RF model with testing correlation coefficient (CC) 0.8312, mean absolute error (MAE) 30.7757, R2 (correlation of determination) 0.6909, and root mean square error (RMSE) 44.6947 is the best estimator for appraisal of PM2.5 followed by SVM, GP, M5P, and ANN models. The sensitivity analysis results indicated that wind speed is the utmost influencing parameter for the estimation of PM2.5. Abilities of different models were compared and RF was established as the best technique based on assessment criteria. We recommend more studies employing RF and other techniques as hybrids that lead to better models.
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In this study, new hybrid model is developed by integrating two models, the discrete wavelet transform and least square support vector machine (WLSSVM) model. The hybrid model is then used to measure for monthly stream flow forecasting... more
In this study, new hybrid model is developed by integrating two models, the discrete wavelet transform and least square support vector machine (WLSSVM) model. The hybrid model is then used to measure for monthly stream flow forecasting for two major rivers in Pakistan. The monthly stream flow forecasting results are obtained by applying this model individually to forecast the rivers flow data of the Indus River and Neelum Rivers. The root mean square error (RMSE), mean absolute error (MAE) and the correlation (R) statistics are used for evaluating the accuracy of the WLSSVM, the proposed model. The results are compared with the results obtained through LSSVM. The outcome of such comparison shows that WLSSVM model is more accurate and efficient than LSSVM.
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The ability of obtain accurate information on future river flow is a fundamental key for water resources planning, and management. Traditionally, single models have been introduced to predict the future value of river flow. This paper... more
The ability of obtain accurate information on future river flow is a fundamental key for water resources planning, and management. Traditionally, single models have been introduced to predict the future value of river flow. This paper investigates the ability of Principal Component Analysis as dimensionality reduction technique and combined with single Support Vector Machine and Least Square Support Vector Machine, referred to as PCA-SVM and PCA-LSSVM. This study also presents comparison between the proposed models with single models of SVM and LSSVM. These models are ranked based on four statistical measures namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Correlation Coefficient ( ), and Correlation of Efficiency (CE). The results shows that PCA combined with LSSVM has better performance compared to other models. The best ranked models are then measured using Mean of Forecasting Error (MFE) to determine its forecast rate. PCA-LSSVM proven to be better model as it a...
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This study aims to propose a hydrological model for estimating the future value for monthly river flow. The proposed model was constructed by combining three components: i.e. Discrete Wavelet Transform (DWT), Principal Component Analysis... more
This study aims to propose a hydrological model for estimating the future value for monthly river flow. The proposed model was constructed by combining three components: i.e. Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA) and Least Square Support Vector Machine (LSSVM). The first two components, i.e. the wavelets and the PCA, are meant for preparing input data. Wavelets were employed to obtain a certain level of data decomposition, and in this case, a three level decomposition was employed. The output from the wavelets was given to PCA. This component simply picks up the important components from the given data, i.e. it addresses the issues relating to the dimensionality of the data. For approximating the desired value, LSSVM was employed for training, using the data derived from Wavelets and PCA models. For testing stability and reliability of the proposed model monthly data from two Pakistani rivers was collected. The reliability was measured by employing wel...