Browsing by Author "Nadhir Al-Ansari"

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A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration
(Nature Research, 2024) Bhupendra Joshi; Vijay Kumar Singh; Dinesh Kumar Vishwakarma; Mohammad Ali Ghorbani; Sungwon Kim; Shivam Gupta; V.K. Chandola; Jitendra Rajput; Il-Moon Chung; Krishna Kumar Yadav; Ehsan Mirzania; Nadhir Al-Ansari; Mohamed A. Mattar
Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott’s index of agreement (WI), and Legates–McCabe’s index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships. © The Author(s) 2024.
An Integrated Statistical-Machine Learning Approach for Runoff Prediction
(MDPI, 2022) Abhinav Kumar Singh; Pankaj Kumar; Rawshan Ali; Nadhir Al-Ansari; Dinesh Kumar Vishwakarma; Kuldeep Singh Kushwaha; Kanhu Charan Panda; Atish Sagar; Ehsan Mirzania; Ahmed Elbeltagi; Alban Kuriqi; Salim Heddam
Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall–runoff (R-R) modeling is an appropriate approach for runoff prediction, making it possible to take preventive measures to avoid damage caused by natural hazards such as floods. In the present study, several data-driven models, namely, multiple linear regression (MLR), multiple adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), were used for rainfall–runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The rainfall–runoff model analysis was conducted using daily rainfall and runoff data for 12 years (2009 to 2020) of the Gola watershed. The first 80% of the complete data was used to train the model, and the remaining 20% was used for the testing period. The performance of the models was evaluated based on the coefficient of determination (R2), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBAIS) indices. In addition to the numerical comparison, the models were evaluated. Their performances were evaluated based on graphical plotting, i.e., time-series line diagram, scatter plot, violin plot, relative error plot, and Taylor diagram (TD). The comparison results revealed that the four heuristic methods gave higher accuracy than the MLR model. Among the machine learning models, the RF (RMSE (m3/s), R2, NSE, and PBIAS (%) = 6.31, 0.96, 0.94, and −0.20 during the training period, respectively, and 5.53, 0.95, 0.92, and −0.20 during the testing period, respectively) surpassed the MARS, SVM, and the MLR models in forecasting daily runoff for all cases studied. The RF model outperformed in all four models’ training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for the runoff prediction of the Gola watershed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Long-term application of agronomic management strategies effects on soil organic carbon, energy budgeting, and carbon footprint under rice–wheat cropping system
(Nature Research, 2024) R.K. Naresh; P.K. Singh; Rajan Bhatt; Mandapelli Sharath Chandra; Yogesh Kumar; N.C. Mahajan; S.K. Gupta; Nadhir Al-Ansari; Mohamed A. Mattar
In the plains of western North India, traditional rice and wheat cropping systems (RWCS) consume a significant amount of energy and carbon. In order to assess the long-term energy budgets, ecological footprint, and greenhouse gas (GHG) pollutants from RWCS with residual management techniques, field research was conducted which consisted of fourteen treatments that combined various tillage techniques, fertilization methods, and whether or not straw return was present in randomized block design. By altering the formation of aggregates and the distribution of carbon within them, tillage techniques can affect the dynamics of organic carbon in soil and soil microbial activity. The stability of large macro-aggregates (> 2 mm), small macro-aggregates (2.0–2.25 mm), and micro-aggregates in the topsoil were improved by 35.18%, 33.52%, and 25.10%, respectively, over conventional tillage (0–20 cm) using tillage strategies for conservation methods (no-till in conjunction with straw return and organic fertilizers). The subsoil (20–40 cm) displayed the same pattern. In contrast to conventional tilling with no straw returns, macro-aggregates of all sizes and micro-aggregates increased by 24.52%, 28.48%, and 18.12%, respectively, when conservation tillage with organic and chemical fertilizers was used. The straw return (aggregate-associated C) also resulted in a significant increase in aggregate-associated carbon. When zero tillage was paired with straw return, chemical, and organic fertilizers, the topsoil's overall aggregate-associated C across all aggregate proportions increased. Conversely, conventional tillage, in contrast to conservation tillage, included straw return as well as chemical and organic fertilizers and had high aggregate-associated C in the subsurface. This study finds that tillage techniques could change the dynamics of microbial biomass in soils and organic soil carbon by altering the aggregate and distribution of C therein. © 2024, The Author(s).
Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms
(Springer Science and Business Media Deutschland GmbH, 2023) Deepak Kumar; Vijay Kumar Singh; Salwan Ali Abed; Vinod Kumar Tripathi; Shivam Gupta; Nadhir Al-Ansari; Dinesh Kumar Vishwakarma; Ahmed Z. Dewidar; Ahmed A. Al‑Othman; Mohamed A. Mattar
The present research work focused on predicting the electrical conductivity (EC) of surface water in the Upper Ganga basin using four machine learning algorithms: multilayer perceptron (MLP), co-adaptive neuro-fuzzy inference system (CANFIS), random forest (RF), and decision tree (DT). The study also utilized the gamma test for selecting appropriate input and output combinations. The results of the gamma test revealed that total hardness (TH), magnesium (Mg), and chloride (Cl) parameters were suitable input variables for EC prediction. The performance of the models was evaluated using statistical indices such as Percent Bias (PBIAS), correlation coefficient (R), Willmott’s index of agreement (WI), Index of Agreement (PI), root mean square error (RMSE) and Legate-McCabe Index (LMI). Comparing the results of the EC models using these statistical indices, it was observed that the RF model outperformed the other algorithms. During the training period, the RF algorithm has a small positive bias (PBIAS = 0.11) and achieves a high correlation with the observed values (R = 0.956). Additionally, it shows a low RMSE value (360.42), a relatively good coefficient of efficiency (CE = 0.932), PI (0.083), WI (0.908) and LMI (0.083). However, during the testing period, the algorithm’s performance shows a small negative bias (PBIAS = − 0.46) and a good correlation (R = 0.929). The RMSE value decreases significantly (26.57), indicating better accuracy, the coefficient of efficiency remains high (CE = 0.915), PI (0.033), WI (0.965) and LMI (− 0.028). Similarly, the performance of the RF algorithm during the training and testing periods in Prayagraj. During the training period, the RF algorithm shows a PBIAS of 0.50, indicating a small positive bias. It achieves an RMSE of 368.3, R of 0.909, CE of 0.872, PI of 0.015, WI of 0.921, and LMI of 0.083. During the testing period, the RF algorithm demonstrates a slight negative bias with a PBIAS of − 0.06. The RMSE reduces significantly to 24.1, indicating improved accuracy. The algorithm maintains a high correlation (R = 0.903) and a good coefficient of efficiency (CE = 0.878). The index of agreement (PI) increases to 0.035, suggesting a better fit. The WI is 0.960, indicating high accuracy compared to the mean value, while the LMI decreases slightly to − 0.038. Based on the comparative results of the machine learning algorithms, it was concluded that RF performed better than DT, CANFIS, and MLP. The study recommended using the current month’s total hardness (TH), magnesium (Mg), and chloride (Cl) parameters as input variables for multi-ahead forecasting of electrical conductivity (ECt+1, ECt+2, and ECt+3) in future studies in the Upper Ganga basin. The findings also indicated that RF and DT models had superior performance compared to MLP and CANFIS models. These models can be applied for multi-ahead forecasting of monthly electrical conductivity at both Varanasi and Prayagraj stations in the Upper Ganga basin. © 2023, The Author(s).
Novel Genetic Algorithm (GA) based hybrid machine learning-pedotransfer Function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity
(Taylor and Francis Ltd., 2022) Vijay Kumar Singh; Kanhu Charan Panda; Atish Sagar; Nadhir Al-Ansari; Huan-Feng Duan; Pradosh Kumar Paramaguru; Dinesh Kumar Vishwakarma; Ashish Kumar; Devendra Kumar; P.S. Kashyap; R.M. Singh; Ahmed Elbeltagi
Saturated hydraulic conductivity (Ks) is an important soil characteristic that controls water moves through the soil. On the other hand, its measurement is difficult, time-consuming, and expensive; hence Pedotransfer Functions (PTFs) are commonly used for its estimation. Despite significant development over the years, the PTFs showed poor performance in predicting Ks. Using Genetic Algorithm (GA), two hybrid Machine Learning based PTFs (ML-PTF), i.e. a combination of GA with Multilayer Perceptron (MLP-GA) and Support Vector Machine (SVM-GA), were proposed in this study. We compared the performances of four machine learning algorithms for different sets of predictors. The predictor combination containing sand, clay, Field Capacity, and Wilting Point showed the highest accuracy for all the ML-PTFs. Among the ML-PTFs, the SVM-GA algorithm outperformed the rest of the PTFs. It was noticed that the SVM-GA PTF demonstrated higher efficiency than the MLP-GA algorithm. The reference model for hydraulic conductivity prediction was selected as the SVM-GA PTF paired with the K-5 predictor variables. The proposed PTFs were compared with 160 models from past literature. It was found that the algorithms advocated were an improvement over these PTFs. The current model would help in efficient spatio-temporal measurement of hydraulic conductivity using pre-available databases. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Publisher Correction: Long-term application of agronomic management strategies effects on soil organic carbon, energy budgeting, and carbon footprint under rice–wheat cropping system (Scientific Reports, (2024), 14, 1, (337), 10.1038/s41598-023-48785-z)
(Nature Research, 2024) R.K. Naresh; P.K. Singh; Rajan Bhatt; Mandapelli Sharath Chandra; Yogesh Kumar; N.C. Mahajan; S.K. Gupta; Nadhir Al-Ansari; Mohamed A. Mattar
In the original version of this Article, Mohamed Mattar was omitted as a corresponding author. Correspondence and requests for materials should also be addressed to mmattar@ksu.edu.sa. © The Author(s) 2024.