Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

Malik, Anurag; Tikhamarine, Yazid; Al‐Ansari, Nadhir; Shahid, Shamsuddin; Sekhon, Harkanwaljot Singh; Pal, R. K.; Rai, Priya; Pandey, Kusum; Singh, Padam; Elbeltagi, Ahmed; Sammen, Saad Sh.

doi:10.1080/19942060.2021.1942990

Cited by 26 publications

(9 citation statements)

References 88 publications

(104 reference statements)

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“…Throughout the investigation, pan evaporation (EP d ) measurement and predicted values were compared. Statistical assessment was done to compare the accuracy of the applied algorithms (i.e., SVM, RT, REPTree, and RSS) using the root mean square error (RMSE) [8,10,44], mean absolute error (MAE) [7,45], Nash-Sutcliffe efficiency (NSE) [4,45], Willmott index (WI) [8], and correlation coefficient (r) [19]. In addition, qualitative performance was evaluated through graphical scrutiny.…”

Section: Statistical Assessment and Validationmentioning

confidence: 99%

Data Intelligence Model and Meta-Heuristic Algorithms-Based Pan Evaporation Modelling in Two Different Agro-Climatic Zones: A Case Study from Northern India

et al. 2021

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Precise quantification of evaporation has a vital role in effective crop modelling, irrigation scheduling, and agricultural water management. In recent years, the data-driven models using meta-heuristics algorithms have attracted the attention of researchers worldwide. In this investigation, we have examined the performance of models employing four meta-heuristic algorithms, namely, support vector machine (SVM), random tree (RT), reduced error pruning tree (REPTree), and random subspace (RSS) for simulating daily pan evaporation (EPd) at two different locations in north India representing semi-arid climate (New Delhi) and sub-humid climate (Ludhiana). The most suitable combinations of meteorological input variables as covariates to estimate EPd were ascertained through the subset regression technique followed by sensitivity analyses. The statistical indicators such as root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), Willmott index (WI), and correlation coefficient (r) followed by graphical interpretations, were utilized for model evaluation. The SVM algorithm successfully performed in reconstructing the EPd time series with acceptable statistical criteria (i.e., NSE = 0.937, 0.795; WI = 0.984, 0.943; r = 0.968, 0.902; MAE = 0.055, 0.993 mm/day; and RMSE = 0.092, 1.317 mm/day) compared with the other applied algorithms during the testing phase at the New Delhi and Ludhiana stations, respectively. This study also demonstrated and discussed the potential of meta-heuristic algorithms for producing reasonable estimates of daily evaporation using minimal meteorological input variables with applicability of the best candidate model vetted in two diverse agro-climatic settings.

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Section: Statistical Assessment and Validationmentioning

confidence: 99%

Data Intelligence Model and Meta-Heuristic Algorithms-Based Pan Evaporation Modelling in Two Different Agro-Climatic Zones: A Case Study from Northern India

et al. 2021

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“…However, the major drawback of NF is that its performance significantly is susceptible to the selection and optimization of the input variable's fuzzy membership function. The state-of-the-art hybrid AI model displayed promising prediction results over standalone models in different hydrological studies (Maroufpoor et al, 2019a;Pham et al, 2019;Maroufpoor et al, 2020;Mohammadi et al, 2020;Ebtehaj et al, 2021;Malik et al, 2021;Sammen et al, 2021). Therefore, such models may be a suitable alternative to standalone models in DO prediction.…”

Section: Introductionmentioning

confidence: 99%

A novel hybridized neuro-fuzzy model with an optimal input combination for dissolved oxygen estimation

Maroufpoor

Sammen

Al‐Ansari

et al. 2022

Front. Environ. Sci.

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Dissolved oxygen (DO) is one of the main prerequisites to protect amphibian biological systems and to support powerful administration choices. This research investigated the applicability of Shannon’s entropy theory and correlation in obtaining the combination of the optimum inputs, and then the abstracted input variables were used to develop three novel intelligent hybrid models, namely, NF-GWO (neuro-fuzzy with grey wolf optimizer), NF-SC (subtractive clustering), and NF-FCM (fuzzy c-mean), for estimation of DO concentration. Seven different input combinations of water quality variables, including water temperature (TE), specific conductivity (SC), turbidity (Tu), and pH, were used to develop the prediction models at two stations in California. The performance of proposed models for DO estimation was assessed using statistical metrics and visual interpretation. The results revealed the better performance of NF-GWO for all input combinations than other models where its performance was improved by 24.2–66.2% and 14.9–31.2% in terms of CC (correlation coefficient) and WI (Willmott index) compared to standalone NF for different input combinations. Additionally, the MAE (mean absolute error) and RMSE (root mean absolute error) of the NF model were reduced using the NF-GWO model by 9.9–46.0% and 8.9–47.5%, respectively. Therefore, NF-GWO with all water quality variables as input can be considered the optimal model for predicting DO concentration of the two stations. In contrast, NF-SC performed worst for most of the input combinations. The violin plot of NF-GWO-predicted DO was found most similar to the violin plot of observed data. The dissimilarity with the observed violin was found high for the NF-FCM model. Therefore, this study promotes the hybrid intelligence models to predict DO concentration accurately and resolve complex hydro-environmental problems.

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“…In rainfall-runoff modeling [2,26] and rainfall forecasting [27,28], the use of artificial intelligence (AI) and machine learning (ML) established modeling in water resources in a new direction. Several studies attempt the application of AI and ML, whether for R-R or for rainfall forecasting [4,9,29,30], streamflow [31][32][33][34][35][36], suspended sediment-load prediction [36][37][38][39][40][41][42], flood forecasting [5,6,43], stage-discharge modeling [44][45][46][47][48], soil temperature estimation [49][50][51][52][53][54][55][56], pan evaporation [57][58][59][60][61][62][63][64][65][66][67]…”

Section: Introductionmentioning

confidence: 99%

An Integrated Statistical-Machine Learning Approach for Runoff Prediction

et al. 2022

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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.

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Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

Abstract: Sammen (2021) Daily pan-evaporation estimation in different agroclimatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test, Engineering

Cited by 26 publications

References 88 publications

Data Intelligence Model and Meta-Heuristic Algorithms-Based Pan Evaporation Modelling in Two Different Agro-Climatic Zones: A Case Study from Northern India

Data Intelligence Model and Meta-Heuristic Algorithms-Based Pan Evaporation Modelling in Two Different Agro-Climatic Zones: A Case Study from Northern India

A novel hybridized neuro-fuzzy model with an optimal input combination for dissolved oxygen estimation

An Integrated Statistical-Machine Learning Approach for Runoff Prediction

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