Modeling of Rainfall and Ground Water Fluctuation of Gonda  District Uttar Pradesh, India

Vishwakarma, Dinesh Kumar; Kumar, Rohitashw; Pandey, Kusum; Singh, Vikash; Kushwaha, Kuldeep Singh

doi:10.20546/ijcmas.2018.705.302

Cited by 14 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For PBIAS, between 0 to 10 is considered to be very good, 10 to 15 is considered to be good, 15 to 25 is considered to be fair and more than 25 is regarded as unsatisfactory or inadequate. A better and excellence model is one that has the minimum value of root mean square error (RMSE) and percent bias (PBIAS) and higher values of coefficients of correlation (CC) [41][42][43][44][45][46].…”

Section: Model Performance Evaluationmentioning

confidence: 99%

Daily Sediment Yield Prediction Using Hybrid Machine Learning Approach

Yadav

Alam²,

Suryavanshi³

2023

IJECC

View full text Add to dashboard Cite

In this study, four different soft computing AI techniques were tested for the prediction of sediment yield based on hydro-meteorological variables at Jondhara station, Seonath stream in Rajnandgaon district, India. In order to fulfill this purpose, the models namely, multilayer perceptron (MLP), support vector machine (SVM), multilayer perceptron coupled with genetic algorithm (MLP-GA), and support vector machine coupled with genetic algorithm (SVM-GA) models were employed. To select the optimal input variables, a statistical method such as the Gamma test was considered among several methods. Based on the results of the analysis, all models were evaluated by using the following statistical indices: Coefficient of Correlation (CC), room mean square error (RMSE) and percent bias (PBAIS). Overall, the performance of the studied models indicates that all of them are capable of simulation sediments yield at Jondhara station, Seonath river basin in a satisfactory manner. Comparison of results showed that the MLP-GA with CC = 0.988, RMSE = 0.006 and PBIAS = 0.000 in training period and CC= 0.990, RMSE = 0.007 and PBIAS = 0.000 in testing period for S-6 model and CC = 0.986, RMSE = 0.025 and PBIAS = -0.001 in training period and CC = 0.988, RMSE = 0.029 and PBIAS = -0.001 in testing period for S-13 model were able to yield better results than the other models considered. Furthermore, an SVM model is also observed to have some advantages over MLP models and SVM-GA models since it can represent the output data in a continuous manner by fitting a linear regression function to the output data, which has the advantage of making the model more precise than MLP and SVM-GA models.

show abstract

Section: Model Performance Evaluationmentioning

confidence: 99%

Daily Sediment Yield Prediction Using Hybrid Machine Learning Approach

Yadav

Alam²,

Suryavanshi³

2023

IJECC

View full text Add to dashboard Cite

show abstract

“…The performances of the MLR, MARS, SVM, and RF models were evaluated based on the coefficient of determination (R 2 ), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS), and visual interpretation using a line diagram, scatter diagram, violin plot, and relative and Taylor diagrams. The R 2 , RMSE [12,47,117,137,138], NSE [139], and BIAS [119,140,141] are described as:…”

Section: Performance Evaluation Of Modelsmentioning

confidence: 99%

“…The socioeconomic impacts of rainfall are significant, from physical damage in floods to disruptions in transport networks [3,9]. Simultaneously, India is challenged with increasing population and climate change, which have threatened the present freshwater need for irrigation and drinking [10][11][12][13]. To overcome the challenges of water scarcity and the deterioration of cultivable land, modeling rainfall-runoff plays an important role.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Integrated Statistical-Machine Learning Approach for Runoff Prediction

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…A linear MLR model is employed to predict the most common output-input variables, and these multiple variables are linearly related [ 17 ]. Different analytical models were used to forecast stage-discharge for monitoring the water resource activities at the basin level, like flood routing, flood mitigation and protection, drought assessment, and optimization of reservoir activities for agriculture, electricity generation, and drinking water supply [ [18] , [19] , [20] , [21] ].…”

Section: Introductionmentioning

confidence: 99%