Runoff Prediction Using Hybrid Neural Networks in Semi-Arid Watershed, India: A Case Study

Samantaray, Sandeep; Sahoo, Abinash; Mohanta, Nihar Ranjan; Biswal, Paresh; Das, Umesh Kumar

doi:10.1007/978-981-15-5397-4_74

Cited by 9 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this huge production of energy, a great amount of fly ash is released. And, this industrial waste is responsible for environmental pollution and space cover [8]. Many researches are being carried out all over the world in order to determine the effects of fly ash on various properties of concrete.…”

Section: Introductionmentioning

confidence: 99%

Study on the Combined Effect of Agricultural and Industrial Waste on the Properties of Self Compacting Concrete

2023

IRJMETS

View full text Add to dashboard Cite

The objective of this research was to examine the behavior of concrete when the cement is duly replaced by two different materials one is agricultural waste i.e. Rice Husk Ash and another is industrial waste i.e. Marble Dust Powder. This project presents the study of concrete mix design (M40 mix) using rice husk ash and marble Dust. The problems of disposal of the rice husk ash & Marble Dust also seemed feasible by the results obtained. RHA-MD causes significant increase in the compressive strength of concrete. Compressive strength, Flexural strength test as well as Split Tensile Strength were conducted on RHA-MD concrete mix. The concrete is mixed with RHA and MD in varying percentage of 5-35% with both the materials varying, then properties like compressive strength, flexural strength and split tensile strength are studied. There has been a linear increase in the strength properties of concrete mix up to 25% of RHA-MD thereafter gradual decrease is observed in the strength properties of the mix. The optimum value of RHA-MD in concrete is 25% with the proportion of RHA being 15% and MD being 10%. The investigation concluded that the concrete containing RHA-MD has better strength characteristics than the normal concrete.

show abstract

Section: Introductionmentioning

confidence: 99%

Study on the Combined Effect of Agricultural and Industrial Waste on the Properties of Self Compacting Concrete

2023

IRJMETS

View full text Add to dashboard Cite

show abstract

“…The necessity for short-and longterm runoff simulation is significant for managing watersheds which comprises surplus runoff control, managing and using runoff for detailed tenacities. The complexity of the rainfall-runoff process and its unpredictability depending on watershed features and rainfall patterns makes it difficult to predict and estimate with desired accuracy (Abudi et al 2012;Samantaray et al 2019Samantaray et al , 2021. Yet, hydrologists have recently developed several approaches and models varying from empirical-to physical-based relationships.…”

Section: Introductionmentioning

confidence: 99%

Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches

Samantaray

Ghose

2021

Journal of Water and Climate Change

Self Cite

View full text Add to dashboard Cite

Effective prediction of runoff is a substantial feature for the successful management of hydrological phenomena in arid regions. The present research findings reveal that a rainfall simulator (RS) can be a valuable instrument to estimate runoff as the intensity of rainfall is modifiable in the course of an experimental process, which turns out to be of great advantage. The rainfall-runoff process is a complex physical phenomenon caused by the effect of various parameters. In this research, a new hybrid technique integrating PSR (phase space reconstruction) with FFA (firefly algorithm) and SVM (support vector machine) has gained recognition in various modelling investigations in contrast to the principle of empirical risk minimization through ANN practices. Outcomes of SVM are contrasted against SVM-FFA and PSR-SVM-FFA models. The improvements in NSE (Nash–Sutcliffe efficiency), RMSE (Root Mean Square Error), and WI (Willmott's Index) by PSR-SVM-FFA over SVM models specify that the prediction accuracy of the hybrid model is better. The established PSR-SVM-FFA model generates preeminent WI values that range from 0.97 to 0.98, while the SVM and SVM-FFA models encompass 0.93–0.95 and 0.96–0.97, respectively. The proposed PSR-SVM-FFA model gives more accurate results and error limiting up to 2–3%.

show abstract

Monthly Runoff Prediction by Hybrid CNN-LSTM Model: A Case Study

Ghose

Mahakur

Sahoo

2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

Runoff Prediction Using Hybrid Neural Networks in Semi-Arid Watershed, India: A Case Study

Cited by 9 publications

References 17 publications

Study on the Combined Effect of Agricultural and Industrial Waste on the Properties of Self Compacting Concrete

Study on the Combined Effect of Agricultural and Industrial Waste on the Properties of Self Compacting Concrete

Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches

Monthly Runoff Prediction by Hybrid CNN-LSTM Model: A Case Study

Contact Info

Product

Resources

About