Neuro Fuzzy Application in Capacity Prediction and Forecasting Model for Ukai Reservoir

Saxena, Surabhi; Yadav, Sweta

doi:10.1007/978-3-319-55125-8_9

Cited by 2 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand for soil loss study, Saxena and Yadav (2017) adapted an Adaptive Neuro Fuzzy Inference System for reservoir capacity prediction and developed a forecasting model. Vulnerability assessment for Soil erosion was performed by Jain and Goel (2002) by preparing vegetation map, soil brightness index map, morphological and topographical parameters estimation and DEM.…”

Section: Introductionmentioning

confidence: 99%

Correlating Spatial-Temporal Changes in Ukai Reservoir with Universal Soil Loss Equation and Geospatial Indices

Avishek,

Chaturvedi,

Kumar

et al. 2024

Preprint

View full text Add to dashboard Cite

Ukai Reservoir witnessed flood incidents in 2006, 2013 and 2019, causing significant damage to property and infrastructure. The objective is to analyse the spatial-temporal change in inundation of Reservoir & soil loss. Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Universal Soil Loss Equation, and Land Use Land Cover (LULC) were applied. Landsat-8 data was collected from 2013 to 2020 for pre & post-monsoon periods. Decreasing water inundation area trend from 2015 to 2016 observed, possibly due to excess irrigation, power generation. 2016 faced increased water spread area, could be because of heavy precipitation in the upstream catchment. A high Rainfall erosivity factor (96.27 to 1427.58) indicates intense rainfall, which denotes a more remarkable ability of an area to detach and transport soil particles. High Soil Erodibility Factor (0.05 to 0.34), depict higher erodibility. The Topographic Factor (0 to 146.01), means more erosion occurs in steeper areas. The highest predicted annual soil loss was in the moderately high erosion category. Low Crop Management factor (0 to 1) denotes loss. Overall soil loss in this area is increasing, and need soil conservation. The Average Annual soil loss factor (26.64 to 44.05) depicts increase in soil loss. LULC analysis shows agriculture (69.77% in 2018) and barren land (24.38% in 2020) are the two predominant classes. The variation in the area was directed by rainfall in the post-monsoon time, excess irrigation, and electricity generation during the pre-monsoon time.

show abstract

Section: Introductionmentioning

confidence: 99%

Correlating Spatial-Temporal Changes in Ukai Reservoir with Universal Soil Loss Equation and Geospatial Indices

Avishek,

Chaturvedi,

Kumar

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Talei (2010) used ANFIS method for modeling rainfall-runoff process. Saxena and Yadav (2017) used ANFIS in capacity prediction for Ukai reservoir. Solgi et al (2014) assessed the capability of ANFIS model in forecasting daily precipitation.…”

Section: Introductionmentioning

confidence: 99%

Effect of Channel Boundary Conditions in Predicting Hydraulic Jump Characteristics using an ANFIS-Based Approach

Roushangar

Ghasempour

Valizadeh

2018

JAFM

View full text Add to dashboard Cite

Hydraulic jump is a phenomenon which is used to dissipate the kinetic energy of the flow and prevent scour below overflow spillways, chutes and sluices. This paper applies adaptive neuro-fuzzy inference system (ANFIS) as a Meta model approach to estimate hydraulic jump characteristics in channels with different bed conditions (i.e. channels with different shapes and appurtenances). In hydraulic jump characteristics modeling, different input combinations were developed and tested using 1700 experimental data. The obtained results indicated that the applied method has high capability in modeling hydraulic jump characteristics. It was observed that the developed models for expanding channel with a block performed more successful than other channels. For rectangular channels, it was found that the basin with rough bed led to better predictions compared to the basin with a step. In the prediction of jump length, the superior performance was obtained for the model with input combinations of Froude number and the relative height of jump. From the sensitivity analysis, it was induced that, Fr1 (upstream Froude number) is the most significant parameter in modeling process. Also comparison between ANFIS and semi-empirical equations indicated the great performance of the ANFIS.

show abstract

Neuro Fuzzy Application in Capacity Prediction and Forecasting Model for Ukai Reservoir

Cited by 2 publications

References 6 publications

Correlating Spatial-Temporal Changes in Ukai Reservoir with Universal Soil Loss Equation and Geospatial Indices

Correlating Spatial-Temporal Changes in Ukai Reservoir with Universal Soil Loss Equation and Geospatial Indices

Effect of Channel Boundary Conditions in Predicting Hydraulic Jump Characteristics using an ANFIS-Based Approach

Contact Info

Product

Resources

About