International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 02 | Feb 2024
p-ISSN: 2395-0072
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Review on: Rainfall Runoff Modelling. Prof. Dr. S. S. Joshi 1, Anant A. Kamble 2 1 Head of Department, Department of Civil & Environmental Engineering, KIT’s College of
Engineering,Kolhapur, Maharashtra State, India
2 M-Tech Student, Department of Civil & Environmental Engineering, KIT’s College of Engineering,
Kolhapur, Maharashtra State, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - One of the most significant natural resources and
ability to drain, which increases the likelihood of flood-like conditions. Numerous scholars are concentrating their investigations on these problems and attempting to identify a remedy. Here is a quick summary of some of these scientists' research projects that suggested using rainfall runoff models.
a vital component of a state's and nation's socioeconomic growth is water. Water has an impact on every aspect of the ecosystem that sustains life on Earth. Since fresh water is a finite resource, humanity is concerned about its fluctuating availability in space and time. A rainfall-runoff model is a mathematical representation of the relationships between rainfall and runoff in a watershed, drainage basin, or catchment area. Calculating discharge from a basin can be greatly aided by the use of a rainy runoff model.
Key Words: Flood Modelling, River, DEM, GIS, HEC-HMS.
1. LITERATURE REVIEW M. P. Rajurkar, U. C. Kothyari & U. C. Chaube (2017)[1]
Hydrological modelling is a popular technique for assessing a basin's hydrological response to precipitation. There are many disadvantages of using hydrological measurement. It is true that we have a restricted range of measurements in both space and time, as well as a limited breadth of measurement techniques. We consequently require a method to extrapolate from those available observations in space and time to assess the potential impact of future hydrological change, particularly to ungauged catchments (where measurements are unavailable) and into the future (where measurements are unfeasible). When making judgments, it should be helpful to be able to predict or extrapolate statistically using various types of models.
This study presents the use of artificial neural networks to model daily flows during monsoon flood events in a sizable watershed of the Narmada River in Madhya Pradesh, India. The Jamtara gauge and discharge station, situated in the central Indian state of Madhya Pradesh, is the source of daily rainfall and runoff data for the Narmada watershed. In comparison to linear and nonlinear MISO models, it is demonstrated that a linear multiple-input single-output (MISO) model combined with the artificial neural network (ANN) offers a superior representation of the rainfall-runoff relationship in such large size catchments. Compared to the previous models examined here, the current model improves prediction accuracy and offers a methodical approach to runoff estimates.
Despite the fact that water covers 70% of the earth's surface, there is a severe water issue. This is due to the fact that 97.5 percent of all water on Earth is saltwater. 99 percent of the water that is left is trapped in subterranean aquifers and glaciers. Therefore, in actuality, less than 1% of freshwater is accessible to humans as rivers, lakes, streams, etc. Please be aware that even the last man on Earth may have his needs met by the less than 1% water availability. But if appropriate precautions are not taken for the best possible management of water resources, human existence would soon be in jeopardy due to human invasion at all levels. For example, the "day zero" scenario is already affecting 12% of the Indian population.
AnilKumar Lohani, N.K. Goel & K.K.S.Bhatia (2011)[2] In order to estimate daily rainfall-runoff, this article analyzes techniques based on artificial neural networks (ANN), fuzzy logic (FL), and linear transfer functions (LTF). The Takagi-Sugeno (TS) fuzzy model's potential and the effect of antecedent soil moisture conditions on the daily rainfall-runoff models' performance are also examined in this work. To examine the effects of input data vector on rainfall-runoff modeling, eleven distinct input vectors are investigated under four classes: (i) rainfall, (ii) rainfall and antecedent moisture content, (iii) rainfall and runoff, and (iv) rainfall, runoff, and antecedent moisture content. Based on a variety of model performance metrics, a viable modelling technique with an adequate model input structure is proposed using the rainfall-runoff data of the upper Narmada basin, Central India. The outcomes demonstrate that the fuzzy modeling strategy.
In recent decades, India has experienced an increase in the frequency of flood disasters. A flood is a stream's natural flow that overwhelms a riverbank or hydraulic structure, causing harm to nearby property or human lives. The primary cause of this is the changing climate and massive development, which have changed the features of the basin. The development projects near riverbanks have the worst effects on a reach's
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