Flood frequency analysis and inundation mapping for lower Narmada basin, India

Mangukiya, Nikunj K.; Jariwala, Raj; Mehta, Darshan

doi:10.2166/wpt.2022.009

Cited by 13 publications

(5 citation statements)

References 27 publications

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“…Moreover, water is a primary need for domestic, industrial, and agricultural activities (Mehta et al 2022). Thus, it is essential to carefully manage and plan water resources to reduce loss of life and property damage caused by drought, floods, or heat waves (Ali, Shahbaz 2020; Mangukiya et al 2022). Climate changes influence the hydrological cycle globally; the resulting variations in weather and climate have increased the risks of drought and floods because weather changes, variations in precipitation, peak flows, and extreme temperatures have impacts on river discharge (Mehmood et al 2021).…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid framework to model the relationship of daily river discharge with meteorological variables

Shabbir,

Chand,

Iqbal

2024

Meteorol. Hydrol. Water Manage.

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River discharge is affected by many factors, such as water level, rainfall, and precipitation. This study proposes a new hybrid framework named LAES (LASSO-ANN-EMD-SVM) to model the relationship of daily river discharge with meteorological variables. This hybrid framework is a composite of the least absolute shrinkage and selection operator (LASSO), an artificial neural network (ANN), and an error correction method. In the first stage, LASSO identifies meteorological variables that have a significant influence on the generation of river discharge. Next, the ANN model is used to predict river discharge using meteorological variables selected by LASSO, and the error series is determined. The error series is decomposed into intrinsic mode functions and residuals using empirical mode decomposition (EMD). The EMD components are modeled using the support vector machine (SVM) model, and the error predictions are aggregated. In the last stage, the LASSO-ANN predictions and the predicted error series are aggregated as the final discharge prediction. The proposed hybrid framework is illustrated on the Kabul River of Pakistan. The performance of the proposed hybrid framework is compared with six models using various performance measures and the Diebold-Mariano test. These models include multiple linear regression (MLR), SVM, ANN, LASSO-MLR, LASSO-SVM, and LASSO-ANN models. The findings reveal that the proposed hybrid model outperforms all other models considered in the study.In the testing phase, the root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) of the proposed LAES hybrid model are 337.143 m 3 /s, 32.354%, and 218.353 m 3 /s which are smaller than all other models compared in the study. Our proposed hybrid system is an efficient model for river discharge prediction that will be helpful in water management and protection against floods. Long-term prediction can help to identify the major effects of climate change and to make evidence-based environmental policies.

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Section: Introductionmentioning

confidence: 99%

A novel hybrid framework to model the relationship of daily river discharge with meteorological variables

Shabbir,

Chand,

Iqbal

2024

Meteorol. Hydrol. Water Manage.

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“…Unrealistic design discharge can lead to the failure or the over-budget of construction [4]. Researchers and engineers derive discharge designs based on an acceptable type of discharge data distribution in the frequency analysis [5][6][7][8]. However, there are difficulties in recognizing the distribution.…”

Section: Introductionmentioning

confidence: 99%

The Modification of Chi-Square Tests for the Identification of Rainfall and River Flow Data Distribution

Sulistiyono¹,

Yasa²,

Setiawan³

et al. 2023

cea

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The growing population in the community has led to an increase in the need for community infrastructure. Civil engineers have to maintain the safety of the community in the design of urban areas' service infrastructure. The infrastructure must resist the load caused by extreme events, such as rainstorms and floods. Therefore, civil engineers must design the infrastructure based on the precise data parameters. Engineers obtain the precise parameters of a return period through frequency analysis. The precise parameters will produce an acceptable data distribution. Civil engineers can use the Chi-Square method to test the fitness of the data distribution type. However, the original way to get the Expected Frequency is complicated because it uses the integral solving method. This weakness causes the engineers to linger to test the distribution suitability. This article proposes a modification to ease obtaining the expected frequency in the Chi-Square test. This article demonstrates the proposal using rainfall and river flow data around the globe. The demonstration results show that the proposal is easy to implement. The method accurately identifies the type of rainfall and river flow data distribution. Among the seven stations, five groups of data follow a lognormal distribution; one group of data follows a normal distribution. One other group of data follows an exponential distribution.

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“…The cumulative impacts of intensified land-use activities and climate change cast uncertainties on water resource availability, particularly by manipulating surface and groundwater hydrological regimes [7][8][9]. In this context, monitoring dam water levels is paramount for efficient dam operation and various applications such as reservoir management, understanding factors influencing water level variability, assessing climate change impacts on hydrological systems, and maintaining freshwater supply [10,11]. Comprehensive monitoring enables proactive decision-making, optimized water allocation, and adaptive strategies development to ensure sustainable water resource management amidst evolving environmental conditions [5,12].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning for Daily Forecasting Dam Water Levels

Almubaidin,

Ahmed,

Winston

et al. 2023

Tikrit j. eng. sci.

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The evolving character of the environment makes it challenging to predict water levels in advance. Despite being the most common approach for defining hydrologic processes and implementing physical system changes, the physics-based model has some practical limitations. Multiple studies have shown that machine learning, a data-driven approach to forecast hydrological processes, brings about more reliable data and is more efficient than traditional models. In this study, seven machine learning algorithms were developed to predict a dam water level daily based on the historical data of the dam water level. Multiple input combinations were investigated to improve the model’s sensitivity, and statistical indicators were used to assess the reliability of the developed model. The study of multiple models with multiple input scenarios suggested that the bagged trees model trained with seven days of lagged input provided the highest accuracy. The bagged tree model achieved an RMSE of 0.13953, taking less than 10 seconds to train. Its efficiency and accuracy made this model stand out from the rest of the trained model. With the deployment of this model on the field, the dam water level predictions can be made to help mitigate issues relating to water supply.

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Flood frequency analysis and inundation mapping for lower Narmada basin, India

Cited by 13 publications

References 27 publications

A novel hybrid framework to model the relationship of daily river discharge with meteorological variables

A novel hybrid framework to model the relationship of daily river discharge with meteorological variables

The Modification of Chi-Square Tests for the Identification of Rainfall and River Flow Data Distribution

Application of Machine Learning for Daily Forecasting Dam Water Levels

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