Development and application of modeling techniques to estimate the unsaturated hydraulic conductivity

Singh, Karan; Singh, Balraj; Sihag, Parveen; Kumar, V. D. Ambeth; Sharma, Kul Vaibhav

doi:10.1007/s40808-023-01744-z

Cited by 14 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The development of AI, however, was gradual until the 1980s, when ML made it possible for AI systems to continuously improve their performance by learning from data [51]. Among the early ML techniques were decision trees (DT) [52], neural networks (NN) [53], and support vector machines (SVM) [54]. These methods opened the way for the development of AI systems that can identify patterns in speech, text, and picture data [55].…”

Section: Methodsmentioning

confidence: 99%

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Kumar,

Azamathulla,

Sharma

et al. 2023

Sustainability

Self Cite

View full text Add to dashboard Cite

Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure.

show abstract

Section: Methodsmentioning

confidence: 99%

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Kumar,

Azamathulla,

Sharma

et al. 2023

Sustainability

Self Cite

View full text Add to dashboard Cite

show abstract

“…Data-driven models are also sensitive to the quality and representativeness of the training data. Biases or outliers in the data can significantly affect the model's performance, and it may be challenging to identify and address these issues without a good understanding of the underlying hydrological processes [19,20].…”

Section: Introductionmentioning

confidence: 99%

Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models

Kumar

Kedam

Sharma

et al. 2023

Water

Self Cite

View full text Add to dashboard Cite

The management of water resources depends heavily on hydrological prediction, and advances in machine learning (ML) present prospects for improving predictive modelling capabilities. This study investigates the use of a variety of widely used machine learning algorithms, such as CatBoost, ElasticNet, k-Nearest Neighbors (KNN), Lasso, Light Gradient Boosting Machine Regressor (LGBM), Linear Regression (LR), Multilayer Perceptron (MLP), Random Forest (RF), Ridge, Stochastic Gradient Descent (SGD), and the Extreme Gradient Boosting Regression Model (XGBoost), to predict the river inflow of the Garudeshwar watershed, a key element in planning for flood control and water supply. The substantial engineering feature used in the study, which incorporates temporal lag and contextual data based on Indian seasons, leads it distinctiveness. The study concludes that the CatBoost method demonstrated remarkable performance across various metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) values, for both training and testing datasets. This was accomplished by an in-depth investigation and model comparison. In contrast to CatBoost, XGBoost and LGBM demonstrated a higher percentage of data points with prediction errors exceeding 35% for moderate inflow numbers above 10,000. CatBoost established itself as a reliable method for hydrological time-series modelling, easily managing both categorical and continuous variables, and thereby greatly enhancing prediction accuracy. The results of this study highlight the value and promise of widely used machine learning algorithms in hydrology and offer valuable insights for academics and industry professionals.

show abstract

“…Its ability to capture complicated weather patterns makes it an invaluable tool for improving overall forecast performance [33]. Support Vector Regression (SVR) is a machine learning model for regression problems that is based on Support Vector Machines (SVMs) [34,35]. SVR can capture non-linear correlations between meteorological factors and rainfall in rainfall forecasting.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities

Kumar,

Kedam,

Sharma

et al. 2023

Sustainability

Self Cite

View full text Add to dashboard Cite

Current research studies offer an investigation of machine learning methods used for forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their temporal complexities, are exploited using a unique data segmentation approach, providing discrete training, validation, and testing sets. Two unique models are created: Model-1, which is based on daily data, and Model-2, which is based on weekly data. A variety of performance criteria are used to rigorously analyze these models. CatBoost, XGBoost, Lasso, Ridge, Linear Regression, and LGBM are among the algorithms under consideration. This research study provides insights into their predictive abilities, revealing significant trends across the training, validation, and testing phases. The results show that ensemble-based algorithms, particularly CatBoost and XGBoost, outperform in both models. CatBoost emerged as the model of choice throughout all assessment stages, including training, validation, and testing. The MAE was 0.00077, the RMSE was 0.0010, the RMSPE was 0.49, and the R2 was 0.99, confirming CatBoost’s unrivaled ability to identify deep temporal intricacies within daily rainfall patterns. Both models had an R2 of 0.99, indicating their remarkable ability to predict weekly rainfall trends. Significant results for XGBoost included an MAE of 0.02 and an RMSE of 0.10, indicating their ability to handle longer time intervals. The predictive performance of Lasso, Ridge, and Linear Regression varies. Scatter plots demonstrate the robustness of CatBoost and XGBoost by demonstrating their capacity to sustain consistently low prediction errors across the dataset. This study emphasizes the potential to transform urban meteorology and planning, improve decision-making through precise rainfall forecasts, and contribute to disaster preparedness measures.

show abstract

Development and application of modeling techniques to estimate the unsaturated hydraulic conductivity

Cited by 14 publications

References 20 publications

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models

A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities

Contact Info

Product

Resources

About