Three Steps towards Better Forecasting for Streamflow Deep Learning

Tan, Woon Yang; Hin, Lai Sai; Teo, Fang Yenn; Armaghani, Danial Jahed; Kumar, Pravendra; El-Shafie, Ahmed

doi:10.3390/app122412567

Cited by 9 publications

(4 citation statements)

References 52 publications

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“…By including this extended dataset, which covers a longer time span, we aim to improve the accuracy of our study. The availability of a larger and more comprehensive dataset allows us to capture a wider range of rainfall patterns and trends, which can contribute to more precise predictions [6]. In the study conducted by Baharak Motamidwairi et al, they examined a dataset covering 10 years of data.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Streamflow in River Basin-Using ANN

K.M,

Jitender,

et al. 2024

E3S Web of Conf.

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In addition to the flood level predictions, our system provides valuable insights into future rainfall patterns. With the data set we have gathered, we can determine the expected amount of rainfall in the upcoming months. By combining the flood level predictions with the rainfall data, we can better understand the overall flood risk and take proactive measures to mitigate its impact. Our system equips us with the necessary information to make informed decisions and enhance flood preparedness strategies. The main difference is that we focus on predicting flood levels using a combination of current water level data and real-time weather data. This allows us to have a more accurate understanding of potential flood events. Additionally, our paper also incorporates rainfall data to assess the risk of flooding in the coming months. By considering multiple factors, we aim to provide a more holistic understanding of flood risks and enhance preparedness strategies. By combining the flood level predictions with rainfall forecasts, we can assess the flood risk in the coming months and take preventive actions, such as implementing early warning systems or strengthening infrastructure, to minimize the impact of potential flood events.

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Section: Introductionmentioning

confidence: 99%

Prediction of Streamflow in River Basin-Using ANN

K.M,

Jitender,

et al. 2024

E3S Web of Conf.

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“…Diverse classification, variable relationship recognition, and regression problems could be solved by utilizing tree-based models [56]. Compatibility with different assumptions, diverse data distributions, and simple construction make these models one of the applicable methods in geotechnics [57][58][59]. Pham et al [60] presented a new model for the classification of soils using Adaboost and enhanced tree methods.…”

Section: Introductionmentioning

confidence: 99%

Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting

et al. 2023

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Blasting operations involve some undesirable environmental issues that may cause damage to equipment and surrounding areas. One of them, and probably the most important one, is flyrock induced by blasting, where its accurate estimation before the operation is essential to identify the blasting zone’s safety zone. This study introduces several tree-based solutions for an accurate prediction of flyrock. This has been done using four techniques, i.e., decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). The modelling of tree-based techniques was conducted with in-depth knowledge and understanding of their most influential factors. The mentioned factors were designed through the use of several parametric investigations, which can also be utilized in other engineering fields. As a result, all four tree-based models are capable enough for blasting-induced flyrock prediction. However, the most accurate predicted flyrock values were obtained using the AdaBoost technique. Observed and forecasted flyrock by AdaBoost for the training and testing phases received coefficients of determination (R2) of 0.99 and 0.99, respectively, which confirm the power of this technique in estimating flyrock. Additionally, according to the results of the input parameters, the powder factor had the highest influence on flyrock, whereas burden and spacing had the lowest impact on flyrock.

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“…From a monetary perspective, the application of ML techniques is also profitable, because it reduces the costs related to lab tests for ascertaining the UCS. It is important to note that the mentioned ML techniques have been used and applied to solve science and engineering problems [ 21 , 23 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ].…”

Section: Introductionmentioning

confidence: 99%

Advanced Tree-Based Techniques for Predicting Unconfined Compressive Strength of Rock Material Employing Non-Destructive and Petrographic Tests

et al. 2023

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The accurate estimation of rock strength is an essential task in almost all rock-based projects, such as tunnelling and excavation. Numerous efforts to create indirect techniques for calculating unconfined compressive strength (UCS) have been attempted. This is often due to the complexity of collecting and completing the abovementioned lab tests. This study applied two advanced machine learning techniques, including the extreme gradient boosting trees and random forest, for predicting the UCS based on non-destructive tests and petrographic studies. Before applying these models, a feature selection was conducted using a Pearson’s Chi-Square test. This technique selected the following inputs for the development of the gradient boosting tree (XGBT) and random forest (RF) models: dry density and ultrasonic velocity as non-destructive tests, and mica, quartz, and plagioclase as petrographic results. In addition to XGBT and RF models, some empirical equations and two single decision trees (DTs) were developed to predict UCS values. The results of this study showed that the XGBT model outperforms the RF for UCS prediction in terms of both system accuracy and error. The linear correlation of XGBT was 0.994, and its mean absolute error was 0.113. In addition, the XGBT model outperformed single DTs and empirical equations. The XGBT and RF models also outperformed KNN (R = 0.708), ANN (R = 0.625), and SVM (R = 0.816) models. The findings of this study imply that the XGBT and RF can be employed efficiently for predicting the UCS values.

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Three Steps towards Better Forecasting for Streamflow Deep Learning

Cited by 9 publications

References 52 publications

Prediction of Streamflow in River Basin-Using ANN

Prediction of Streamflow in River Basin-Using ANN

Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting

Advanced Tree-Based Techniques for Predicting Unconfined Compressive Strength of Rock Material Employing Non-Destructive and Petrographic Tests

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