Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling

Bolick, Madeleine M.; Post, Christopher J.; Naser, M.Z.; Forghanparast, Farhang; Mikhailova, Elena A.

doi:10.3390/w15142581

Cited by 6 publications

(3 citation statements)

References 70 publications

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“…Xiao et al [54] found that Prophet improved runoff modeling in the Zhou River Basin. Bolick et al [55] used the Prophet method to predict hourly water level changes in an urban stream (South Carolina, USA) and obtained very accurate estimates, with coefficient of determination values greater than 0.9.…”

Section: Discussionmentioning

confidence: 99%

Forecasting and Anomaly Detection in BEWS: Comparative Study of Theta, Croston, and Prophet Algorithms

Grekov,

Vyshkvarkova,

Mavrin

2024

Forecasting

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Evaluation of water quality and accurate prediction of water pollution indicators are key components in water resource management and water pollution control. The use of biological early warning systems (BEWS), in which living organisms are used as biosensors, allows for a comprehensive assessment of the aquatic environment state and a timely response in the event of an emergency. In this paper, we examine three machine learning algorithms (Theta, Croston and Prophet) to forecast bivalves’ activity data obtained from the BEWS developed by the authors. An algorithm for anomalies detection in bivalves’ activity data was developed. Our results showed that for one of the anomalies, Prophet was the best method, and for the other two, the anomaly detection time did not differ between the methods. A comparison of methods in terms of computational speed showed the advantage of the Croston method. This anomaly detection algorithm can be effectively incorporated into the software of biological early warning systems, facilitating rapid responses to changes in the aquatic environment.

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Section: Discussionmentioning

confidence: 99%

Forecasting and Anomaly Detection in BEWS: Comparative Study of Theta, Croston, and Prophet Algorithms

Grekov,

Vyshkvarkova,

Mavrin

2024

Forecasting

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“…For instance, Bolick et al used the Prophet time series machine learning algorithm to predict the hourly water level variations at certain sites in urban streams. They further gathered ground optical remote sensing (lidar) data for Hunnicutt Creek, modeling these regions in 3D to elucidate how the predicted water level changes correspond to the variations in water levels within the stream channel [38]. A more comprehensive overview on the recent applications of radar data in rainfall prediction and real-time forecasting systems can be found in [39].…”

Section: Discussionmentioning

confidence: 99%

Predicting the Overflowing of Urban Personholes Based on Machine Learning Techniques

Chang,

Tseng,

Hsu

2023

Water

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Urban stormwater drainage systems, which include many personholes to collect and discharge precipitation within a city, are extensively constructed to prevent streets and buildings from flooding. This research intends to build a machine learning model to predict whether a personhole will overflow soon, which is crucial to alleviate the damage caused by floods. To address the challenges posed by many diverse personholes, we proposed segmenting the personholes into several groups and have designed two methods employing different personhole features. The first, the geography-based method, uses the geographical locations of the personholes for the grouping. The second, the hydrology-based method, uses the characteristics that are directly related to the overflowing situation, such as the depth of the personhole, and the average and the maximum water level of the personholes. We also investigated several machine learning techniques, such as the multilayer perceptron (MLP) model and a fine-tuning architecture. The study area was located in the new Taipei city and the experimental results have shown the impressive predictive ability of the proposed approaches. Particularly, by applying the hydrology-based grouping method, and using a hybrid model combining the machine learning model prediction results with heuristic rules, we can obtain the best prediction result, and the accuracy is over 99%. We have also noticed the influence of the activation function used in the neural network and the number of frozen layers in the fine-tuning architecture. Particularly, using the tanh function with one frozen layer is good in some cases. However, since it is not general enough, we suggest the readers perform empirical studies before choosing the best setting in their own environment.

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“…This study uses the Prophet time series machine learning algorithm to forecast hourly changes in water level in an urban stream, Hunnicutt Creek, Clemson, South Carolina (SC), USA. Bolick et al [3] collected terrestrial Light Detection and Ranging (LiDAR) data for Hunnicutt Creek to model these areas in 3D to illustrate how the predicted changes in water levels correspond to changes in water levels in the stream channel [3].…”

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confidence: 99%