Water

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19 pages, 3576 KiB

Open AccessArticle

The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed

by Nabila Siti Burnama, Faizal Immaddudin Wira Rohmat, Mohammad Farid, Arno Adi Kuntoro, Hadi Kardhana, Fauzan Ikhlas Wira Rohmat and Winda Wijayasari

Water 2023, 15(17), 3026; https://doi.org/10.3390/w15173026 - 23 Aug 2023

Cited by 3 | Viewed by 1899

Abstract

The Majalaya area is one of the most valuable economic districts in the south of Greater Bandung, West Java, Indonesia, and experiences at least six floods per year. The floods are characterized by a sudden rise in the water level approximately one to [...] Read more.

The Majalaya area is one of the most valuable economic districts in the south of Greater Bandung, West Java, Indonesia, and experiences at least six floods per year. The floods are characterized by a sudden rise in the water level approximately one to two hours after the rain occurs. With the aim of reducing flood risk, this study models a data-driven method for predicting the inundation height across the Majalaya Watershed. The flood inundation maps of selected events were modeled using the HEC-RAS 2D numerical model. Extracted data from the HEC-RAS model, GSMaP satellite rainfall data, elevation, and other spatial data were combined to build an artificial neural network (ANN) model. The trained model targets inundation height, while the spatiotemporal data serve as the explanatory variables. The results from the trained ANN model provided very good R² (0.9537), NSE (0.9292), and RMSE (0.3701) validation performances. The ANN model was tested with a new dataset to demonstrate the capability of predicting flood inundation height with unseen data. Such a data-driven approach is a promising tool to be developed to reduce flood risks in the Majalaya Watershed and other flood-prone locations. Full article

(This article belongs to the Special Issue A Safer Future—Prediction of Water-Related Disasters)

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31 pages, 6519 KiB

Open AccessArticle

Low-Flow (7-Day, 10-Year) Classical Statistical and Improved Machine Learning Estimation Methodologies

by Andrew DelSanto, Md Abul Ehsan Bhuiyan, Konstantinos M. Andreadis and Richard N. Palmer

Water 2023, 15(15), 2813; https://doi.org/10.3390/w15152813 - 3 Aug 2023

Viewed by 2247

Abstract

Water resource managers require accurate estimates of the 7-day, 10-year low flow (7Q10) of streams for many reasons, including protecting aquatic species, designing wastewater treatment plants, and calculating municipal water availability. StreamStats, a publicly available web application developed by the United States Geologic [...] Read more.

Water resource managers require accurate estimates of the 7-day, 10-year low flow (7Q10) of streams for many reasons, including protecting aquatic species, designing wastewater treatment plants, and calculating municipal water availability. StreamStats, a publicly available web application developed by the United States Geologic Survey that is commonly used by resource managers for estimating the 7Q10 in states where it is available, utilizes state-by-state, locally calibrated regression equations for estimation. This paper expands StreamStats’ methodology and improves 7Q10 estimation by developing a more regionally applicable and generalized methodology for 7Q10 estimation. In addition to classical methodologies, namely multiple linear regression (MLR) and multiple linear regression in log space (LTLR), three promising machine learning algorithms, random forest (RF) decision trees, neural networks (NN), and generalized additive models (GAM), are tested to determine if more advanced statistical methods offer improved estimation. For illustrative purposes, this methodology is applied to and verified for the full range of unimpaired, gaged basins in both the northeast and mid-Atlantic hydrologic regions of the United States (with basin sizes ranging from 2–1419 mi²) using leave-one-out cross-validation (LOOCV). Pearson’s correlation coefficient (R²), root mean square error (RMSE), Kling–Gupta Efficiency (KGE), and Nash–Sutcliffe Efficiency (NSE) are used to evaluate the performance of each method. Results suggest that each method provides varying results based on basin size, with RF displaying the smallest average RMSE (5.85) across all ranges of basin sizes. Full article

(This article belongs to the Special Issue A Safer Future—Prediction of Water-Related Disasters)

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25 pages, 2296 KiB

Open AccessArticle

Understanding the Challenges of Hydrological Analysis at Bridge Collapse Sites

by Fahmidah U. Ashraf and Mohammad H. Islam

Water 2023, 15(15), 2772; https://doi.org/10.3390/w15152772 - 31 Jul 2023

Cited by 1 | Viewed by 2085

Abstract

There is a crucial need for modeling hydrological extremes in order to optimize hydraulic system safety. It is often perceived that the best-fitted distribution accurately captures the intricacies of the hydrological extremes, particularly for the least disturbed watersheds. Thirty collapse sites with the [...] Read more.

There is a crucial need for modeling hydrological extremes in order to optimize hydraulic system safety. It is often perceived that the best-fitted distribution accurately captures the intricacies of the hydrological extremes, particularly for the least disturbed watersheds. Thirty collapse sites with the least disturbed watersheds within the Appalachian Highland region in the U.S. are identified and used to test this perception. Goodness-of-fit tests, time series analysis, and comparison of predictor variables are carried out to find out the best-fitted distribution, identify trends and seasonal variation, and assess site variability. The study results are found to be inconclusive and sometimes contradictory; sometimes even complex distribution models do not provide better results. For most sites, the historic peak flow data are best-fitted with multiple distributions, including heavy and light tails. For monthly flow data, seasonal variation and trend cannot be categorized since no definitive, distinct tendency can be identified. When comparing sites best-fitted with a single distribution to sites best-fitted with multiple distributions, significant differences in certain geospatial characteristics are identified. However, these characteristics at the watershed scale are claimed to be less important in predicting the behavior of a flood event. All of these results capture the difficulties and inconsistencies in interpreting the results of hydrologic analysis, potentially reducing the robustness of the hydrologic tools used in the design and risk assessment of bridges. Full article

(This article belongs to the Special Issue A Safer Future—Prediction of Water-Related Disasters)

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20 pages, 10508 KiB

Open AccessArticle

Driving Forces and Influences of Flood Diversion on Discharge Fraction and Peak Water Levels at an H-Shaped Compound River Node in the Pearl River Delta, South China

by Yongjun Fang, Xianwei Wang, Jie Ren, Huan Liu and Ya Wang

Water 2023, 15(11), 1970; https://doi.org/10.3390/w15111970 - 23 May 2023

Cited by 1 | Viewed by 1528

Abstract

The SiXianJiao (SXJ) is the first-order exchange node of the West River and the North River and redistributes water (mass) to the downstream river network in the Pearl River Delta (PRD), South China. The lateral SXJ waterway plays a critical role in flow [...] Read more.

The SiXianJiao (SXJ) is the first-order exchange node of the West River and the North River and redistributes water (mass) to the downstream river network in the Pearl River Delta (PRD), South China. The lateral SXJ waterway plays a critical role in flow (mass) diversion between the West River and the North River, forming a unique H-shaped compound river node. Previous studies mainly focused on Y-shaped bifurcation and confluence nodes, and there is a lack of research on deltaic H-shaped river nodes. This study established the Delft3D model to investigate the driving forces and influences of flood diversion at the SXJ node. The results showed that the H-shaped SXJ river node was usually in hydraulic equilibrium but was often disturbed by large water level differences between the two rivers, due to unbalanced and asynchronous upstream flood waves. The large water level differences drove mutual flood diversion through the lateral SXJ waterway, which synchronized the downstream discharge and reduced the peak water levels (flood hazards), resulting in similar water levels or hydraulic equilibrium in the two rivers. There exists a critical flow fraction—about 75.9% (West River)—at which the incoming flow from both rivers presents similar water levels at the SXJ node, resulting in little flood diversion. Above the threshold, the flood water will divert from the West River to the North River with a maximum rate of −11,900 m³/s, accounting for 20% of the West River, reducing the peak water level up to 1.48 m at Makou. Below the threshold, the flood water will divert from the North River to the West River with a maximum rate of 11,990 m³/s, accounting for 55% of the North River, reducing the peak water level up to 6.63 m at Sanshui. Meanwhile, the discharge fraction at downstream Makou (Sanshui) maintained a near-constant value during individual floods and fluctuated around 76.6% (23.4%). This critical discharge fraction and the analytical approach are of significance in flood-risk management and hydraulic engineering design in the PRD. The concept model of the H-shaped compound river node clearly elucidates the flood diversion mechanism via the lateral SXJ waterway and may work for other similar river nodes as well. Full article

(This article belongs to the Special Issue A Safer Future—Prediction of Water-Related Disasters)

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17 pages, 5030 KiB

Open AccessArticle

Medium Term Streamflow Prediction Based on Bayesian Model Averaging Using Multiple Machine Learning Models

by Feifei He, Hairong Zhang, Qinjuan Wan, Shu Chen and Yuqi Yang

Water 2023, 15(8), 1548; https://doi.org/10.3390/w15081548 - 14 Apr 2023

Cited by 2 | Viewed by 2143

Abstract

Medium-term hydrological streamflow forecasting can guide water dispatching departments to arrange the discharge and output plan of hydropower stations in advance, which is of great significance for improving the utilization of hydropower energy and has been a research hotspot in the field of [...] Read more.

Medium-term hydrological streamflow forecasting can guide water dispatching departments to arrange the discharge and output plan of hydropower stations in advance, which is of great significance for improving the utilization of hydropower energy and has been a research hotspot in the field of hydrology. However, the distribution of water resources is uneven in time and space. It is important to predict streamflow in advance for the rational use of water resources. In this study, a Bayesian model average integrated prediction method is proposed, which combines artificial intelligence algorithms, including long-and short-term memory neural network (LSTM), gate recurrent unit neural network (GRU), recurrent neural network (RNN), back propagation (BP) neural network, multiple linear regression (MLR), random forest regression (RFR), AdaBoost regression (ABR) and support vector regression (SVR). In particular, the simulated annealing (SA) algorithm is used to optimize the hyperparameters of the model. The practical application of the proposed model in the ten-day scale inflow prediction of the Three Gorges Reservoir shows that the proposed model has good prediction performance; the Nash–Sutcliffe efficiency NSE is 0.876, and the correlation coefficient r is 0.936, which proves the accuracy of the model. Full article

(This article belongs to the Special Issue A Safer Future—Prediction of Water-Related Disasters)

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17 pages, 7789 KiB

Open AccessArticle

Remote Sensing with UAVs for Modeling Floods: An Exploratory Approach Based on Three Chilean Rivers

by Robert Clasing, Enrique Muñoz, José Luis Arumí, Diego Caamaño, Hernán Alcayaga and Yelena Medina

Water 2023, 15(8), 1502; https://doi.org/10.3390/w15081502 - 12 Apr 2023

Cited by 3 | Viewed by 2004

Abstract

The use of unmanned aerial vehicles (UAVs) has been steadily increasing due to their ability to acquire high-precision ground elevation information at a low cost. However, these devices have limitations in estimating elevations of the water surface and submerged terrain (i.e., channel bathymetry). [...] Read more.

The use of unmanned aerial vehicles (UAVs) has been steadily increasing due to their ability to acquire high-precision ground elevation information at a low cost. However, these devices have limitations in estimating elevations of the water surface and submerged terrain (i.e., channel bathymetry). Therefore, the creation of a digital terrain model (DTM) using UAVs in low-water periods means a greater dry channel surface area and thus reduces the lack of information on the wet area not appropriately measured by the UAV. Under such scenarios, UAV-DTM-derived data present an opportunity for practical engineering in estimating floods; however, the accuracy of estimations against current methods of flood estimations and design needs to be measured. The objective of this study is therefore to develop an exploratory analysis for the creation of hydraulic models of river floods using only UAV-derived topographic information. Hydraulic models were constructed based on DTMs created in (i) the traditional manner, considering the bathymetry measured with RTK-GPS and topography, and via (ii) remote sensing, which involves topography measurement with a UAV and assumes a flat bed in the part of the channel covered by water. The 1D steady-state HEC-RAS model v.5.0.3 was used to simulate floods at different return periods. The applied methodology allows a slightly conservative, efficient, economical, and safe approach for the estimation of floods in rivers, with an RMSE of 6.1, 11.8 and 12.6 cm for the Nicodahue, Bellavista and Curanilahue rivers. The approach has important implications for flood studies, as larger areas can be surveyed, and cost- and time-efficient flood estimations can be performed using affordable UAVs. Further research on this topic is necessary to estimate the limitations and precision in rivers with different morphologies and under different geographical contexts. Full article

(This article belongs to the Special Issue A Safer Future—Prediction of Water-Related Disasters)

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14 pages, 5540 KiB

Open AccessArticle

Mutation Characteristics of Precipitation Concentration Spatiotemporal Variation and Its Potential Correlation with Low-Frequency Climate Factors in the LRB Area from 1960 to 2020

by Lu Zhang, Qing Cao and Kanglong Liu

Water 2023, 15(5), 955; https://doi.org/10.3390/w15050955 - 1 Mar 2023

Cited by 3 | Viewed by 1950

Abstract

The precipitation conce ntration degree (PCD) and precipitation concentration period (PCP) in the Liaohe River basin (LRB) from 1960 to 2020 were calculated depending on the daily precipitation data derived from meteorological stations. The mutations of the PCD and PCP were identified by [...] Read more.

The precipitation conce ntration degree (PCD) and precipitation concentration period (PCP) in the Liaohe River basin (LRB) from 1960 to 2020 were calculated depending on the daily precipitation data derived from meteorological stations. The mutations of the PCD and PCP were identified by sliding t-test, and spatiotemporal evolution characteristics before and after the mutation point were further analyzed. Cross wavelet transform (CWT) was used to reveal the influence of four low-frequency climate factors (Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), El Niño -Southern Oscillation (ENSO), and Sunspots (SS)) on precipitation concentration. The results were presented as follows: Mutations occurred in the PCD sequence in 1980 and the PCP sequence in 2005 in the LRB. Spatial distribution of the PCD generally increased from the southeast to the northwest and tended to flatten. Over the past 60 years, the annual PCD tended to decrease, with a variation range of 0.53 to 0.80. The PCP was relatively concentrated in early July to early August, decreasing before and increasing after the mutation. Important climatic factors driving the mutation of PCD included PDO, SS, and AO. However, the resonance between climate factors and the PCD was characterized by complexity and diversity. The PCP was mainly affected by AO and SS before the mutation. ENSO had an important influence on both PCD and PCP, but had no significant correlation with mutation occurrence. Full article

(This article belongs to the Special Issue A Safer Future—Prediction of Water-Related Disasters)

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12 pages, 13000 KiB

Open AccessArticle

Development of Technology for Identification of Climate Patterns during Floods Using Global Climate Model Data with Convolutional Neural Networks

by Jaewon Jung and Heechan Han

Water 2022, 14(24), 4045; https://doi.org/10.3390/w14244045 - 12 Dec 2022

Viewed by 1591

Abstract

Given the increasing climate variability, it is becoming difficult to predict flooding events. We may be able to manage or even prevent floods if detecting global climate patterns, which affect flood occurrence, and using them to make predictions are possible. In this study, [...] Read more.

Given the increasing climate variability, it is becoming difficult to predict flooding events. We may be able to manage or even prevent floods if detecting global climate patterns, which affect flood occurrence, and using them to make predictions are possible. In this study, we developed a deep learning-based model to learn climate patterns during floods and determine flood-induced climate patterns using a convolutional neural network. We used sea surface temperature anomaly as the learning data, after classifying them into four cases according to the spatial extent. The flood-induced climate pattern identification model showed an accuracy of ≥89.6% in all cases, indicating its application for the determination of patterns. The obtained results can help predict floods by recognizing climate patterns of flood precursors and be insightful to international cooperation projects based on global climate data. Full article

(This article belongs to the Special Issue A Safer Future—Prediction of Water-Related Disasters)

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12 pages, 3015 KiB

Open AccessArticle

Flood Scenario Simulation, Based on the Hydrological and Hydrodynamic Model in the Puyang River Catchment

by Hua Zhong, Bing Zhang, Ting Ma, Xinlong Qi, Xuying Wang, Hualing Shang, Simin Qu and Weihua Fang

Water 2022, 14(23), 3873; https://doi.org/10.3390/w14233873 - 27 Nov 2022

Cited by 4 | Viewed by 2128

Abstract

The topography of the Puyang River catchment is complex and includes hills and plains. The Zhuji basin in the middle reaches a densely populated area facing serious flooding issues, due to the upstream flooding and downstream backwater. To address the problem, this study [...] Read more.

The topography of the Puyang River catchment is complex and includes hills and plains. The Zhuji basin in the middle reaches a densely populated area facing serious flooding issues, due to the upstream flooding and downstream backwater. To address the problem, this study applies the Xin’anjiang hydrological model and IFMS 1D-2D hydrodynamic model, to simulate flood scenarios. The simulation results demonstrated that the hydrological model and the -hydrodynamic model together are a feasible tool to monitor the flooding process in the Puyang River catchment. In addition, different flood scenarios which consider disaster-inducing factors and flood control operations are simulated by the model. Reasonable solutions are analyzed for the local flood management. Full article

(This article belongs to the Special Issue A Safer Future—Prediction of Water-Related Disasters)

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17 pages, 14972 KiB

Open AccessArticle

Integrated Risk Assessment of Waterlogging in Guangzhou Based on Runoff Modeling, AHP, GIS and Scenario Analysis

by Shuai Xie, Wan Liu, Zhe Yuan, Hongyun Zhang, Hang Lin and Yongqiang Wang

Water 2022, 14(18), 2899; https://doi.org/10.3390/w14182899 - 16 Sep 2022

Cited by 11 | Viewed by 2299

Abstract

Among the various natural disasters encountered by cities, rainstorm waterlogging has become a serious disaster, affecting the sustainable development of cities. Taking Guangzhou as the research object, based on disaster system theory and risk triangle theory, the evaluation framework “risk of hazard causing [...] Read more.

Among the various natural disasters encountered by cities, rainstorm waterlogging has become a serious disaster, affecting the sustainable development of cities. Taking Guangzhou as the research object, based on disaster system theory and risk triangle theory, the evaluation framework “risk of hazard causing factors—sensitivity of disaster environment—vulnerability of hazard bearing body” was selected to construct the waterlogging risk assessment model of Guangzhou. The weighted comprehensive evaluation method (AHP) was used to determine the index weight, and the rainfall runoff inundation range under different rainstorm scenarios was deduced through a Soil Conservation Service (SCS) runoff generation model and GIS local equal volume passive inundation simulation. The results show that when the precipitation in 2 h is less than 100 mm, the inundation range increases by 3.4 km² for every 10 mm increase in precipitation; When the precipitation in 2 h is greater than 100 mm, the inundation range will increase by 18 km² for every 10 mm increase in precipitation. The total area of medium and high flood risk in Guangzhou is 441.3 km², mainly concentrated in Yuexiu District, Liwan District, Haizhu District and Tianhe District. Full article

(This article belongs to the Special Issue A Safer Future—Prediction of Water-Related Disasters)

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16 pages, 6662 KiB

Open AccessArticle

Intelligent Prediction Method for Waterlogging Risk Based on AI and Numerical Model

by Yuanyuan Liu, Yesen Liu, Jingwei Zheng, Fuxin Chai and Hancheng Ren

Water 2022, 14(15), 2282; https://doi.org/10.3390/w14152282 - 22 Jul 2022

Cited by 6 | Viewed by 2512

Abstract

Numerical simulation models are commonly used to analyze and simulate urban waterlogging risk. However, the computational efficiency of numerical models is too low to meet the requirements of urban emergency management. In this study, a new method was established by combining a long [...] Read more.

Numerical simulation models are commonly used to analyze and simulate urban waterlogging risk. However, the computational efficiency of numerical models is too low to meet the requirements of urban emergency management. In this study, a new method was established by combining a long short-term memory neural network model with a numerical model, which can quickly predict the waterlogging depth of a city. First, a numerical model was used to simulate and calculate the ponding depth of each ponding point under different rainfall schemes. Using the simulation results as training samples, the long short-term memory neural network was trained to predict and simulate the waterlogging process. The results showed that the proposed “double model” prediction model appropriately reflected the relationship between the changes in waterlogging depth and the temporal and spatial changes in rainfall, and the accuracy and speed of computation were higher than those of the numerical model alone. The simulation speed of the “double model” was 324,000 times that of the numerical model alone. The proposed “double model” method provides a new idea for the application of artificial intelligence technology in the field of disaster prevention and reduction. Full article

(This article belongs to the Special Issue A Safer Future—Prediction of Water-Related Disasters)

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16 pages, 4654 KiB

Open AccessArticle

Urban Pluvial Flood Modeling by Coupling Raster-Based Two-Dimensional Hydrodynamic Model and SWMM

by Quntao Yang, Zheng Ma and Shuliang Zhang

Water 2022, 14(11), 1760; https://doi.org/10.3390/w14111760 - 30 May 2022

Cited by 10 | Viewed by 3900

Abstract

Urban flood modeling usually involves simulating drainage network runoff and overland flow. We describe a method for urban pluvial flood modeling by coupling the stormwater management model (SWMM) with a raster-based 2D hydrodynamic model, which is based on a simplified form of the [...] Read more.

Urban flood modeling usually involves simulating drainage network runoff and overland flow. We describe a method for urban pluvial flood modeling by coupling the stormwater management model (SWMM) with a raster-based 2D hydrodynamic model, which is based on a simplified form of the shallow water equations. Then, the method is applied to a highly urbanized area in Nanjing City, China. The elevation of the raster-based 2D hydrodynamic model shows that the raster-based model has comparable capabilities to LISFLOOD-FP for surface flood modeling. The calibration and validation results of the coupled model show that the method is reliable. Moreover, simulation results under the six rainfall return periods, which include 1-, 5-, 10-, 20-, 50-, and 100-year return periods show that node overflow, water depth, and flooding area increase proportionately as the intensity of rainfall increases. Therefore, the coupling model provides a simplified and intuitive method for urban pluvial flood modeling, which can be used to detect flood-sensitive areas and elevate the capacity of urban drainage networks for urban pluvial flooding. Full article

(This article belongs to the Special Issue A Safer Future—Prediction of Water-Related Disasters)

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