3,922 research outputs found
Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management
The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management
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Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information
The complex temporal heterogeneity of rainfall coupled with mountainous physiographic context makes a great challenge in the development of accurate short-term rainfall forecasts. This study aims to explore the effectiveness of multiple rainfall sources (gauge measurement, and radar and satellite products) for assimilation-based multi-sensor precipitation estimates and make multi-step-ahead rainfall forecasts based on the assimilated precipitation. Bias correction procedures for both radar and satellite precipitation products were first built, and the radar and satellite precipitation products were generated through the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), respectively. Next, the synthesized assimilated precipitation was obtained by merging three precipitation sources (gauges, radars and satellites) according to their individual weighting factors optimized by nonlinear search methods. Finally, the multi-step-ahead rainfall forecasting was carried out by using the adaptive network-based fuzzy inference system (ANFIS). The Shihmen Reservoir watershed in northern Taiwan was the study area, where 641 hourly data sets of thirteen historical typhoon events were collected. Results revealed that the bias adjustments in QPESUMS and PERSIANN-CCS products did improve the accuracy of these precipitation products (in particular, 30-60% improvement rates for the QPESUMS, in terms of RMSE), and the adjusted PERSIANN-CCS and QPESUMS individually provided about 10% and 24% contribution accordingly to the assimilated precipitation. As far as rainfall forecasting is concerned, the results demonstrated that the ANFIS fed with the assimilated precipitation provided reliable and stable forecasts with the correlation coefficients higher than 0.85 and 0.72 for one- and two-hour-ahead rainfall forecasting, respectively. The obtained forecasting results are very valuable information for the flood warning in the study watershed during typhoon periods. © 2013 Elsevier B.V
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Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm
Reservoirs and dams are vital human-built infrastructures that play essential roles in flood control, hydroelectric power generation, water supply, navigation, and other functions. The realization of those functions requires efficient reservoir operation, and the effective controls on the outflow from a reservoir or dam. Over the last decade, artificial intelligence (AI) techniques have become increasingly popular in the field of streamflow forecasts, reservoir operation planning and scheduling approaches. In this study, three AI models, namely, the backpropagation (BP) neural network, support vector regression (SVR) technique, and long short-term memory (LSTM) model, are employed to simulate reservoir operation at monthly, daily, and hourly time scales, using approximately 30 years of historical reservoir operation records. This study aims to summarize the influence of the parameter settings on model performance and to explore the applicability of the LSTM model to reservoir operation simulation. The results show the following: (1) for the BP neural network and LSTM model, the effects of the number of maximum iterations on model performance should be prioritized; for the SVR model, the simulation performance is directly related to the selection of the kernel function, and sigmoid and RBF kernel functions should be prioritized; (2) the BP neural network and SVR are suitable for the model to learn the operation rules of a reservoir from a small amount of data; and (3) the LSTM model is able to effectively reduce the time consumption and memory storage required by other AI models, and demonstrate good capability in simulating low-flow conditions and the outflow curve for the peak operation period
Flood Forecasting Using Machine Learning Methods
This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate
A Systematic Review of Real-time Urban Flood Forecasting Model in Malaysia and Indonesia -Current Modelling and Challenge
Several metropolitan areas in tropical Southeast Asia, mainly in Malaysia and Indonesia have lately been witnessing unprecedentedly severe flash floods owing to unexpected climate change. The fast water flooding has caused extraordinarily serious harm to urban populations and social facilities. In addition, urban Southeast Asia generally has insufficient capacity in drainage systems, complex land use patterns, and a largely susceptible population in confined urban regions. To lower the urban flood risk and strengthen the resilience of vulnerable urban populations, it has been of fundamental relevance to create real-time urban flood forecasting systems for flood disaster prevention agencies and the urban public. This review examined the state-of-the-art models of real-time forecasting systems for urban flash floods in Malaysia and Indonesia. The real-time system primarily comprises the following subsystems, i.e., rainfall forecasting, drainage system modeling, and inundation area mapping. This review described the current urban flood forecasting modeling for rainfall forecasting, physical-process-based hydraulic models for flood inundation prediction, and data-driven artificial intelligence (AI) models for the real-time forecasting system. The analysis found that urban flood forecasting modeling based on data-driven AI models is the most applied in many metropolitan locations in Malaysia and Indonesia. The analysis also evaluated the existing potential of data-driven AI models for real-time forecasting systems as well as the challenges towards i
Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan
Multi-step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3-hours warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context makes the development of real-time rainfall-runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3-hours. In this paper we develop a novel semi-distributed, data-driven, rainfall-runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network-based Fuzzy Inference System solutions is created using various combinations of auto-regressive, spatially-lumped radar and point-based rain gauge predictors. Different levels of spatially-aggregated radar-derived rainfall data are used to generate 4, 8 and 12 sub-catchment input drivers. In general, the semi-distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead-times greater than 3-hours. Performance is found to be optimal when spatial aggregation is restricted to 4 sub-catchments, with up to 30% improvements in the performance over lumped and point-based models being evident at 5-hour lead times. The potential benefits of applying semi-distributed, data-driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, is thus demonstrated
Supervised intelligent committee machine method for hydraulic conductivity estimation
Hydraulic conductivity is the essential parameter for groundwater modeling and management. Yet estimation of hydraulic conductivity in a heterogeneous aquifer is expensive and time consuming. In this study; artificial intelligence (AI) models of Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Multilayer Perceptron Neural Network associated with Levenberg-Marquardt (ANN), and Neuro-Fuzzy (NF) were applied to estimate hydraulic conductivity using hydrogeological and geoelectrical survey data obtained from Tasuj Plain Aquifer, Northwest of Iran. The results revealed that SFL and NF produced acceptable performance while ANN and MFL had poor prediciton. A supervised intelligent committee machine (SICM), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of the hydraulic conductivity in Tasuj plain. The performance of SICM was also compared to those of the simple averaging and weighted averaging intelligent committee machine (ICM) methods. The SICM model produced reliable estimates of hydraulic conductivity in heterogeneous aquifers
IMPACTS OF URBAN DEVELOPMENT PATTERN ON RUNOFF PEAK FLOWS AND STREAMFLOW FLASHINESS OF PERI-URBAN CATCHMENTS: ASSESSING THE PERFORMANCE OF PHYSICAL AND DATA-DRIVEN MODELS FOR REAL-TIME ENSEMBLE FLOOD FORECASTING
Urban growth is a global phenomenon, and the associated impacts on hydrology from land development are expected to increase, especially in peri-urban catchments, which are newly developing catchments in proximity of growing cities. In northern climates, hydrologic response of peri-urban catchments change with the water budget and climatic conditions. As a result, runoff response of northern peri-urban catchments can vary immensely across seasons. During warm seasons, the evapotranspiration (ET) and infiltration rates are high, so urban floods are expected to occur during high intensity, low duration storm events. During cold seasons and below freezing temperatures, surficial soils are typically frozen and nearly impervious. In addition, the ET rate is low throughout winter. Therefore, the difference in runoff response between peri-urban and natural catchments is least in winter. Furthermore, winter snow redistribution by plowing and endogenous urban heat affect the snowmelt timing and frequency. Due to the limited availability of data on snow removal and redistribution activities in northern peri-urban catchments, cold-season hydrologic modeling for peri-urban catchments remains a challenging task in urban hydrology.
Research on the cold season hydrologic response of peri-urban catchments are mostly limited to Finland, Sweden, and Canada. The resulting research gap on seasonal change in hydrologic response of peri-urban catchments is common to many northern settings. In the first phase of this study, I use intensive discharge monitoring records at several peri-urban catchments near Syracuse, NY to calculate and compare seasonal runoff peak flows among several peri-urban catchments. These are selected to provide a range of drainage area and imperviousness to clarify the impact of urban development and catchment size on seasonal hydrologic behavior of peri-urban catchments.
It is well understood that greater peak flows and higher stream flashiness are associated with increased surface imperviousness and storm location. However, the effect of the distribution of impervious areas on runoff peak flow response and stream flashiness of peri-urban catchments has not been well studied. In the second phase of this dissertation, I define a new geometric index, Relative Nearness of Imperviousness to the Catchment Outlet (RNICO), to correlate imperviousness distribution of peri-urban catchments with runoff peak flows and stream flashiness. The study sites for this phase of the study include ninety peri-urban catchments in proximity of 9 large US cities: New York, NY (NYC), Syracuse, NY, Baltimore, MD, Portland, OR, Chicago, IL, Austin, TX, Houston, TX, San Francisco, CA, and Los Angeles, CA. Based on RNICO, all development patterns are divided into 3 classes: upstream, centralized, and downstream. Analysis results showed an obvious increase in runoff peak flows and decrease in time to peak as the centroid of imperviousness moves downstream. This indicates that RNICO is an effective tool for classifying urban development patterns and for macroscale understanding of the hydrologic behavior of small peri-urban catchments, despite the complexity of urban drainage systems. Results for nine cities show strong positive correlations between RNICO and runoff peak flows and stream flashiness index for small peri-urban catchments. However, the area threshold used to distinguish small and large catchments differs slightly by location. For example, for Chicago, IL, NYC, NY, Baltimore, MD, Houston, TX, and Austin, TX area threshold values of 55, 40, 50, 42, and 32 km2 emerged, runoff peak flows in catchments with drainage area below these values were positively correlated to RNCIO. This first phase of this study suggests that RNICO is a stronger predictor of runoff peak flow and stream-flow regime in humid northern and southern US study sites, compared to more arid western US study sites. This difference is likely due to the greater precipitation rates and greater antecedent soil moisture contents for humid climates. The extent of urban infrastructure is less likely to control the effectiveness of RNICO for predicting runoff peak flows and R-B flashiness index for the selected study sites, due to the relatively similar urban development level within the peri-urban study catchments.
Consistent forecast of peak flows across scales in flood hydrographs remains a challenge for most hydrologic models. Urbanization increases the magnitude and frequency of peak flows, often challenging the forecast ability for real-time flood prediction. Following advances in satellite and ground-based meteorological observations, global and continental real-time ensemble flood forecasting systems use a variety of physical hydrology models to predict urban peak flows. Artificial intelligence (AI) models provide an alternative approach to physical hydrology models for real-time flood forecasting. Despite recent advances in AI techniques for hydrologic prediction, ensemble stream-flow prediction by these methods has been limited. In addition, application of AI models for flood forecasting has been limited to large river basins, with very limited research on use of AI models for small peri-urban catchments. Flood forecasting in small urban catchments can be a critical task to urban safety due to the short time of concentration and quick precipitation runoff response. AI flood forecasting models typically apply upstream streamflow measurements to forecast downstream flood discharge. Therefore, the storm direction may change the flood travel time and time to peak, which challenges accurate flood forecasting. For example, if the storm direction is upstream through an AI model trained on the upstream gage data may fail to accurately predict peak flow magnitude and timing, at the outlet, this is due to the quicker runoff response of the downstream gage compared to the upstream station. There has been very limited focus on the impact of storm direction on peak flow response of urban catchments and available literature are limited to lab-scale prototypes and rainfall simulators. These may not fully represent real-world flooding scenarios. Therefore, the impact of storm direction on flood forecasting performance of peri-urban catchments is another important research gap in real-time urban flood forecasting.
In the third phase of my dissertation project, I initially assess the impact of storm direction on the flood forecasting performance of an Adaptive Neuro Fuzzy Inference System (ANFIS) at a peri-urban catchment in proximity of Syracuse, NY. Next, I compare the relative utility of physical hydrology and AI approaches to predict flood hydrograph in peri-urban catchments. For this comparison, I selected ANFIS, and Sacramento Soil Moisture Accounting Model (SAC-SMA) for real-time ensemble re-forecasting of streamflow in several small to medium size suburban catchments near NYC for Hurricane Irene and a smaller storm event. The SAC-SMA model is a physical hydrology model that was initially developed by Burnash et al. (1973). The National Oceanic and Atmospheric Administration (NOAA) selected the SAC-SMA lumped model as a comparison baseline for participating distributed hydrologic models in the Distributed Model Intercomparison Project (DMIP), which aimed to identify the most suitable model for National Weather Service (NWS) streamflow prediction across the US (http://www.nws.noaa.gov/ohd/hrl/dmip/). More importantly, the NWS is currently using the lumped form of SAC-SMA for ensemble flood forecasting across the US (Emerton et al., 2016). For these reasons, I chose to employ a lumped version of SAC-SMA in my dissertation project. SAC-SMA performed well for both large and small events and for lead times of three to 24 hours, but ANFIS predicted the Hurricane Irene flood discharge well only for short lead times in small study catchments. ANFIS had reasonable percent bias (PBIAS) for predicting the small storm event for all lead times, indicating the utility of ANFIS for small events. In addition, the accuracy of both SAC-SMA and ANFIS models for ensemble flood prediction did not change significantly with catchment size and imperviousness. Overall, results of the third phase of this study suggest that the lumped SAC-SMA model may be a reliable option for local urban flood forecasting for evacuation plan lead time up to 24 hours. Due to the uncertainties in future climatic conditions, my study emphasizes the importance of using physical hydrology models for real-time flood forecasting of large events in small urban catchments. This recommendation is based on the finding that the performance of data-driven models may greatly decrease with the storm scale if the training period includes storms of magnitude less than storms in the validation period
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Three decades of the Shuffled Complex Evolution (SCE-UA) optimization algorithm: Review and applications
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