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Critical infrastructure systems are typically managed as isolated systems, however, past events have demonstrated that disruptions to electricity supply can initiate major social and economic impac...

This study is motivated by the potential improvement in water supply reliability and better forecasts of extreme rainfall and floods linked to Atmospheric rivers (ARs) in New Zealand. Results indic...

The headwater catchment of the Siem Reap River has supplied the Angkor Temple Complex and surrounding communities since the twelfth century. The Angkor Temple Complex area consists of historical moats and barays (reservoirs) which are currently used to store the water from the Siem Reap River to maintain temple foundation, irrigate cultivation areas and provide floodwater storage. The Angkor Wat Temple, which is located in the complex, was constructed on a sandy alluvial substrate and needs a stable supply of water to avert land subsidence and destabilization of the temple foundation. In light of changing climate, land use and land cover (LULC) trends, it is crucial to examine the wide-ranging implications of reduced water supply for the Angkor Temple Complex. Using the Soil and Water Assessment Tool, this study seeks to assess the conditions necessary to provide sustainable streamflow to the Angkor Temple Complex. We modelled 30 scenarios of co-varied LULC and precipitation regime under a changing climate. The results show that under most LULC scenarios, sufficient water resources can be harvested to supply the complex—however—any further loss of forest cover is likely to impact groundwater conditions, flood management and dry season shortages. Conversely, the water supply to the complex is shown to be sensitive under the range of climate scenarios explored; a reduction of more than 10–20% in mean annual precipitation was enough to put the water supply under stress for the current and future conditions of the complex.

Scour at bridge piers is time-dependent. In this paper, temporal evolution of clear-water scour at complex bridge piers is studied experimentally. The pier model has a typical form comprisi...

Considering network topologies and structures of the artificial neural network (ANN) used in the field of hydrology, one can categorize them into two different generic types: feedforward and feedback (recurrent) networks. Different types of feedforward and recurrent ANNs are available, but multilayer perceptron type of feedforward ANN is most commonly used in hydrology for the development of wavelet coupled neural network (WNN) models. This study is conducted to compare performance of the various wavelet based feedforward artificial neural network (ANN) models. The feedforward ANN types used in the study include the multilayer perceptron neural network (MLPNN), generalized feedforward neural network (GFFNN), radial basis function neural network (RBFNN), modular neural network (MNN) and neuro-fuzzy neural network (NFNN) models. The rainfall-runoff data of four catchments located in different hydro-climatic regions of the world is used in the study. The discrete wavelet transformation (DWT) is used in the present study to decompose input rainfall data using db8 wavelet function. A total of 220 models are developed in this study to evaluate the performance of various feedforward neural network models. Performance of the developed WNN models is compared with their counterpart simple models developed without applying wavelet transformation (WT). The results of the study are further compared with - multiple linear regression (MLR) model which suggest that the WNN models outperformed their counterpart simple models. The hybrid wavelet models developed using MLPNN, the GFFNN and the MNN models performed best among the six selected data driven models explored in the study. Moreover, performance of the three best models is found to be similar and thus the hybrid wavelet GFFNN and the MNN models can be considered as an alternative to the most commonly used hybrid WNN models developed using MLPNN. The study further reveals that the wavelet coupled models outperformed their counterpart simple models only with the parsimonious input vector.

Critical infrastructure networks are geographically distributed systems spanning multiple scales. These networks are increasingly interdependent for normal operations, which causes localized asset failures from natural hazards or man-made interference to propagate across multiple networks, affecting those far removed from an initiating failure event. This paper provides methodology to identify such failure propagation effects by quantifying the spatial variability in magnitude, frequency, and disruptive reach of failures across national infrastructure networks. To achieve this, we present methodology to combine functionally interdependent infrastructure networks with geographic interdependencies by simulating complete asset failures across a national scale grid of spatially localized hazards. A range of metrics are introduced to compare the systemic vulnerabilities of infrastructure systems and the resulting spatial variability in both the potential for initiating widespread failures and the risk of being impacted by distant hazards. We demonstrate the approach through an application in New Zealand of infrastructures across the energy (electricity, petroleum supply), water and waste (water supply, wastewater, solid waste), telecommunications (mobile networks), and transportation sectors (passenger rail, ferry, air, and state highways). In addition to identifying nationally significant systemic vulnerabilities, we observe that nearly half (46%) of the total disruptions across the simulation set can be attributed to network propagation initiated asset failures. This highlights the importance in considering interdependencies when assessing infrastructure risks and prioritizing investment decisions for enhancing resilience of national networks.

Cambodia is one of the most vulnerable countries to climate change impacts such as floods and droughts. Study of future climate change and drought conditions in the upper Siem Reap River catchment is vital because this river plays a crucial role in maintaining the Angkor Temple Complex and livelihood of the local population since 12th century. The resolution of climate data from Global Circulation Models (GCM) is too coarse to employ effectively at the watershed scale, and therefore downscaling of the dataset is required. Artificial neural network (ANN) and Statistical Downscaling Model (SDSM) models were applied in this study to downscale precipitation and temperatures from three Representative Concentration Pathways (RCP 2.6, RCP 4.5 and RCP 8.5 scenarios) from Global Climate Model data of the Canadian Earth System Model (CanESM2) on a daily and monthly basis. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were adopted to develop criteria for dry and wet conditions in the catchment. Trend detection of climate parameters and drought indices were assessed using the Mann-Kendall test. It was observed that the ANN and SDSM models performed well in downscaling monthly precipitation and temperature, as well as daily temperature, but not daily precipitation. Every scenario indicated that there would be significant warming and decreasing precipitation which contribute to mild drought. The results of this study provide valuable information for decision makers since climate change may potentially impact future water supply of the Angkor Temple Complex (a World Heritage Site).

Coastal bridges serve as lifelines connecting affected areas to the outside world after extreme events such as tsunamis. It is thus important to predict tsunami loads applied to bridges vulnerable to tsunami attack. Most previous studies have focused on the horizontal force on bridges due to tsunamis. However, the overturning moments applied to bridges also contribute to the flexural failure of the bridge and have not been thoroughly investigated. Continuing efforts should be made to quantify tsunami-induced loads (forces and overturning moments) on coastal bridges. This study presents a series of experiments undertaken to study the tsunami loads on slab bridges using tsunami bores. Apart from the average horizontal pressure (Ph) and the horizontal (Fx) and vertical (Fz) forces, the overturning moments about the base of the pier (Myb) and the pier-deck connection (Myp) were analysed, which are closely related to the flexural failure of bridges in tsunamis. Four different pier heights were tested, as well as seven tsunami bore cases for each bridge set-up. Results show the maximum “area-adjusted momentum flux” (the product of the flow velocity squared and the contact area of the bridge in the direction of the flow) provides a better predictor for the maximum horizontal force than the maximum momentum flux, which has been used to calculate horizontal forces on emergent structures with a uniform width in the vertical direction. When a bridge is fully submerged under a tsunami, the overturning moments Myb and Myp applied to the bridge are largest at a specific pier height. Empirical equations are proposed to calculate the maximum loads (Ph, Fx, Fz, Myb and Myp) at the initial impact stage. The threshold of jump inception when a tsunami impacts a bridge has been discussed.

Studies evaluating the determinants of water demand typically use household-scale data or aggregated data. The household-scale data basically is preferred since it can reveal the heterogeneity in responses to the demand drivers across different consumer groups. However, the scarcity of household-scale data and its high data collection cost generally have limited the studies to rely on small samples of household data. Thus, they failed to show the spatial variation of water demand. In contrast, the aggregated studies have assessed the spatial variation of water use however they overlooked the variations across households. Using a rich source of GIS-based urban databases in Auckland, New Zealand, this study overcame this challenge by developing a large sample of 31000 single-unit housing through integration of household-level water consumption and property data with micro-scale household demographics information. This large dataset enabled this study to evaluate the water consumption both at the household scale and the census area unit scale. Panel data models were used for the water demand analysis in both scales. The proposed multi-scale analysis approach provided detailed knowledge about water consumption and its major determinants across different consumer groups and urban areas. This information may help water planners to more reliably plan water supply systems and manage consumption in the complex urban environments.

Over recent decades Auckland, New Zealand, metropolitan area has vastly expanded as a result of rapid population growth and low-density housing developments. In order to manage the uncontrolled low-density urban sprawl, Auckland Council proposed a compact city model through promoting higher density housing developments. In order to understand the implications of this transition on future residential water demand, this study first evaluated water consumption in three major housing types in Auckland including single houses, low-rise and high-rise apartments. Using the geographic information system, the water consumption information, estimated from a large sample of 60,000 dwellings across Auckland, was subsequently integrated with the Proposed Auckland Unitary Plan outlining the future housing composition over different areas in Auckland. Through developing different growth scenarios, the study showed that the housing transition from single houses to more intensified multi-unit houses cannot considerably af...

This study investigates factors affecting tsunami-borne debris impact force on structures. Debris collision accelerations were measured at the contact point on the structure's seaward wall using disc- and box-shaped smart debris devices; the equation of motion was used to estimate the debris impact forces from the measured accelerations. Also, the impact force was measured at the base of the structure using a multi-axis load cell, and compared with the forces determined using the smart debris devices. The basic impulse-momentum formula was modified by the addition of coefficients, taking into account the added mass and the debris velocity. Debris shape also influenced the impact force. Deformability of the debris and flexibility of the structure both reduced the debris impact force.

Lifeline utilities and critical infrastructures are becoming increasingly interactive and dependent on one another for normal operation. With a natural disaster or disruptive event, these dependencies can be studied under stressed conditions. To replicate events and inform future simulations, such dependencies can be quantified in both magnitude and direction. This paper builds on recent efforts by proposing a new dependency index methodology that gives importance to the direction of dependency between coupled infrastructures and equally weighting the multiple dependencies that may be realized across a variety of lag times. The effectiveness of this methodology is presented as a case study for the 22 February 2011 earthquake experienced in Christchurch, New Zealand. Dependencies are quantified for a range of critical infrastructure couplings, which provide insight into the future application of these results and the requirement for integration with qualitative studies to accurately inform interdependency models.

Summary In order to predict runoff accurately from a rainfall event, the multilayer perceptron type of neural network models are commonly used in hydrology. Furthermore, the wavelet coupled multilayer perceptron neural network (MLPNN) models has also been found superior relative to the simple neural network models which are not coupled with wavelet. However, the MLPNN models are considered as static and memory less networks and lack the ability to examine the temporal dimension of data. Recurrent neural network models, on the other hand, have the ability to learn from the preceding conditions of the system and hence considered as dynamic models. This study for the first time explores the potential of wavelet coupled time lagged recurrent neural network (TLRNN) models for runoff prediction using rainfall data. The Discrete Wavelet Transformation (DWT) is employed in this study to decompose the input rainfall data using six of the most commonly used wavelet functions. The performance of the simple and the wavelet coupled static MLPNN models is compared with their counterpart dynamic TLRNN models. The study found that the dynamic wavelet coupled TLRNN models can be considered as alternative to the static wavelet MLPNN models. The study also investigated the effect of memory depth on the performance of static and dynamic neural network models. The memory depth refers to how much past information (lagged data) is required as it is not known a priori. The db8 wavelet function is found to yield the best results with the static MLPNN models and with the TLRNN models having small memory depths. The performance of the wavelet coupled TLRNN models with large memory depths is found insensitive to the selection of the wavelet function as all wavelet functions have similar performance.

Tsunamis in Indonesia (2004) and Japan (2011) severely affected communities bordering the Indian Ocean and Japan's east coast. Despite the plausible findings arising from the available studies, the research on the impact of tsunami bore forces applied to bridges of different designs is limited. This is because the estimation methods for tsunami-induced loads presented in the literature have mainly been developed for buildings and require careful adaptation to apply to bridges. The main focus of the present study is to experimentally examine the interaction between a tsunami bore and a box section bridge with different orientations to the direction of the incoming bore, bore strengths, and deck clearances. The experiments were conducted in a 15-m-long, 1.2-m-wide, and 1.2-m-deep wave flume. The forces and pressures applied to the bridge, the bore heights, and the velocities were measured. It was found that the skewed bridge deck is subjected to additional force and moment components, namely the cross-stream force (Fy) and the rolling and yawing moments (Mx and Mz). These components are non-existent for the unskewed bridge deck. Based on the experimental results, equations were proposed for estimating tsunami forces for a box section bridge deck.

The use of wavelet-coupled data-driven models is increasing in the field of hydrological modelling. However, wavelet-coupled artificial neural network (ANN) models inherit the disadvantages of containing more complex structure and enhanced simulation time as a result of use of increased multiple input sub-series obtained by the wavelet transformation (WT). So, the identification of dominant wavelet sub-series containing significant information regarding the hydrological system and subsequent use of those dominant sub-series only as input is crucial for the development of wavelet-coupled ANN models. This study is therefore conducted to evaluate various approaches for selection of dominant wavelet sub-series and their effect on other critical issues of suitable wavelet function, decomposition level and input vector for the development of wavelet-coupled rainfall-runoff models. Four different approaches to identify dominant wavelet sub-series, ten different wavelet functions, nine decomposition levels, and five different input vectors are considered in the present study. Out of four tested approaches, the study advocates the use of relative weight analysis (RWA) for the selection of dominant input wavelet sub-series in the development of wavelet-coupled models. The db8 and the dmey (Discrete approximation of Meyer) wavelet functions at level nine were found to provide the best performance with the RWA approach.

The restoration of lifeline infrastructures following a major disruptive disaster is a complex task. Along with the implementation of mitigation measures, pre-event recovery planning can be of great assistance to this process. This paper seeks to inform such planning discussions by suggesting likely paths of recovery over time, and in turn computing indicative estimates of expected restoration times. While current methods can require significant amounts of data and are calibrated to few events, the presented approach analyses and combines 63 electricity, water, gas, and telecommunications post-disaster infrastructure recoveries from across the world. Recoveries are compared across disaster types with global median recovery curves produced to inform likely restoration rates for future disasters. Models based on initial outages or seismic shaking intensity directly provide estimates of expected recovery times back to 90% operability of the initial disruption. An application of the presented methodology is presented as a case study for the Wellington Region of New Zealand with recovery estimates comparing favorably with those presented in the literature.

Summary This study presents a novel approach of using the hybrid Wavelet Gene Expression Programming (WGEP) model to forecast the runoff using rainfall data. The rainfall-runoff data from four different catchments located in the different Hydro-Climatic regions of the world is used in the study. The WGEP model is developed by integrating the discrete wavelet transform (DWT) with the Gene Expression Programming (GEP) models so that individual strengths of each approach can be exploited in a synergistic manner. It is implemented by transforming the input rainfall data using the DWT in order to reveal the temporal and the spectral information contained in the data and subsequently this transformed data is used as input for the GEP models. Ten different mother wavelet functions from different wavelet families are employed in the study in order to transform the data. The mother wavelet functions used in the study include the simple mother wavelet function Haar, the db2, db4, db8 wavelet functions of the most popular wavelet family Daubechies, sym2, sym4, sym8 wavelet functions with sharp peaks, coif2, coif4 wavelet functions and the discrete approximation of meyer (dmey) wavelet. The study further investigates the selection of the optimum input vector to be used in conjunction with the WGEP models by considering nine different input vectors. The first five input vectors contain only one rainfall data series ranging from lagged-1 day to lagged-5 day rainfall data series. The remaining four input vectors are selected using the most common approach in which selection of the input vector comprising of the sequential time series data. The performance of the hybrid WGEP models are compared with the simple GEP models developed without the wavelet transformation for the four selected catchments. The study found that the performance of the WGEP models is superior relative to the simple GEP model for all the nine input vectors considered. However, the WGEP models outperformed their respective simple GEP models only for the first five input vectors. Furthermore, the WGEP models exhibits better results with the dmey wavelet function for all the four catchments during both training and testing.

Baffles have been utilised in the ponds and wetland to improve the rate of treatment for the polluted water. However, there is limited research in the optimum configuration and type of baffles for sediment retention ponds (SRP). In this study, the effect of porous and submerged solid baffles on the hydraulic performance of a model SRP is assessed. In order to optimise the type and configuration of baffles, several configurations were tested with four different metal meshes (with different aperture size and open area) as porous baffles, and acrylic sheets as solid baffles. The porous baffles were more effective in increasing the retention time and improving the overall hydraulic performance than the solid baffles. The finest mesh with 0.415 mm aperture size and 40% open area had the highest performance for most of the configurations with 3 or less baffles. However, for four and five baffles, the medium-fine mesh with 1 mm aperture size and 42% open area was the best. For three porous baffles, they were more effective when installed in the first half of the pond compared with when installed about the middle point of the pond, regardless of the mesh size. The two porous baffles with same aperture sizes but different open areas had different hydraulic performance which highlights the importance of mesh aperture in addition to the total open area.

Statistical downscaling has become an important part in most of the watershed scale climate change investigations. It is usually performed using multiple regression-based models. Basic working principle of such models is to develop a suitable relationship between the large scale (predictors) and the local climatic parameters called predictands. The development of such relationships using linear regression becomes very challenging when the local parameter to be downscaled is complex in nature such as precipitation. For this reason, use of nonlinear data driven techniques including Artificial Neural Networks (ANNs) is becoming more and more popular. Therefore, an attempt has been made in the study presented here to introduce a new Multi-Layer Perceptron (MLP) ANN-based scheme to develop a robust predictors-predictand relationship to be used as a downscaling model at daily time scale. The efficiency of this model has been compared with a popularly used model called Statistical Down Scaling Model (SDSM), for daily precipitation at the Clutha watershed in New Zealand. The results show that the model developed based on ANN scheme exhibits better performance than the SDSM. Hence, it is concluded that the use of artificial intelligence techniques such as ANN can greatly help in developing more efficient predictor-predictand models for even for precipitation being the toughest climate variable to model

Following a major disaster or disruption, the restoration of infrastructure function is often tracked over time. For a more detailed understanding of the recovery process, this paper separates wastewater recovery into multiple service categories. Each service category is further defined by three distinct levels of service: normal, restricted, and no service provision. This proposed format of tracking system recovery allows the functionality to be defined in more detail than commonly presented metrics in the literature while still being conducive to data collection in the immediate recovery following major disruptions. The proposed methodology is applied to the February 22, 2011 Christchurch (New Zealand) earthquake wastewater system recovery. Through this case study, the complexity of wastewater system recovery is evinced along with the future potential for using the suggested breakdown of service and associated metrics. Future application is not restricted to postevent analyses, but also in guidi...

An investigation of the effect of baffles on retention pond performance using a physical model of an existing sediment retention pond is presented. Analysis of residence time (RTD curves) was used to compare the hydraulic performance of different arrangements of baffles in the pond. Five different arrangements for the design of baffles were studied. The results show that placing a single baffle to deflect the influent to a sediment retention pond does not improve pond performance; rather, it stimulates short-circuiting. This is contradictory to the literature and is considered to be a consequence of the model pond incorporating sloping walls, which is a novel aspect of this study. Most of the previous studies have neglected the effects of battered walls. Conversely, the inclusion of more than two baffles was found to increase the hydraulic performance. The results reported here are limited to small and narrow ponds where a large portion of the pond is batter (i.e. made up of sloping walls). For large area ponds, batter effects may be negligible and are likely to be different from those reported here.

Artificial neural networks (ANNs) are generally regarded to behave as black-box systems. Recent research explores various methods that can provide an insight into the internal connections and relationships existing within the network. Various methodologies that understand the input variable contribution are analyzed in detail, and rule extraction approaches for a trained artificial neural network are addressed. To understand the contribution of input variables to rainfall-runoff model combination systems, this paper for the first time investigates knowledge extraction from artificial neural network, which is used to combine the results obtained from different competing rainfall-runoff models, using three different approaches: (1) Garson’s algorithm; (2) neural interpretation diagram (NID); and (3) sensitivity analysis (SA). For the purpose of investigating knowledge extraction techniques, the trained multilayer perceptron neural network to combine the results from four different rainfall-runoff mo...

The use of wavelet transformation in rainfall–runoff modeling has become popular because of its ability to simultaneously deal with both the spectral and the temporal information contained within time series data. The selection of an appropriate wavelet function plays a crucial role for successful implementation of the wavelet based rainfall–runoff artificial neural network models as it can lead to further enhancement in the model performance. The present study is therefore conducted to evaluate the effects of 23 mother wavelet functions on the performance of the hybrid wavelet based artificial neural network rainfall–runoff models. The hybrid Multilayer Perceptron Neural Network (MLPNN) and the Radial Basis Function Neural Network (RBFNN) models are developed in this study using both the continuous wavelet and the discrete wavelet transformation types. The performances of the 92 developed wavelet based neural network models with all the 23 mother wavelet functions are compared with the neural network models developed without wavelet transformations. It is found that among all the models tested, the discrete wavelet transform multilayer perceptron neural network (DWTMLPNN) and the discrete wavelet transform radial basis function (DWTRBFNN) models at decomposition level nine with the db8 wavelet function has the best performance. The result also shows that the pre-processing of input rainfall data by the wavelet transformation can significantly increases performance of the MLPNN and the RBFNN rainfall–runoff models.

In order to explore the influence of morphological characteristics of green patches on its surrounding thermal environment, the remote sensing image data of Taiyuan City was used as the basic data source, and the temperature field under the influence of morphological characteristics of green patches was simulated by CFD technique. The dimensionless parameter of relative temperature change rate was introduced as the index of thermal environment effect. From the existing shape index reflecting the patch characteristics, the indicators of the green patch affecting the surrounding thermal environment were screened out, and the corresponding quantitative relationship between them was established. Based on the statistical analysis of patch shape index and thermal environment effect index, it is pointed out that the deviation degree for equivalent perimeter of patch is positively correlated with the relative change rate of temperature reflecting thermal environment effect. Meanwhile, the smaller area of green space can be used to improve local microclimate. The introduction of CFD technology into the study of urban thermal environment provides a basis for exploring the key parameters to improve the ecological environment benefits of urban green space and determining the shape control index of patch in green space planning and design. On the other hand, the simulation function of CFD technology is used to optimize green space design, which has better significance to guide production practice.

Experimental investigations undertaken to optimize the layout of floating treatment wetlands (FTWs) for maximum hydraulic performance of storm-water retention ponds are described. The study is the first to investigate how the arrangement of an FTW in a storm-water retention pond affects its hydraulic performance. The size, orientation, and arrangement of FTWs were investigated as well as the influence of inlet arrangements on the effects that the FTW has on the system hydraulics. For all the tests, the FTWs were positioned centrally across the width of the pond. The results show that FTWs can significantly improve the hydraulic performance of storm-water retention ponds. The hydraulic performance is shown to depend on the position, size, and placing arrangement of an FTW in the pond as well as the inlet arrangement.

The use of computational fluid dynamics (CFD) as an engineering tool for the design of storm water retention ponds is a rapidly growing area of interest, but there is a large gap in the literature with regard to validating the CFD models against experimental data for investigation of flow patterns and velocity distributions in storm water retention ponds. This paper assesses a CFD model against experimental flow data from a laboratory-scale physical model of an existing field retention pond. The simulated results were compared to each other and also to the experimental data to test the ability of numerical simulations for this type of problem. A representative and realistic range of flow rates from 0.16 to 1.5 L/s was tested in the physical model for comparison with the CFD model. Also, the vorticity from the physical model tests was compared to that from the numerical model to validate the CFD model. The results confirm previous findings that CFD modeling is a potential engineering tool to simul...

The aim of this study is to compare the performance of a symbolic regression combination method based on gene expression programming (GEP) with different neural network combination methods when used in the development of multimodel systems. The two different neural network combination methods used in this study are the multilayer perceptron neural network (MLPNN) and the radial basis function neural network (RBFNN). The methods were used to combine the results from different types of rainfall-runoff models to test the multimodel combination system in catchments located in Thailand and New Zealand. Comparison of the results revealed that the GEP performed better than neural network methods in the case of the catchment located in New Zealand. Nevertheless, the RBFNN method outperformed the GEP and the MLPNN combination method in the case of the catchment located in Thailand. However, which combination method produces better results in the multimodel combination is not conclusive. The results suggest...

 artificial neural networks;  flood routing;  real-time;  stability Abstract Developing a robust flood forecasting and warning system (FFWS) is essential in flood-prone areas. Hydrodynamic models, which are a major part of such systems, usually suffer from computational instabilities and long runtime problems, which are particularly important in real- time applications. In this study, two artificial intelligence models, namely artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), were used for flood routing in an FFWS in Madarsoo river basin, Iran. For this purpose, different rainfall patterns were transformed to run-off hydrographs using the Hydrologic Engineering Center (HEC)-1 hydrological model and routed along the river using HEC river analysis system RAS hydrodynamic model. Then, the simulated hydrographs with different lag times were used as inputs for training of ANN and ANFIS models to simulate flood hydrograph at the basin outlet. Results showed that the simulations obtained from ANN and ANFIS coincided with the results simulated by the HEC-RAS, and application of such models is strongly suggested as a backup tool for flood routing in FFWSs.

When analysing the performance of hydrological models in river forecasting, researchers use a number of diverse statistics. Although some statistics appear to be used more regularly in such analyses than others, there is a distinct lack of consistency in evaluation, making studies undertaken by different authors or performed at different locations difficult to compare in a meaningful manner. Moreover, even within individual reported case studies, substantial contradictions are found to occur between one measure of performance and another. In this paper we examine the ideal point error (IPE) metric – a recently introduced measure of model performance that integrates a number of recognised metrics in a logical way. Having a single, integrated measure of performance is appealing as it should permit more straightforward model inter-comparisons. However, this is reliant on a transferrable standardisation of the individual metrics that are combined to form the IPE. This paper examines one potential option for standardisation: the use of naive model benchmarking.

This paper traces two decades of neural network rainfall-runoff and streamflow modelling, collectively termed ‘river forecasting’. The field is now firmly established and the research community involved has much to offer hydrological science. First, however, it will be necessary to converge on more objective and consistent protocols for: selecting and treating inputs prior to model development; extracting physically meaningful insights from each proposed solution; and improving transparency in the benchmarking and reporting of experimental case studies. It is also clear that neural network river forecasting solutions will have limited appeal for operational purposes until confidence intervals can be attached to forecasts. Modular design, ensemble experiments, and hybridization with conventional hydrological models are yielding new tools for decision-making. The full potential for modelling complex hydrological systems, and for characterizing uncertainty, has yet to be realized. Further gains could also emerge from the provision of an agreed set of benchmark data sets and associated development of superior diagnostics for more rigorous intermodel evaluation. To achieve these goals will require a paradigm shift, such that the mass of individual isolated activities, focused on incremental technical refinement, is replaced by a more coordinated, problem-solving international research body.

This chapter focuses on the development of artificial intelligence based regional flood frequency analysis (RFFA) techniques for Eastern Australia. The techniques considered in this study include artificial neural network (ANN), genetic algorithm based artificial neural network (GAANN), gene-expression programing (GEP) and co-active neuro fuzzy inference system (CANFIS). This uses data from 452 small to medium sized catchments from Eastern Australia. In the development/training of the artificial intelligence based RFFA models, the selected 452 catchments are divided into two groups: (i) training data set, consisting of 362 catchments; and (ii) validation data set, consisting of 90 catchments. It has been shown that in the training of the four artificial intelligence based RFFA models, no model performs the best across all the considered six average recurrence intervals (ARIs) for all the adopted statistical criteria. Overall, the ANN based RFFA model is found to outperform the other three models in the training. Based on an independent validation, the median relative error values for the ANN based RFFA model are found to be in the range of 35–44 % for eastern Australia. The results show that ANN based RFFA model is applicable to eastern Australia.

Application of Wavelet transformation (WT) has been found effective in dealing with the issue of non-stationary data. WT is a mathematical tool that improves the performance of Artificial Neural Network (ANN) models by simultaneously considering both the spectral and the temporal information contained in the input data. WT decomposes the main time series data into its sub-components. ANN models developed using input data processed by the WT instead of using data in its raw form are known as hybrid wavelet models. The hybrid wavelet data driven models, using multi-scale input data, results in improved performance by capturing useful information concealed in the main time series data in its raw form. This chapter will cover theoretical as well as practical applications of hybrid wavelet neural network models in hydrology.

Maintenance decision making that selects appropriate maintenance strategies for a road network is an important and complex part of infrastructure asset management (IAM). Multiobjective optimization (MOO) can help in simplifying a decision making problem with multiple objectives and trading off objectives by identifying efficient solutions. Therefore, MOO is a helpful tool in the decision-making process. The main objective of this study is to analyze the optimization problem involved in practical decision-making processes using MOO and to identify efficient solutions in the context of maintenance decision making. To accomplish this aim, this paper (1) introduces decision making in IAM along with a discussion of previous applications of optimization; (2) discusses the mathematical formulation of optimization problems in the context of IAM decision making; (3) proposes an optimization method known as dichotomic approach (DA) to solve the optimization problems of decision making by identifying efficie...

An experimental study was carried out to investigate the mitigation effect of vertical walls on a wharf model subjected to tsunami bores. Dam-break waves were generated in a flume to simulate tsunami bore propagation, the bore characteristics were observed, and the tsunami pressures on vertical walls and a wharf model were measured. Results indicate different characteristics for bores traveling on wet-bed and dry-bed. The tsunami bore impact on a vertical wall was shown to exhibit four stages, and the time-history of the pressure exhibits three phases accordingly. Based on the law of conservation of energy, an equation for estimating the pressure exerted on the mid-point of the wall was proposed with coefficient of 1.8–2.4, and found to be suitable in this experimental range. Based on experimental data, an equation of the mitigation effect of vertical walls on tsunami was proposed as a function of the inundation depth, the wall height and the deck height. The predicted values from the equation are generally within [Formula: see text] of the measured values.

Impact of tsunami-borne debris causes a significant force on coastal structures. Tsunami bores can carry different geometrical shapes of floating debris, which are often the greatest cause of damage to inland structures. Despite such a serious threat, the impact of floating debris on structures has received limited attention. The objective of this paper is to introduce a new method for measuring the impact force of floating debris on the seaward wall of structures during tsunami events. This will improve the understanding of tsunami-borne debris impact forces on structures and increase the predictive capabilities required for estimates of such forces in design guidelines. The debris impact tests were conducted using two smart debris devices with different geometrical shapes. Impact accelerations and forces in the horizontal and vertical planes were investigated.

This paper presents development of a comprehensive decision support tool (eTank) to analyse and optimise a rainwater tank size. The developed tool enables a simple quantitative analysis of the expected water that can be saved based on daily water balance concept. The tool produces graphs showing cumulative yearly rainwater used, overflow and augmented townwater supply. To account for climate variability, provision has been made in the tool to analyse for a particular option in three different climatic conditions (dry, average and wet). Also, the tool enables a life cycle costing and payback period analysis of any particular tank size through the simulated expected water savings per year, initial construction costs and operational. The outcomes of eTank are compared with other contemporary online tools in regards to rainwater savings. It is revealed that most of the tools overestimate the potential savings due to not considering several influencing factors.

This study explores the application of Mamdani-type fuzzy inference systems (FIS) to the development of rainfall–runoff models operating on a daily basis. The model proposed uses a Rainfall Index, obtained from the weighted sum of the most recently observed rainfall values, as input information. The model output is the daily discharge amount at the catchment outlet. The membership function parameters are calibrated using a two-stage constrained optimization procedure, involving the use of a global and a local search method. The study area is the Shiquan-3 catchment in China, which has an area of 3092 km2 and is located in a typical monsoon-influenced climate region. The performance of the fuzzy model is assessed through the mean squared error and the coefficient of efficiency R2 performance indexes. The results of the fuzzy model are compared with three other rainfall–runoff models which use the same input information as the fuzzy model. Overall, the results of this study indicate that Mamdani-type FIS are a suitable alternative for modelling the rainfall–runoff relationship.

This book is broad in content, but integrated, and covers topics such as: a European perspective on flooding, climate change, rainfall and river flow forecasting systems, decision support systems, river flood hydraulics, sediment & dam-break modelling, risk & uncertainty, social issues and developments in flood forecasting and warning systems.