Reliability Analysis Applied on Land Subsidence Effects of Groundwater Remediation: Probabilistic vs. Deterministic Approach

The quantification of soil variability is one of the most important aspects in the geo-engineering context. The uncertainty analysis is the main part of the reliability assessment for which a quantitative evaluation was performed in this study. The Reliability Index and the Probability of Failure using the First-Order Reliability Method (FORM) represents both, an effective method which is easy to implement at the same time. This work analyzes possible effects of compaction induced into the aquifer of the Scarlino Plain, caused by the extension of the hydraulic barrier for groundwater remediation. The currently implemented vertical barrier is composed of 12 wells which reach the depth of 10 m. The improvement of the project involves the construction of a further 40 clusters, each consisting of a doublet which intercepts different depths (10 and 18 m). The models of the subsoil stratigraphy and of the groundwater were built using a numerical model. The groundwater flow and the piezometric surface in the current configuration of the barrier were studied and the project configuration was evaluated. Using the Aquitard drainage model, the land subsidence was estimated to calculate the maximum admissible displacement related to exhibited goods, the so called territorial vulnerability. The evaluation analysis was performed using a traditional deterministic approach, followed by a reliability method based on probabilistic models. Finally, the respective results were reported in a soil mapping with overlapping layers.     

Modeling River Mixing Mechanism Using Data Driven Model

Modeling river mixing mechanism in terms of pollution transmission in rivers is an important subject in environmental studies. Dispersion coefficient is an important parameter in river mixing problem. In this study, to model and predict the longitudinal dispersion coefficient (D _L ) in natural streams, two soft computing techniques including multivariate adaptive regression splines (MARS) as a new approach to study hydrologic phenomena and multi-layer perceptron neural network as a common type of neural network model were prepared. To this end, related dataset were collected from literature and used for developing them. Performance of MARS model was compared with MLP and the empirical formula was proposed to calculate D _L . To define the most effective parameters on D _L structure of obtained formula from MARS model and more accurate formula was evaluated. Calculation of error indices including coefficient of determination (R²) and root mean square error (RMSE) for the results of MARS model showed that MARS model with R²?=?0.98 and RMSE?=?0.89 in testing stage has suitable performance for modeling D _L . Comparing the performance of empirical formulas, ANN and MARS showed that MARS model is more accurate compared to others. Attention to the structure of developed MARS and the most accurate empirical formulas model showed that flow velocity, depth of flow (H) and shear velocity are the most influential parameters on D _L .     

Overview of Water Policy Developments: Pre- and Post-2015 Development Agenda

The year 2015 marked the end of some important universal decisions regarding water developments. The International Decade for Action ‘Water for Life’ (2005–2015) was concluded so that a “Post-2015 Development Agenda” is now defined. 2015 was also the year when UN Millenium Goals (MDGs) came to an end to convert into the new Agenda, which is a process led by the United Nations (UN) to define the future global development framework. The proposed goal is now referred to as SDG’s or Sustainable Development Goals, that extend existing commitments such as the MDGs and the priorities of Rio + 20. SDG’s will balance the economic, social and environmental dimensions of sustainable development with a strong linkage between environment and socio-economic goals. They converge with the post-2015 development agenda, now called Agenda 30 as these goals will be valid until 2030. One of the major concepts of SDG’s is water security, which is the basic element of the Global Goal on Water. Water security is the capacity of a population to safeguard sustainable access to adequate quantities of acceptable quality of water for sustaining livelihoods, human well-being, and socioeconomic development, for ensuring protection against pollution and waterrelated disasters, and for preserving ecosystems in a climate of peace and political stability. This paper discusses the developments in water management within the last 30 years, which eventually led to the above concepts. A summary is provided on key events and documents of these past years to point out how the international community has reacted towards present and emerging needs of the society.     

Land Use Effect on the CN Model Parameters in a Tropical Dry Environment

Dry tropical forests account for over 1,000,000 km², and there is still lack of knowledge on their hydrologic processes. The curve number (CN) hydrologic model developed by the Natural Resources Conservation Service (NRCS) is widely applied for runoff determination in various parts of the world, but not so in tropical semiarid regions. This study analyzes the impact of land use changes on the CN model in a tropical semiarid environment, in two catchments of native dry tropical forest and thinned dry tropical forest land use from 2009 to 2012. The CN model was calibrated and validated for the NRCS recommended initial abstraction ratio λ = 0.2, and for λ evaluated from rainfall and runoff data. A reliability analysis was performed using Monte Carlo simulation. Model goodness-of-fit was assessed with statistical criteria. A total of 42 and 40 rainfall-runoff events were analyzed for the native and thinned dry tropical forest, respectively. Characteristic λ values of 0.15 and 0.11 were determined for the two respective catchments. Although CN values were similar for both land uses, CN_λ=0.20 = 80 and CN_{median λ} = 77, the thinned catchment showed a higher CN model parameters variability. The CN model was more sensitive to variations of CN values than to those of λ. This study showed that no matter the vegetation management in a dry tropical forest environment, modeled runoff is not affected by λ, but rather affected by CN, which represents soil, landuse and management.     

A Scenario Based Impact Assessment of Trace Metals on Ecosystem of River Ganges Using Multivariate Analysis Coupled with Fuzzy Decision-Making Approach

The growing consciousness about the health risks associated with environmental pollutants has brought a major shift in global concern towards prevention of hazardous/trace metals discharge in water bodies. Majority of these trace metals gets accumulated in the body of aquatic lives, which are considered as potential indicators of hazardous content. This results in an ecological imbalance in the form of poisoning, diseases and even death of fish and other aquatic lives, and ultimately affect humans through food chain. Trace metals such as Cd, Cr, Cu, Mn, Ni, Pb and Zn originated from various industrial operations containing metallic solutions and agricultural practices, have been contributing significantly to cause aquatic pollution. The present study develops a novel approach of expressing sustainability of river’s ecosystem based on health of the fish by coupling fuzzy sensitivity analysis into multivariate analysis. A systematic methodology has been developed by generating monoplot, two dimensional biplot and rotated component matrix (using ‘Analyze it’ and ‘SPSS’ software), which can simultaneously identify critical trace metals and their industrial sources, critical sampling stations, and adversely affected fish species along with their interrelationships. A case study of assessing the impact of trace metals on the aquatic life of river Ganges, India has also been presented to demonstrate effectiveness of the model. The clusters pertaining to various water quality parameters have been identified using Principal Component Analysis (PCA) to determine actual sources of pollutants and their impact on aquatic life. The fuzzy sensitivity analysis reveals the cause-effect relationship of these critical parameters. The study suggests pollution control agencies to enforce appropriate regulations on the wastewater dischargers responsible for polluting river streams with a particular kind of trace metal(s).     

Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks

The success of hydraulic simulation models of water distribution networks is associated with the ability of these models to represent real systems accurately. To achieve this, the calibration phase is essential. Current calibration methods are based on minimizing the error between measured and simulated values of pressure and flow. This minimization is based on a search of parameter values to be calibrated, including pipe roughness, nodal demand, and leakage flow. The resulting hydraulic problem contains several variables. In addition, a limited set of known monitored pressure and flow values creates an indeterminate problem with more variables than equations. Seeking to address the lack of monitored data for the calibration of Water Distribution Networks (WDNs), this paper uses a meta-model based on an Artificial Neural Network (ANN) to estimate pressure on all nodes of a network. The calibration of pipe roughness applies a metaheuristic search method called Particle Swarm Optimization (PSO) to minimize the objective function represented by the difference between simulated and forecasted pressure values. The proposed method is evaluated at steady state and over an extended period for a real District Metering Area (DMA), named Campos do Conde II, and the hypothetical network named C-town, which is used as a benchmark for calibration studies.     

Sustainable Basin-Scale Water Allocation with Hydrologic State-Dependent Multi-Reservoir Operation Rules

This study extends the PSO-MODSIM model, integrating particle swarm optimization (PSO) algorithm and MODISM river basin decision support system (DSS) to determine optimal basin-scale water allocation, in two aspects. The first is deriving hydrologic state-dependent (conditional) operating rules to better account for drought and high-flow periods, and the second is direct, explicit consideration of sustainability criteria in the model’s formulation to have a better efficiency in basin-scale water allocation. Under conditional operating rules, the operational parameters of reservoir target storage levels and their priority rankings were conditioned on the hydrologic state of the system in a priority-based water allocation scheme. The role of conditional operating rules and policies were evaluated by comparing water shortages associated with objective function values under unconditional and conditional operating rules. Optimal basin-scale water allocation was then evaluated by incorporating reliability, vulnerability, reversibility and equity sustainability indices into the PSO objective function. The extended model was applied for water allocation in the Atrak River Basin, Iran. Results indicated improved distribution of water shortages by about 7.5% using conditional operating rules distinguishing dry, normal and wet hydrologic states. Alternative solutions with nearly identical objective function values were found with sustainability indices included in the model.     

Medium-Term Hydro Generation Scheduling (MTHGS) with Chance Constrained Model (CCM) and Dynamic Control Model (DCM)

Medium-Term Hydro Generation Scheduling (MTHGS) plays an important role in the operation of hydropower systems. In the first place, this paper presents a Chance Constrained Model for solving the optimal MTHGS problem. The model recognizes the impact of inflow uncertainty and the constraints involving hydrologic parameters subjected to uncertainty are described as probabilistic statements. It aims at providing a more practical technique compared to the traditional deterministic approaches used for MTHGS. The stochastic inflow is expressed as a simple discrete-time Markov chain and Stochastic Dynamic Programming is adopted to solve the model. Then in order to use the information of long-term inflow forecast to improve dispatching decisions, a Dynamic Control Model is developed. Short-term forecast results of the current period and long-term forecast results of the remaining period are treated as inputs of the model. Finally, the two methods are applied to MTHGS of Xiluodu hydro plant in China. The results are compared to those obtained from Deterministic Dynamic Programming with hindsight and advantages and disadvantages of the two methods are analyzed.     

Informational Entropy: a Failure Tolerance and Reliability Surrogate for Water Distribution Networks

Evolutionary algorithms are used widely in optimization studies on water distribution networks. The optimization algorithms use simulation models that analyse the networks under various operating conditions. The solution process typically involves cost minimization along with reliability constraints that ensure reasonably satisfactory performance under abnormal operating conditions also. Flow entropy has been employed previously as a surrogate reliability measure. While a body of work exists for a single operating condition under steady state conditions, the effectiveness of flow entropy for systems with multiple operating conditions has received very little attention. This paper describes a multi-objective genetic algorithm that maximizes the flow entropy under multiple operating conditions for any given network. The new methodology proposed is consistent with the maximum entropy formalism that requires active consideration of all the relevant information. Furthermore, an alternative but equivalent flow entropy model that emphasizes the relative uniformity of the nodal demands is described. The flow entropy of water distribution networks under multiple operating conditions is discussed with reference to the joint entropy of multiple probability spaces, which provides the theoretical foundation for the optimization methodology proposed. Besides the rationale, results are included that show that the most robust or failure-tolerant solutions are achieved by maximizing the sum of the entropies.