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A study of climate change scenarios is presented in this paper by projecting the model of recorded precipitation/temperature data into three future periods by statistical downscaling methods through LARS-WG with data from 7 synoptic stations. The study area largely spans the closed basin of Lake Urmia with overlaps to some of the surrounding basins. The model is at two stages (the downscaling stage comprises the following: (i) it uses large-scale GCM models to provide climate variables (predictors), and (ii) the outcomes are downscaled to the local climatic variables for correlating with the observed time series (e.g. rainfall) for the period of T0, 1961–2001—40 years; the projecting stage comprises the derived precipitation/temperature values at T0 to T1: 2011–2030; T2: 2046–2065; and T3: 2080–2099 periods at synoptic stations using three standard scenarios of A1B, A2, and B1). The outputs are used to map the climate zones, which show the following: (i) climates at T1 are still similar to T0, and if there is any declining trend in precipitation during both periods, they are small to the extent that the shrinkage of Lake Urmia is unlikely to be driven by climate change; (ii) changes are likely at T2 and T3 periods including climatic regimes may turn peakier at the northern and drier at central regions; and (iii) precipitation is likely to decrease in some of the zones at T2 and T3 periods. This underpins the need for more responsive policymaking, and should this not be realised in the near future, the prospect seems bleak in terms of serious damages to Lake Urmia, depleting aquifers and subsequent immigration from the region, but can be averted by responsive planning. [ABSTRACT FROM AUTHOR]

Declining groundwater levels due to the absence of a planning system makes aquifers vulnerable to subsidence. This paper investigates possible hotspots in terms of Subsidence Vulnerability Indices (SVI) by applying the ALPRIFT framework, introduced recently by the authors by mirroring the procedure for the DRASTIC framework. ALPRIFT is suitable to cases, where data is sparse, and is the acronym of seven data layers to be presented in due course. It is a scoring technique, in which each data layer bears an aspect of land subsidence and is prescribed with rates to account for local variability, and with prescribed weights to account for relative significance of the data layer. The inherent subjectivity in prescribed weights is treated in this paper by learning their values from site-specific data by the strategy of using artificial intelligence to learn from multiple models (AIMM). The strategy has two levels: (i) at Level 1, three fuzzy models are used to learn weight values from the local data and from observed target data, and (ii) at Level 2, genetic expression algorithm (GEP) is used to learn further, in which the outputs of the models at Level 1 are reused as its inputs and observed data as its target values. The results show that (i) the Nash-Sutcliff Efficiency (NSE) coefficient for ALPRIFT with measured land subsidence values is approx. 0.21; (ii) NSE is improved to 0.88 by learning the weights at Level 1 using fuzzy logic, and (iii) NSE is further improved to 0.94 by further learning at Level 2 using GEP. [ABSTRACT FROM AUTHOR]

The barriers for the development of continuous monitoring of Suspended Sediment Concentration (SSC) in channels/rivers include costs and technological gaps but this paper shows that a solution is feasible by: (i) using readily available high-resolution images; (ii) transforming the images into image analytics to form a modelling dataset; and (iii) constructing predictive models by learning inherent correlation between observed SSC values and their image analytics. High-resolution images were taken of water containing a series of SSC values using an exploratory flume. Machine learning is processed by dividing the dataset into training and testing sets and the paper uses the following models: Generalized Linear Machine (GLM) and Distributed Random Forest (DRF). Results show that each model is capable of reliable predictions but the errors at higher SSC are not fully explained by modelling alone. Here we offer sufficient evidence for the feasibility of a continuous SSC monitoring capability in channels before the next phase of the study with the goal of producing practice guidelines. [ABSTRACT FROM AUTHOR]

The safety factor (SF) in riverbank stability problems for noncohesive soils is treated mathematically in this article to unravel its inherent bimodality, as stipulated by the cusp catastrophe technique in a hyperspace. The developed methodology may be contrasted with traditional approaches delineating the SF space to failure and operational states. The emerging three states are a significant shift from traditional treatments overlooking bimodality. The mathematical treatment presented in the technical note incorporates classic soil equations for noncohesive soils into the cusp catastrophe technique, with clear formulations at multiple levels of a potential function, including the energy level. The integrated mathematical expressions use soil properties to express lower order properties such as catastrophe flags, for example, bifurcation sets, hysteresis, and bimodality delineating sudden and gradual change in the state of a system. These equations show that even the SF of operational states depends on soil properties and gravity, and therefore a safe use of the SF requires a deep knowledge of the SF hyperspace. Practical applications of the new mathematical development presented in the technical note may be viewed in three steps. In Step 1, the tacit nature of the cusp catastrophe bimodality of safety factor in riverbank stability problems needs to be tested through experimental data. The laboratory tests and fieldwork can be designed to encompass the full range of cases comprising: (i) the failure region; (ii) the operational region; and (iii) their bimodal zone. In Step 2, the existence of the three cases is verified by various applications to underpin the dependency of the safety factor on soil parameters. In Step 3, this new knowledge is realized by wide applications to gain an insight into behaviors of safety factors in wide-ranging problems such as natural slopes, channels, embankments, riverbanks, and levees. [ABSTRACT FROM AUTHOR]

Chaos theory is integrated with Multi-Gene Genetic Programming (MGGP) engine as a new hybrid model for river flow forecasting. This is to be referred to as Chaos-MGGP and its performance is tested using daily historic flow time series at four gauging stations in two countries with a mix of both intermittent and perennial rivers. Three models are developed: (i) Local Prediction Model (LPM); (ii) standalone MGGP; and (iii) Chaos-MGGP, where the first two models serve as the benchmark for comparison purposes. The Phase-Space Reconstruction (PSR) parameters of delay time and embedding dimension form the dominant input signals derived from original time series using chaos theory and these are transferred to Chaos-MGGP. The paper develops a procedure to identify global optimum values of the PSR parameters for the construction of a regression-type prediction model to implement the Chaos-MGGP model. The inter-comparison of the results at the selected four gauging stations shows that the Chaos-MGGP model provides more accurate forecasts than those of stand-alone MGGP or LPM models. [ABSTRACT FROM AUTHOR]

The information content of the classic equations describing the problem of forced hydraulic jumps in open channels is the subject of this paper. The forcing refers to designed structural composites to transform incoming supercritical flows into outgoing subcritical flows through hydraulic jumps. The complete flow history through such systems is described by two stable flow profiles, which are interlocked and one can jump to the other. The interlocked profiles underpin a hydraulic effect known as hysteresis with the prime feature of the dependence of flows on their past history. The paper develops an algorithm by using classic hydraulic equations alone to identify hysteresis by building on tacit knowledge already published. The algorithm reproduces the effects of catastrophe theory and shows that hysteresis has predictable configurations. It is validated for geometries with step-rises by using published experimental data; as well as a flume with abrupt contractions by authors' experimental data. [ABSTRACT FROM AUTHOR]

Recorded time series of relative humidity (RH) are modeled by using genetic expression programming (GEP) and artificial neural networks (ANNs) models. The data are noisy and contain missing datapoints. RH is modeled as a function of three meteorological variables: temperature, wind speed, and pressure. Various model structures of both of these models are investigated with the aim of testing the robustness of the predicted values in the presence of noise and missing data. Due to the presence of noise, a sophisticated treatment of missing data was not justifiable, and therefore, the strategy adopted was just to carry the datapoints backward, although this may induce bias in the time dimension and contaminate the predicted results. The results of this study indicate that through a careful selection of model structures both GEP and ANN can produce adequately reliable prediction of RH values 1 year into the future. The paper provides evidence that this model structure is feasible when the dependent variables include both the present and past values. [ABSTRACT FROM AUTHOR]

Abstract: Predictive ozone models are becoming indispensable tools by providing a capability for pollution alerts to serve people who are vulnerable to the risks. We have developed a tropospheric ozone prediction capability for Tabriz, Iran, by using the following five modeling strategies: three regression-type methods: Multiple Linear Regression (MLR), Artificial Neural Networks (ANNs), and Gene Expression Programming (GEP); and two auto-regression-type models: Nonlinear Local Prediction (NLP) to implement chaos theory and Auto-Regressive Integrated Moving Average (ARIMA) models. The regression-type modeling strategies explain the data in terms of: temperature, solar radiation, dew point temperature, and wind speed, by regressing present ozone values to their past values. The ozone time series are available at various time intervals, including hourly intervals, from August 2010 to March 2011. The results for MLR, ANN and GEP models are not overly good but those produced by NLP and ARIMA are promising for the establishing a forecasting capability. [Copyright &y& Elsevier]

A critical understanding of the water crisis of Lake Urmia is the driver in this paper for a basin-wide investigation of its Meteorological (Met) droughts and Groundwater (GW) droughts. The challenge is to formulate a data-driven modelling strategy capable of discerning anthropogenic impacts and resilience patterns through using 21-years of monthly data records. The strategy includes: (i) transforming recorded timeseries into Met/GW indices; (ii) extracting their drought duration and severity; and (iii) deriving return periods of the maximum drought event through the copula method. The novelty of our strategy emerges from deriving return periods for Met and GW droughts and discerning anthropogenic impacts on GW droughts. The results comprise return periods for Met/GW droughts and their basin-wide spatial distributions, which are delineated into four zones. The information content of the results is statistically significant; and our interpretations hint at the basin resilience is already undermined, as evidenced by (i) subsidence problems and (ii) altering aquifers' interconnectivity with watercourses. These underpin the need for a planning system yet to emerge for mitigating impacts and rectifying their undue damages. The results discern that aquifer depletions stem from mismanagement but not from Met droughts. Already, migration from the basin area is detectable. [ABSTRACT FROM AUTHOR]

The hydraulic design and analysis of flow conditions in pipe networks are dependent upon estimating the friction factor, f . The performance of its explicit formulations and those of artificial intelligence (AI) techniques are studied in this paper. The AI techniques used here include artificial neural networks (ANNs) and genetic programming (GP); both use the same data generated numerically by systematically changing the values of Reynolds numbers, Re , and relative roughness, ∈/D , and solving the Colebrook-White equation for the value of f by using the successive substitution method. The tests included the transformation of Re and ∈/D using a logarithmic scale. This study shows that some of the explicit formulations for friction factor induce undue errors, but a number of them have good accuracy. The ANN formulation for the solving of the friction factor in the Colebrook-White equation is less successful than that by GP. The implementation of GP offers another explicit formulation for the friction factor; the performance of GP in terms of R² (0.997) and the root-mean-square error (0.013) is good, but its numerically obtained values are slightly perturbed. [ABSTRACT FROM AUTHOR]

Summary: The existence of chaotic behaviour in the river stage and discharge time series observed at the Sogutluhan hydrometric station, Turkey, is investigated. Five nonlinear dynamic methods are employed: (1) phase space reconstruction; (2) False Nearest Neighbour (FNN) algorithm; (3) correlation dimension method; (4) Lyapunov exponent method; and (5) local approximation method. These methods have their bases on data embedding, nearest neighbour search, dimensionality analysis, system divergence/convergence, and local approximation and have varying levels of sophistication in conceptualisation and implementation. They provide either direct identification of chaotic behaviour or at least facilitate identification through system reconstruction, complexity determination (especially in terms of dimensionality), and prediction (including predictability horizon). As the discharge data used in this study are produced by rating directly gauged stage time series, it becomes feasible to investigate any interference triggered by chaotic signals with the rating. The results indicate the existence of low-dimensional chaos in the two time series. They also suggest that the rating of the stage time series to obtain the discharge time series amplifies significantly the fluctuations in the latter in the presence of chaotic signals. [ABSTRACT FROM AUTHOR]

Water level forecasting using recorded time series can provide a local modelling capability to facilitate local proactive management practices. To this end, hourly sea water level time series are investigated. The records collected at the Hillarys Boat Harbour, Western Australia, are investigated over the period of 2000 and 2002. Two modelling techniques are employed: low-dimensional dynamic model, known as the deterministic chaos theory, and genetic programming, GP. The phase space, which describes the evolution of the behaviour of a nonlinear system in time, was reconstructed using the delay-embedding theorem suggested by Takens. The presence of chaotic signals in the data was identified by the phase space reconstruction and correlation dimension methods, and also the predictability into the future was calculated by the largest Lyapunov exponent to be 437 h or 18 days into the future. The intercomparison of results of the local prediction and GP models shows that for this site-specific dataset, the local prediction model has a slight edge over GP. However, rather than recommending one technique over another, the paper promotes a pluralistic modelling culture, whereby different techniques should be tested to gain a specific insight from each of the models. This would enable a consensus to be drawn from a set of results rather than ignoring the individual insights provided by each model. [ABSTRACT FROM AUTHOR]

Summary: The inter-comparison of three artificial intelligence (AI) techniques are presented using the results of river flow/stage timeseries, that are otherwise handled by traditional discharge routing techniques. These models comprise Artificial Neural Network (ANN), Adaptive Nero-Fuzzy Inference System (ANFIS) and Genetic Programming (GP), which are for discharge routing of Kizilirmak River, Turkey. The daily mean river discharge data with a period between 1999 and 2003 were used for training and testing the models. The comparison includes both visual and parametric approaches using such statistic as Coefficient of Correlation (CC), Mean Absolute Error (MAE) and Mean Square Relative Error (MSRE), as well as a basic scoring system. Overall, the results indicate that ANN and ANFIS have mixed fortunes in discharge routing, and both have different abilities in capturing and reproducing some of the observed information. However, the performance of GP displays a better edge over the other two modelling approaches in most of the respects. Attention is given to the information contents of recorded timeseries in terms of their peak values and timings, where one performance measure may capture some of the information contents but be ineffective in others. Thus, this makes a case for compiling knowledge base for various modelling techniques. [ABSTRACT FROM AUTHOR]

Summary: Over time since the prehistory, interactions with floods have undergone evolutionary transitions including aversion to flood risk, flood defence and flood risk management, each serving as a mindset or a paradigm. Historic data describing these interactions are used in this paper to “model” these transitions and to explain them. This is a new bottom-up modelling capability based on a set of postulates integrating: (i) systemic thinking where systems are effected by four types of feedback loops to be described in the paper, which include positive/negative feedback; and (ii) evolutionary thinking, where each feedback loop is associated with a “risk mindset.” These mindsets can undergo evolutionary transition from one to the next and the transition is largely driven by natural selection. After an evolutionary transition, lower mindsets do not necessarily disappear but can adapt and coexist with higher order loops. Based on the insight gained, the paper argues that (i) as the loops coexist pluralistically, systems increase in their complexity; (ii) there may be unexpected dynamic behaviours when a system is interacted with different types of feedback loops; and (iii) currently, these dynamic behaviours are overlooked, suggesting possible loopholes, bottlenecks or barriers and hence the motivation for this paper. [ABSTRACT FROM AUTHOR]

Modelling and prediction of hydrological processes (e.g. rainfall-runoff) can be influenced by discontinuities in observed data, and one particular case may arise when the time scale (i.e. resolution) is coarse (e.g. monthly). This study investigates the application of catastrophe theory to examine its suitability to identify possible discontinuities in the rainfall-runoff process. A stochastic cusp catastrophe model is used to study possible discontinuities in the monthly rainfall-runoff process at the Aji River basin in Azerbaijan, Iran. Monthly-averaged rainfall and flow data observed over a period of 20 years (1981-2000) are analysed using the Cuspfit program. In this model, rainfall serves as a control variable and runoff as a behavioural variable. The performance of this model is evaluated using four measures: correlation coefficient, log-likelihood, Akaike information criterion (AIC) and Bayesian information criterion (BIC). The results indicate the presence of discontinuities in the rainfall-runoff process, with a significant sudden jump in flow (cusp signal) when rainfall reaches a threshold value. The performance of the model is also found to be better than that of linear and logistic models. The present results, though preliminary, are promising in the sense that catastrophe theory can play a possible role in the study of hydrological systems and processes, especially when the data are noisy. Citation Ghorbani, M. A., Khatibi, R., Sivakumar, B. & Cobb, L. (2010) Study of discontinuities in hydrological data using catastrophe theory. Hydrol. Sci. J. 55(7), 1137-1151. [ABSTRACT FROM AUTHOR]

Abstract: Water level forecasting at various time intervals using records of past time series is of importance in water resources engineering and management. In the last 20 years, emerging approaches over the conventional harmonic analysis techniques are based on using Genetic Programming (GP) and Artificial Neural Networks (ANNs). In the present study, the GP is used to forecast sea level variations, three time steps ahead, for a set of time intervals comprising 12h, 24h, 5 day and 10 day time intervals using observed sea levels. The measurements from a single tide gauge at Hillarys Boat Harbor, Western Australia, were used to train and validate the employed GP for the period from December 1991 to December 2002. Statistical parameters, namely, the root mean square error, correlation coefficient and scatter index, are used to measure their performances. These were compared with a corresponding set of published results using an Artificial Neural Network model. The results show that both these artificial intelligence methodologies perform satisfactorily and may be considered as alternatives to the harmonic analysis. [Copyright &y& Elsevier]

Provides information on a study which presented the confidence calculation method for estimating confidence values to a prescribed precision interval associated with the sample mean which is opposite to confidence interval estimation. Inverse problem of open-channel friction parameter; Methodology; Application of method; Conclusions.

Discusses issues in computational hydraulics practice. Overview of the practice; Stage of modeling practices; Trends toward standardization.

Presents a study which reported a numerical study of friction parameters associated with nearly flat tidal channels. Methodology; Mathematical consideration of quality conditions; Conclusions.

Provides information on a study which described the problem of head-discharge relationships in 6-point staggered schemes. Information on the hydrodynamic simulations of free-surface open-channel shallow water waves; Review of existing simulation methods; Proposed approach for implementing head-discharge relationships; Conclusions.

Studies effective control of test conditions on the identification problem of open-channel friction parameters. Use of flood events; Hydraulic basis of the problem; Implementation of optimization methods; Effects of data errors and of selection of objective functions on identified parameters.

A critical understanding of the water crisis of Lake Urmia is the driver in this paper for a basin-wide investigation of its Meteorological (Met) droughts and Groundwater (GW) droughts. The challenge is to formulate a data-driven modelling strategy capable of discerning anthropogenic impacts and resilience patterns through using 21-years of monthly data records. The strategy includes: (i) transforming recorded timeseries into Met/GW indices; (ii) extracting their drought duration and severity; and (iii) deriving return periods of the maximum drought event through the copula method. The novelty of our strategy emerges from deriving return periods for Met and GW droughts and discerning anthropogenic impacts on GW droughts. The results comprise return periods for Met/GW droughts and their basin-wide spatial distributions, which are delineated into four zones. The information content of the results is statistically significant; and our interpretations hint at the basin resilience is already undermined, as evidenced by (i) subsidence problems and (ii) altering aquifers' interconnectivity with watercourses. These underpin the need for a planning system yet to emerge for mitigating impacts and rectifying their undue damages. The results discern that aquifer depletions stem from mismanagement but not from Met droughts. Already, migration from the basin area is detectable. [ABSTRACT FROM AUTHOR]

An investigation is presented in this paper to predict Groundwater Level (GWL) by Inclusive Multiple Modelling (IMM) practices, introduced recently by the authors, as a strategy by organising multiple models at three hierarchical levels: Artificial Neural Networks (ANNs), Sugeno Fuzzy Logic (SFL), Neuro-Fuzzy (NF), Support Vector Machine (SVM) and Gene Expression Programming (GEP). The IMM novelty of the study is to investigate a modelling strategy at three hierarchical levels, such that any base models at Level 1 are not reused as the combiner model at Levels 2 and Level 3 and this leads to a number of strategies. The results provide some evidence that (i) combining base models at Levels 2 and 3 enhance the performances compared with those of individual base models at Level 1; and (ii) the results at Level 3 become defensible and thereby suitable for the development of management scenarios. The decline in GWLs is investigated through management scenarios, which show that water use has higher impacts on groundwater level variations in the study area than those by climatic variabilities and this underpins the evidence for the necessity of management plans and strategies for the study area. [ABSTRACT FROM AUTHOR]

• Treated subjectivity in DRASTIC weights by unsupervised frameworks and supervised models. • Improved information extraction by a model combination strategy at Levels 1 and 2. • Level 1: Constructed 2 frameworks and 2 models; at Level 2: formed 4 strategies. • A set of 8 frameworks and models created a logical step in the level of learning. • An increasing level of improvements is detected as supervision is improved. Production of defensible modelling tools for aquifer vulnerability mapping under the conditions of sparse data remains topical using the DRASTIC framework. DRASTIC is the acronym for seven data layers using a prescribed scoring system in terms of rates to account for local variations and weights to account for the relative importance of data layers. Also, the term framework signifies the consensual nature of choosing the data layers without any theoretical or empirical basis. Artificial Intelligence (AI) or similar techniques are used to reduce inherent subjectivities through a strategy of unsupervised learning from data by formulating Multiple Frameworks (MF) and supervised learning from Multiple Models (MMs) without searching for any 'superior' model. Notably, a framework lacks the procedure for iterative training and testing, as usual in modelling procedures. In this strategy, models are organised in two hierarchical levels: Level 1 comprises the construction of MFs (two frameworks) and/or MMs (two models); Level 2 comprises a model or an algorithm to reuse the outputs from Level 1 through formulating four strategies, including a mixture of MFs/ MMs, in which Support Vector Machine (SVM) is chosen by the paper to run models at Level 2. The supervision process uses target values as a function of observed nitrate-N values. The strategy is applied to the aquifer in the Malekan region, where the past extensive agricultural practices have been transformed into intensive use of fertilisers without protecting water quality in the aquifers. Results show that AIMM/MF lead to significant improvements in delineating high vulnerability areas. [ABSTRACT FROM AUTHOR]

Modelling time series of groundwater levels is investigated by three fuzzy logic (FL) models, Sugeno (SFL), Mamdani (MFL) and Larsen (LFL), using data from observation wells. One novelty in the study is the re-use of these three models as multiple models through the following strategies: (a) simple averaging, (b) weighted averaging and (c) committee machine techniques; these are implemented using artificial neural networks (ANN). These strategies provide some evidence that (i) multiple models improve on the performance of individual models and those using committee machines perform better than the other two options; and (ii) committee machine models produce defensible modelling results to develop management scenarios. The study investigates water table declines through management scenarios and shows that in this aquifer water use has higher impacts on water table variations than climatic variations. This provides evidence of the need for planned management in the study area. [ABSTRACT FROM AUTHOR]

A novel methodology is introduced for the spatial indexing of groundwater drought ‘risks’ (GDRs). It combines reliability analysis and standardised water-level index (SWI), which is readily applicable to areas with sparse data on groundwater depth (GWD) measurements. In reliability analysis, GWDs are reformulated in terms of load, which accounts for external effects, e.g. withdrawals and recharge, as well as resistance, which accounts for system capacity with regard to drought intensities (mild, moderate, severe and extreme). Reliability analysis formulates a novel procedure by using loads and resistance to formulate a performance function, which can be treated by statistical techniques, and thereby derives values of GDR, defined as failure of an operational system but without considering consequences. GDRs at observation wells are spatially distributed by using an interpolation technique. The methodology allows for estimating time variability in GDR to derive an environmental/ecological hazard indicator (EHI), which can serve in the management and planning of predicting groundwater drought. A Graphical User Interface (GDR V.1.0) is developed to serve as a decision support system and to derive GDR and EHI values. [ABSTRACT FROM AUTHOR]

Proof-of-concept (PoC) is presented for a new framework to serve as a proactive capability to mapping subsidence vulnerability of Shabestar plain of approximately 500 km 2 overlaying an important aquifer supporting a region renowned for diversity of agricultural products. This aquifer is one of 12 in East and West Azerbaijan provinces, Northwest Iran, which surround the distressed Lake Urmia, with its water table declined approximately 4 m in between 2004 and 2014. The decline of water table in aquifers undermines their soil texture and structure by exposure to pressures under their weight and thereby induce or trigger land subsidence. Inspired by the DRASTIC framework to map intrinsic aquifer vulnerability to anthropogenic pollution, the paper introduces the ALPRIFT framework for subsidence, which comprises the seven data layers of Aquifer media (A), Land use (L), Pumping of groundwater, Recharge (R), aquifer thickness Impact (I), Fault distance (F) and decline of water Table (T). The paper prescribes rates to account for local variations and weights for the relative importance of the data layers. The proof-of-concept for ALPRIFT is supported by the correlation of Subsidence Vulnerability Indices (SVIs) with measured subsidence values, which renders a value of 0.5 but improves significantly to 0.86 when using fuzzy logic. Similar improvements are suggested by the ROC/AUC performance metric. [ABSTRACT FROM AUTHOR]

An investigation is undertaken to identify arsenic anomalies at the complex of Sahand dam, East Azerbaijan, northwest Iran. The complex acts as a system, in which the impounding reservoir catalyses system components related to Origin-Source-Pathways-Receptor-Consequence (OSPRC) viewed as a risk system. This ‘conceptual framework’ overlays a ‘perceptual model’ of the physical system, in which arsenic with geogenic origins diffused into the formations through extensive fractures swept through the region during the Miocene era. Impacts of arsenic anomalies were local until the provision of the impounding reservoir in the last 10 years, which transformed it into active system-wide risk exposures. The paper uses existing technique of: statistical, graphical, multivariate analysis, geological survey and isotopic study, but these often seem ad hoc and without common knowledgebase. Risk analysis approaches are sought to treat existing fragmentation in practices of identifying and mitigating arsenic anomalies. The paper contributes towards next generation best practice through: (i) transferring and extending knowledge on the OSPRC framework; (ii) introducing ‘OSPRC cells’ to capture unique idiosyncrasies at each cell; and (iii) suggesting a ‘soft modelling’ procedure based on assembling knowledgebase of existing techniques with partially converging and partially diverging information levels, where knowledgebase invokes model equations with increasing resolutions. The data samples from the study area for the period of 2002–12 supports the study and indicates the following ‘risk cells’ for the study area: (i) local arsenic risk exposures at south of the reservoir, (ii) system-wide arsenic risks at its north; and (iii) system-wide arsenic risk exposures within the reservoir even after dilution. [ABSTRACT FROM AUTHOR]

Driven by contamination risks, mapping Vulnerability Indices (VI) of multiple aquifers (both unconfined and confined) is investigated by integrating the basic DRASTIC framework with multiple models overarched by Artificial Neural Networks (ANN). The DRASTIC framework is a proactive tool to assess VI values using the data from the hydrosphere, lithosphere and anthroposphere. However, a research case arises for the application of multiple models on the ground of poor determination coefficients between the VI values and non-point anthropogenic contaminants. The paper formulates SCFL models, which are derived from the multiple model philosophy of Supervised Committee (SC) machines and Fuzzy Logic (FL) and hence SCFL as their integration. The Fuzzy Logic-based (FL) models include: Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Larsen Fuzzy Logic (LFL) models. The basic DRASTIC framework uses prescribed rating and weighting values based on expert judgment but the four FL-based models (SFL, MFL, LFL and SCFL) derive their values as per internal strategy within these models. The paper reports that FL and multiple models improve considerably on the correlation between the modeled vulnerability indices and observed nitrate-N values and as such it provides evidence that the SCFL multiple models can be an alternative to the basic framework even for multiple aquifers. The study area with multiple aquifers is in Varzeqan plain, East Azerbaijan, northwest Iran. [ABSTRACT FROM AUTHOR]

This paper investigates aggregated risks in aquifers, where risk exposures may originate from different contaminants e.g. nitrate-N (NO 3 –N), arsenic (As), boron (B), fluoride (F), and aluminium (Al). The main goal is to develop a new concept for the total risk problem under sparse data as an efficient planning tool for management through the following methodology: (i) mapping aquifer vulnerability by DRASTIC and SPECTR frameworks; (ii) mapping risk indices to anthropogenic and geogenic contaminants by unsupervised methods; (iii) improving the anthropogenic and geogenic risks by a multi-level modelling strategy at three levels: Level 1 includes Artificial Neural Networks (ANN) and Support Vector Machines (SVM) models, Level 2 combines the outputs of Level 1 by unsupervised Entropy Model Averaging (EMA), and Level 3 integrates the risk maps of various contaminants (nitrate-N, arsenic, boron, fluoride, and aluminium) modelled at Level 2. The methodology offers new data layers to transform vulnerability indices into risk indices and thereby integrates risks by a heuristic scheme but without any learning as no measured values are available for the integrated risk. The results reveal that the risk indexing methodology is fit-for-purpose. According to the integrated risk map, there are hotspots at the study area and exposed to a number of contaminants (nitrate-N, arsenic, boron, fluoride, and aluminium). [Display omitted] • Risk Index (RI) is products of Passive and Active Vulnerability Indices (PVI and AVI). • RIs are aggregated from anthropogenic and geogenic sources for 5 contaminants. • By Multiple-level Models strategy, risks are mapped at 3 levels, L1, L2, L3. • L1 uses ANN + SVM; L2 uses Entropy Model Averaging, L3 uses a scoring system. • ROC/AUC plots show defensible RIs for NO 3 –N, As, B, F, and Al and their aggregation. [ABSTRACT FROM AUTHOR]

This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), Neuro-Fuzzy (NF) and Gene Expression Programming (GEP). Each model uses the DRASTIC index, the acronym of 7 geological, hydrological and hydrogeological parameters, which collectively represents intrinsic (or natural) vulnerability and gives a sense of contaminants, such as nitrate-N, penetrating aquifers from the surface. These models are trained to modify or condition their DRASTIC index values by measured nitrate-N concentration. The three AI-techniques often perform similarly but have differences as well and therefore SICM exploits the situation to improve the modeled values by producing a hybrid modeling results through selecting better performing SVM, NF and GEP components. The models of the study area at Ardabil aquifer show that the vulnerability indices by the DRASTIC framework produces sharp fronts but AI models smoothen the fronts and reflect a better correlation with observed nitrate values; SICM improves on the performances of three AI models and cope well with heterogeneity and uncertain parameters. [ABSTRACT FROM AUTHOR]

Bayesian Model Averaging (BMA) is used to study inherent uncertainties using the Basic DRASTIC Framework (BDF) for assessing the groundwater vulnerability in a study area related to Lake Urmia. BMA is naturally an Inclusive Multiple Modelling (IMM) strategy at two levels; at Level 1 multiple models are constructed and the paper constructs three AI (Artificial Intelligence) models, which comprise ANN (Artificial Neural Network), GEP (Gene Expression Programming), and SVM (Support Vector Machines) but their outputs are fed to the next level model; at Level 2, BMA combines ANN, GEP and SVM (the Level 1 models) to quantify their inherent uncertainty in terms of within and in-between model errors. The model performance is tested by using the nitrate-N concentrations measured for the aquifer. The results show that in this particular study area, Level 1 models, even BDF, are quite accurate, but the above modelling strategy maximises the extracted information from the local data and BMA reveals that the higher uncertainties occur at areas with higher vulnerability; whereas lower uncertainties are observed at areas with lower vulnerabilities. [Display omitted] • DRASTIC vulnerability is studied by a strategy by Inclusive Multiple Models (IMM). • IMM uses 3 AI models at Level 1 and BMA (Bayesian) at Level 2 to study uncertainty. • Accuracy is good for Level 1 models (AUC = 0.998), even for basic DRASTIC (AUC = 0.995). • IMM at Level 2 is still needed to study error residuals and inherent uncertainties. • BMA results are defensible and show high uncertainty at high vulnerability zones. [ABSTRACT FROM AUTHOR]

Groundwater over-abstraction due to the absence of an effective management plan is often one of the main reasons for land subsidence in aquifer areas. This paper investigates this environmental problem at Salmas plain, Iran, by using the ALPRIFT framework, an acronym of a set of seven general-purpose data layers, introduced recently by the authors. It is capable of mapping Subsidence Vulnerability Indices (SVI) and the paper investigates an innovation to transform it into Time-variant SVI (TSVI) mapping capabilities through a three module strategy: Module 1 : maps SVI; Module 2 : develops a predictive model for Groundwater Levels (GWL); Module 3 : combines both modules to produces TSVI maps. Modules 1 and 2 employ Inclusive Multiple Modelling (IMM) practices, which promote learning from multiple models, as opposed to their ranking and selecting a 'superior' one. IMM is implemented through the same single modelling strategy for both Modules 1 and 2 at two levels: at Level 1 , multiple models are constructed by three Fuzzy Logic (FL) models: Sugeno FL (SFL), Mamdani FL (MFL) and Larsen FL (LFL). (ii) At Level 2 , FL models at Level 1 are reused by Support Vector Machine (SVM) as the combiner model. The results show that (i) the models at Level 1 are fit-for-purpose; (ii) the models at Level 2 are defensible owing to IMM strategies focussed on enhancing their accuracy and investigating their residuals; and (iii) according to TSVI maps, the north of the plain is vulnerable to hotspot areas and is exposed to subsidence risks due to unplanned over-abstraction of groundwater from the aquifer at Salmas plain. [Display omitted] • Study Time-variable Subsidence Vulnerability Indexing (TSVI) by Water T able (T). • Predict SVI using ALPRIF T in Module 1 (M1); predict T by site data in Module 2 (M2). • Use Inclusive Multiple Modelling strategies for M1 and M2, both at Levels 1 and 2. • Results of M1 and M2 at Level 1 are fit-for-purpose but are defensible at Level 2. • Predict TSVI maps for the near future by combining M1 with M2 – a proof-of-concept. [ABSTRACT FROM AUTHOR]

The topical research on Human Health Risk Assessment (HHRA) is investigated in this paper but in the context of uncertainty using Monto Carlo Simulation (MCS) tools. This study aims to capture some of the inherent uncertainties by implementing MCS in two dimensions: Dimension 1 considers the variability within the prescribed parameters; and Dimension 2 captures the uncertainty due to functional definitions of some of the moments of the selected distributions and interdependency of correlated parameters at a higher level. The 2D MCS model of HHRA is applied to risk assessment of a study area contaminated by arsenic, a challenging case in which arsenic has a geogenic origin but the risk is triggered by human activities. The results indicate that (i) the uncertainty in the results for the site reflects a probability distribution of risk with a positive skew; and (ii) the uncertainty increases by increasing arsenic concentration, as indicated by whisker box diagrams. The study sheds light on identifying remedial strategies since risk corresponding to Reasonable Maximum Exposure (RME Risk) is higher than the concern level of risk recommended by USEPA. The risk corresponding to the central tendency exposure is also higher than the concern level of risk in most of the samples. The paper investigates current water supply sources in the residential areas and their risk values and accordingly identifies a set of possible action plans to mitigate risk. However, the formulated 2D MCS can be extended by employing local data for deriving probability distributions and different uncertainty techniques. [Display omitted] • Formulate probabilistic Human Health Risk Assessment (HHRA) and capture uncertainty. • Implement Monte Carlo Simulation on HHRA and calculate risk distributions. • HHRA and uncertainty studies identified hotspots and the bands of at-risk areas. • The scale of the risk of health to the residents necessitate remedial strategies. • The identified feasible mitigation plans are research-driven and guided by uncertainty. [ABSTRACT FROM AUTHOR]

• Varzeqan aquifer is exposed to nitrate, fluoride & arsenic risks and mining. • Risk exposures stem from anthropogenic or geogenic origins at multiple sources. • Total Information Management present a framework with 5 dimensions with sparse data. • Dimensions includes perceptual, conceptual models, risk cells and soft modelling. • The study gives an insight into the hotspots and a direction for the next phase. The aquifer at Varzeqan plain, with multiple confined/unconfined and hard-rock boundaries, is exposed to risks from several contaminants (nitrate-N, fluoride and arsenic) originated by anthropogenic and/or geogenic activities, which are possibly accelerating by anthropogenic activities. The study is a research initiative driven by impacts of poor or non-existent planning/ regulation practices to produce insights despite the sparsity of the available data and the unknown baseline. A methodology is given, which seeks 'total information management' by pooling together the following five dimensions: (i) a perceptual model to collect existing knowledge-base; (ii) a conceptual model to analyse a sample of ion-concentrations by a set of existing techniques (e.g. statistical, graphical and multivariate analysis); (iii) risk cells to contextualise each contaminant; (iv) "soft modelling" to firm up information by learning from convergences and/or divergences within the conceptual model; and (v) study the processes within each risk cell through the OSPRC framework (Origins , Sources, Pathways, Receptors and Consequence). The research caters for inherent variabilities in the study area by 15 risk cells delineated within the boundaries of confined, unconfined and hard-rock aquifers as follows: 4 risk cells account for minor ions of nitrate-N pollution of anthropogenic origins; 6 for minor ions of fluoride and 5 for trace ions of geogenic arsenic anomalies. It further identifies the possibility of anthropogenic activities encouraging geogenic anomalies. The findings are presented as a descriptive model but this will be transformed into quantitative models in due course when more data become available. [ABSTRACT FROM AUTHOR]

Nitrate pollution of groundwater through spatial models is investigated in this paper by using a sample of nitrate values at monitoring wells using the data from four seasons of a year, in which data are sparse. Two spatial modelling strategies are formulated at two levels, in which Strategy 1 comprises: three variations of kriging-based models (ordinary kriging, simple kriging and universal kriging), which are constructed at Level 1 to predict nitrate concentrations; and a Multiple Co-Kriging (MCoK) model is used at Level 2 to enhance the accuracy of the predictions. Strategy 2 is also at two levels but employs Indicator Kriging (IK) at Level 1 as a probabilistic spatial model to predict areas at risk of exceeding two thresholds of 37.5 mg/L and 50 mg/L of nitrate concentration, and Multiple Co-Indicator Kriging (MCoIK) at Level 2 for a better accuracy. The improvements at Level 2 for both strategies are remarkable and hence they are used to gain an insight into inherent problems. The results of a study delineate areas with excessive nitrate concentrations, which are in the vicinity of urban areas and hence reflect poor planning practices since the 1990s. The results further reveal the patterns on sensitivities to seasonal variations driven by aquifer recharge and strong dilution processes in spring times; and on the role of pumpage impacting aquifers giving rise to possible hotspots of nitrate concentrations. Image 1 • Formulate Strategy 1 for spatial modelling of groundwater nitrate using Co-Kriging (MCoK). • Strategy 2 for probabilistic modelling of groundwater nitrate by Co-Indicator Kriging (MCoIK). • Inclusive Multiple Models (IMM) are used to improve accuracy even with sparse data. • IMM improves the results significantly compared to conventional geostatistical methods. • The model learning is that excessive nitrate is identified in the vicinity of urban areas. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A modelling strategy is formulated at two level to predict Ground Water Level (GWL) • Bayesian Model Averaging (BMA) combines three Artificial Intelligence (AI) models. • AI models are at Level 1 and comprise ANN, SVM and Neuro-fuzzy; BMA is at Level 2. • BMA learns from the data, quantifies uncertainties and sheds light to GWL declines. • It is argued that the decline in aquifer state can be arrested by a set of plans. Abstract A modelling strategy is formulated, which collectively consists of separate Multiple Models (MM) and uses Bayesian Model Averaging (BMA) to combine these MMs to learn from data. The procedure is at two levels: at Level 1, three Artificial Intelligence (AI) models are constructed, which comprise Artificial Neural Network (ANN), Sugeno Fuzzy Logic (SFL) and Multiple-Neuro-Fuzzy (Multi-NF) but their outputs are directed to BMA at the next level; at Level 2, BMA is used to combine ANN, SFL and Multi-NF for better predictions and with facilities for quantifying uncertainty. The model performance is tested using the data from Urmia aquifer in the West Azerbaijan province, northwest Iran, where due to the absence of participatory water usage management practices both Lake Urmia and its surrounding 12 aquifers (including the study area) are distressed. The modelling strategy and its results on Urmia aquifer provides an insight to the study area and will be used to investigate ways of arresting the decline in the water table of the aquifer but this should be feasible only by developing a series of management plans, including basin management plans, aquifer management plans, drought plans, water cycle studies. Under such an integrated management system, the model developed here is demonstrably well-placed to serve as an operational management tool for the aquifer. [ABSTRACT FROM AUTHOR]

Abstract An investigation is presented to improve on the performances of the Basic DRASTIC Framework (BDF) and its variation by the Fuzzy-Catastrophe Framework (FCF), both of which provide an estimate of intrinsic aquifer vulnerabilities to anthropogenic contamination. BDF prescribes rates and weights for 7 data layers but FCF is an unsupervised learning framework based on a multicriteria decision theory by integrating fuzzy membership function and catastrophe theory. The challenges in the paper include: (i) the study area comprises confined and unconfined aquifers and (ii) Artificial Intelligence (AI) is used to run Multiple Framework (AIMF) in order to map specific vulnerability due to a specific contaminant. Predicted results by AIMF are referred to as Specific Vulnerability Indices, as the learned VIs are referenced to site-specific nitrate-N. The results show that correlation coefficient between BDF or FCF with nitrate-N is lower than 0.7 but the AIMF strategy improves it to greater than 0.95. The results are evidence for the proof-of-concept for transforming frameworks to models capable of further learning. Graphical abstract Image Highlights • Basic DRASTIC Framework (BDF) is used to represent intrinsic vulnerability. • Fuzzy-Catastrophe Frameworks (FCF) uses decision theory for a variation of BDF. • Correlation of BDF/FCF with specific vulnerability in terms of nitrate-N is poor. • AIMF uses BDF/FCF and nitrate-N to map specific vulnerability. • AIMF improves correlation in multi-aquifers owing to its further learning scope. [ABSTRACT FROM AUTHOR]

Proof-of-concept is presented in this paper to a methodology formulated for indexing risks to groundwater aquifers exposed to impacts of diffuse contaminations from anthropogenic and geogenic origins. The methodology is for mapping/indexing, which refers to relative values but not their absolute values. The innovations include: (i) making use of the Origins-Source-Pathways-Receptors-Consequences (OSPRC) framework; and (ii) dividing a study area into modular Risk (OSPRC) Cells to capture their idiosyncrasies with different origins. Field measurements are often sparse and comprise pollutants and water table, which are often costly; whereas supplementary data are general-purpose data, which are widely available. Risk mapping for each OSPRC cell is processed by dividing a study area into pixels and for each pixel, the risk from both anthropogenic and geogenic origins are indexed by using algorithms related to: (i) Vulnerability Indices (VI), which identify the potential for risk exposures at each pixel; and (ii) velocity gradient, which expresses the potency to risk exposures across the risk cell. The paper uses DRASTIC for anthropogenic VI but introduces a new framework for geogenic VI. The methodology has a generic architecture and is flexible to modularise risks involving any idiosyncrasies in a generic way in any site exposed to environmental pollution risks. Its application to a real study area provides evidence for the proof-of-concept for the methodology by a set of results that are fit-for-purpose and provides an insight into the study area together with the identification of its hotspots. [ABSTRACT FROM AUTHOR]