Comparative study of different wavelets for developing parsimonious Volterra model for rainfall-runoff simulation

Although the Volterra models are non-parsimonious ones, they are being used because they can mimic dynamics of complex systems. However, applying and identification of the Volterra models using data may result in overfitting problem and uncertainty. In this investigation we evaluate capability of different wavelet forms for decomposing and compressing the Volterra kernels in order to overcome this problem by reducing the number of the model coefficients to be estimated and generating smooth kernels. A simulation study on a rainfall?runoff process over the Cache River watershed showed that the method performance is successful due to multi-resolution capacity of the wavelet analysis and high capability of the Volterra model. The results also revealed that db2 and sym2 wavelets have the same high potential in improving the linear Volterra model performance. However, QS wavelet was more successful in yielding smooth kernels. Moreover, the probability of overfitting while identifying the nonlinear Volterra model may be less than the linear model.     

Possibility of the Application of Danube,Sava and Morava River Area Shallow Aquifer Groundwater (Serbia) in the Irrigation Purposes

Water Resources - This paper presents the results of the groundwater quality assessment, on the Serbian Danube, Sava and Morava River water area, for irrigation purposes. The analysis was done at...     

Vorticity analysis in the Zagros orogen,Shiraz area,Iran

International Journal of Earth Sciences - Quantitative vorticity analyses in orogenic belts are essential for studying the kinematics of deformation and can be performed using a range of methods....     

Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River

Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO₄), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH₄-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d-factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems.     

3D Gravity Inversion of Northern Sinai Peninsula: A Case Study

The Sinai Peninsula has attracted the attention of many geological and geophysical studies as it is influenced and bounded by major tectonic events. Those are (1) the Mesozoic to Early Cenozoic tectonically active opening of the Tethys, (2) the Late Cretaceous to Early Tertiary (Laramide) Syrian arc system, due to closing of the Tethys (3) the Oligo-Miocene Gulf of Suez rifted basin, and (4) the Late Miocene to Recent transform Dead Sea–Gulf of Aqaba rift. Additionally, the shear zones inside Sinai such as the Ragabet El-Naam and Minsherah-Abu Kandu Shear Zones. Each of these major tectonic events has affected dramatically the structure evolution of the northern Sinai area. The present paper estimates the 3D density contrast model using the gravity data of northern Sinai. The estimated 3D density contrast model elucidated the peculiarities of the main structural elements in the region. The estimated 3D density contrast model showed the high and low gravity anomalies that form the main mountains and main valleys in northern Sinai. The estimated low density zones are in agreement with the inferred faults resulting from the first horizontal derivative. Comparing the 3D model with the tectonic history of the region and the results of the first horizontal derivative and least square separation increased the reliability of the model.     

Offshore sand sources for beach replenishment: Potential borrows on the continental shelf of the Eastern Gulf of Mexico

Erosion of sandy beaches is a worldwide problem that elicits innovative geoengineer‐ing techniques to reduce adverse impacts of shoreline retreat. Beach replenishment has emerged as the “soft”; shore‐stabilization technique of choice for mitigating beach erosion. This method of shore protection involves the addition of sand to the littoral sediment budget for sacrificial purposes. Because inland sand sources are often uneconomical or impractical to use, and known nearshore sources are limited, finding adequate quantities of suitable sand on the inner continental shelf is often vital to beach replenishment projects. The technical studies of survey and materials analysis that identify and delineate usable sand sources are sometimes almost as expensive as small‐project dredging, pumping, and placing the sand on the beach as fill. Inadequate quantity or substandard quality of shelf sand, as well as often‐prohibitive overhead expenses, thus compel shoreline managers to seek suitable sand sources offshore.

In the study area off the central‐west coast of Florida, offshore potential borrow areas (PBAs) were identified on the basis of studies conducted in reconnoitory and detailed phases. Sophisticated state‐of‐the‐art equipment used in this investigation provided more detailed subbottom mapping information than is normally obtained with conventional seismic equipment. An example of sand exploration studies was incorporated in a 215‐km² survey of offshore areas by conducting bathymetric surveys and subbottom seismic profiling, collecting jet probes, grab samples, and vibrocores, and analyzing sediment grading in subsamples from vibrocores. These combined analyses indicated that at least 8.8 ×10⁶ m³ of sand is available in potential borrow areas from 7.0 to 12 km offshore in water depths of 8.0 to 11.5 m. In the PBAs, mean grain size of sand falls into the range 0.13–0.53 mm, sorting averages 0.65–1.31ø, and the overall silt content varies from 3.9–8.5%. High silt contents (13–19%) mapped in some areas make these sedimentary deposits unsuitable as fill for artificial beach renourishment.     

Time series analysis of water quality parameters at Stillaguamish River using order series method

This study applied the time series analysis approach to model and predict univariate dissolved oxygen and temperature time series for four water quality assessment stations at Stillaguamish River located in the state of Washington. The order series method was applied to fulfill the normality assumption for modeling the univariate time series. Then, the AR(I)MA models were applied to study the stationary and nonstationary time series, the Auto-Regressive Fractionally Integrated Moving Average model was applied to study the time series with long memory. The results showed there existed three different structures for the univariate water quality time series at Stillaguamish River watershed. The identified time series model for each univariate water quality time series was found to be capable of predicting future values with reasonable accuracy. Overall, the time series modeling approach may be an efficient tool in assessment of the water quality in the river system.     

Testing the weak stationarity of a spatio-temporal point process

A common assumption in analyzing spatial and spatio-temporal point processes is stationarity, while in many real applications because of the environmental effects the stationarity condition is not often met. We propose two types of test statistics to test stationarity for spatio-temporal point processes, by adapting, Palahi, Pukkala & Mateu (2009) and by considering the square difference between observed and expected (under stationarity) intensities. We study the efficiency of the new statistics by simulated data, and we apply them to test the stationarity of real data.