Using the land transformation model to forecast vacant land

Growing or shrinking cities can experience increases in vacant land. As urban populations and boundaries fluctuate, holes can open in once tight urban areas. Many cities chase growth-oriented approaches to dealing with vacancies. It is critical to understand land-use alteration to accurately predict transformations of physical change in order to make better informed decisions about this phenomenon. This research utilizes the land transformation model (LTM), an artificial neural networking mechanism in Geographic Information Systems, to forecast vacant land. Variable influence on vacant land prediction and accuracy of the LTM is assessed by comparing input factors and patterns, using time-series data from 1990 to 2010 in Fort Worth, Texas, USA. Results indicate that the LTM can be useful in simulating vacant land-use changes but more precise mechanisms are necessary to increase accuracy. This will allow for more proactive decisions to better regulate the process of urban decline and regeneration.     

An assessment methodology for successional systems. I. Null models and the regulatory framework

Standard procedures for evaluating environmental impact involve comparison between before and after conditions or scenarios or between treatment and control site pairs. In many cases, however, endogenous directional change (natural succession) is expected to occur at a significant rate over the period of concern, particularly for manmade systems such as impoundments. Static evaluations do not provide an adequate approach to such problems. A new evaluation frame is proposed. Nominal system behavior over time is characterized by a stochastic envelope around a nominal trajectory. We show that both the state variance and the sampling variance can change over time. In this context, environmental regulations can be framed as constraints, targets, or conformance to ideal trajectories. Statistical tests for determining noncompliance are explored relative to process variance, sample error, and sample size. Criteria are elucidated for choosing properties to monitor, sample size, and sampling interval.     

CALCULATION OF NONLINEAR CONFIDENCE AND PREDICTION INTERVALS FOR GROUND-WATER FLOW MODELS1

ABSTRACT: A method is derived to efficiently compute nonlinear confidence and prediction intervals on any function of parameters derived as output from a mathematical model of a physical system. The method is applied to the problem of obtaining confidence and prediction intervals for manually-calibrated ground-water flow models. To obtain confidence and prediction intervals resulting from uncertainties in parameters, the calibrated model and information on extreme ranges and ordering of the model parameters within one or more independent groups are required. If random errors in the dependent variable are present in addition to uncertainties in parameters, then calculation of prediction intervals also requires information on the extreme range of error expected. A simple Monte Carlo method is used to compute the quantiles necessary to establish probability levels for the confidence and prediction intervals. Application of the method to a hypothetical example showed that includsion of random errors in the dependent variable in addition to uncertainties in parameters can considerably widen the prediction intervals.     

DERIVING THE NONLINEAR RISK-BENEFIT ALGORITHM FOR RESERVOIRS1

ABSTRACT: The Nonlinear Risk-Benefit (NRB) Algorithm includes risk as one of the objectives in a multiple-objective optimization problem. The NRB Algorithm is derived by extending the Surrogate Worth Trade-Off method to quadratic programming. This category of problem is common in water resources planning and design, especially multipurpose reservoir systems. Consequently, an example is given using the algorithm for optimally operating a multipurpose reservoir.     

COMPARISON OF PROCESS‐BASED AND ARTIFICIAL NEURAL NETWORK APPROACHES FOR STREAMFLOW MODELING IN AN AGRICULTURAL WATERSHED1

ABSTRACT: The performance of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) models in simulating hydrologic response was assessed in an agricultural watershed in southeastern Pennsylvania. All of the performance evaluation measures including Nash‐Sutcliffe coefficient of efficiency (E) and coefficient of determination (R²) suggest that the ANN monthly predictions were closer to the observed flows than the monthly predictions from the SWAT model. More specifically, monthly streamflow E and R² were 0.54 and 0.57, respectively, for the SWAT model calibration period, and 0.71 and 0.75, respectively, for the ANN model training period. For the validation period, these values were ?0.17 and 0.34 for the SWAT and 0.43 and 0.45 for the ANN model. SWAT model performance was affected by snowmelt events during winter months and by the model's inability to adequately simulate base flows. Even though this and other studies using ANN models suggest that these models provide a viable alternative approach for hydrologic and water quality modeling, ANN models in their current form are not spatially distributed watershed modeling systems. However, considering the promising performance of the simple ANN model, this study suggests that the ANN approach warrants further development to explicitly address the spatial distribution of hydrologic/water quality processes within watersheds.     

A MATHEMATICAL MODEL OF SALMONID SPAWNING HABITAT1

ABSTRACT: A simulation model [Salmonid Spawning Analysis Model (SSAM)] was developed as a management tool to evaluate the relative impacts of stream sediment load and water temperature on salmonid egg survival. The model is useful for estimating acceptable sediment loads to spawning habitat that may result from upland development, such as logging and agriculture. Software in common use in the USA were adapted for use in gravel bedded rivers and linked to simulate water temperature (the USFWS Instream Water Temperature, SNTEMP model) and water and sediment routing (the USAE Scour and Deposition in Rivers and Reservoirs, HEC-6 model, version 3.2). These models drive the redd (spawning nest) model (the USDA-ABS Sediment Intrusion Dissolved Oxygen SIDO model) which simulates sediment intrusion and dissolved oxygen concentration in the redd environment. The SSAM model predictions of dissolved oxygen and water temperature compared favorably with field data from artificial redds containing hatchery chinook salmon eggs.     

MODEL STUDIES OF SALT WATER INTRUSION1

Model studies were and are still being used to verify certain theories in ground-water flow systems in general. In complex cases, the model studies may be extremely useful especially when a theoretical rigorous analysis does not exist. The models cannot be considered entirely satisfactory due to the several drawbacks in each type in addition to the normal human errors in experimentation. This paper is concerned only with the viscous flow models. However, a brief summary of the other types of models, which may possibly be used in connection with salt water intrusion problems is given. It should be noted that some of such experiments are not directly related to the field of salt water intrusion. Two main types lie within this category: The gravity flow systems which are analogous to some phases of salt intrusion problems and problems in oil fields which bear general similarities to sea water intrusion zones. In oil fields, gas cycling studies give valuable information to sea water problems. Model studies are used by hydraulic engineers, geologists, petroleum engineers, physicists, foundation engineers and several other professional groups.     

HYDRO-POWER SYSTEM SIMULATION TECHNIQUE1

ABSTRACT. The non-power requirements of a large hydro-power system of multiple-purpose projects often conflict with the best power peaking operation. In order to schedule an optimum multiple-purpose operation, advanced procedures that necessitate the use of computers are required. Special techniques are used in a computer program developed in the Reservoir Control Center, North Pacific Division of the Corps of Engineers that provide the ability to define operating constraints in order of priority. These techniques are easily adapted to practical reservoir regulation problems so that the program is useful in daily reservoir regulation scheduling.     

DEVELOPMENT OF A BASIN GEOMORPHIC INFORMATION SYSTEM USING A TIN-DEM DATA STRUCTURE1

ABSTRACT: To make a distributed rainfall-runoff model, it is very important to build a model of topographic surface of a basin which takes account of the direction of water flow. In this paper, a geographic information system in hydrologic modeling, the BGIS (Basin Geomorphic Information Systems) are presented for modeling a river basin using a TIN-DEM (Triangulated Irregular Network - Digital Elevation Model) data structure. The BGIS have two core systems, which are the TIN-DEM generating system and the topographic analysis system. In the TIN-DEM generating system, landscapes are modeled as a set of contiguous non-overlapping terangular facets whose vertices are made up of points on a regular grid DEM and on river segments. These triangular facets are subdivided, if needed, so that each of them has only one side through which water flows out. The TIN-DEM generating system is made up of four modules, (1) a module for generating triangles from a grid DEM, (2) a module for getting rid of pits, (3) a module for joining discontinuous valley segments to a channel network, (4) a module for subdividing triangular facets. In the topographic analysis system, using datasets processed with the TIN-DEM generating system, a watershed source area for any segments in a stream network are delineated automatically, and topographic attributes of slopes, aspects, flow path lengths and upslope contributing areas are computed.     

Chemical Treatments of Natural Fiber for Use in Natural Fiber-Reinforced Composites: A Review

Studies on the use of natural fibers as replacement to man-made fiber in fiber-reinforced composites have increased and opened up further industrial possibilities. Natural fibers have the advantages of low density, low cost, and biodegradability. However, the main disadvantages of natural fibers in composites are the poor compatibility between fiber and matrix and the relative high moisture sorption. Therefore, chemical treatments are considered in modifying the fiber surface properties. In this paper, the different chemical modifications on natural fibers for use in natural fiber-reinforced composites are reviewed. Chemical treatments including alkali, silane, acetylation, benzoylation, acrylation, maleated coupling agents, isocyanates, permanganate and others are discussed. The chemical treatment of fiber aimed at improving the adhesion between the fiber surface and the polymer matrix may not only modify the fiber surface but also increase fiber strength. Water absorption of composites is reduced and their mechanical properties are improved.