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Combining Machine Learning And Simulation Modelling For Better Predictions Of Crop Yield And Farmer Income, Thomas Berger
Combining Machine Learning And Simulation Modelling For Better Predictions Of Crop Yield And Farmer Income, Thomas Berger
International Congress on Environmental Modelling and Software
Machine learning methods have proven to be very effective in identifying patterns and implicit dependencies in complex situations with many parameters and in providing correct classifications, predictions or decision aids with the models learned. In practice, however, the large amounts of correctly labelled training data required for such approaches are often not available. In collaboration with farm holdings in South-West Germany, we develop and test a new approach in which existing operational knowledge codified in simulation models is combined iteratively with the increasing insights of learned models. Extensive synthetic training data sets are generated by bioeconomic simulation models using high-performance …
Artificial Intelligence For Intelligent Agriculture, Ramana Lingampally
Artificial Intelligence For Intelligent Agriculture, Ramana Lingampally
International Congress on Environmental Modelling and Software
Low crop yield is a growing concern all over the world. One of the reasons for this is lack of precise knowledge and preparedness among farmers about different factors that affect crop yield. Worsening weather conditions and ecological imbalances only made the matters worse. In a country like India where small and marginal farmers account for more than 80% of the farming community face several challenges with rising temperature and unpredictable rainfall. Many a time seed germination process takes a hit resulting in total crop failure or low crop yield. Numerous studies have been carried out to improve the crop …
Artificial Intelligence For Intelligent Agriculture, Ramana Lingampally
Artificial Intelligence For Intelligent Agriculture, Ramana Lingampally
International Congress on Environmental Modelling and Software
Low crop yield is a growing concern all over the world. One of the reasons for this is lack of precise knowledge and preparedness among farmers about different factors that affect crop yield. Worsening weather conditions and ecological imbalances only made the matters worse. In a country like India where small and marginal farmers account for more than 80% of the farming community face several challenges with rising temperature and unpredictable rainfall. Many a time seed germination process takes a hit resulting in total crop failure or low crop yield. Numerous studies have been carried out to improve the crop …
Computationally Efficient Ann As A Realistic Surrogate Of Modflow-Uzf For Integration With The Geomodsim River Basin Management Model, Faizal Rohmat, John W. Labadie, Timothy K. Gates
Computationally Efficient Ann As A Realistic Surrogate Of Modflow-Uzf For Integration With The Geomodsim River Basin Management Model, Faizal Rohmat, John W. Labadie, Timothy K. Gates
International Congress on Environmental Modelling and Software
The productivity of irrigated agriculture in Colorado’s Lower Arkansas River Basin (LARB), along with similar basins throughout the western U.S., is threatened by salinity and water logging problems resulting in reduced crop yields and abandoned cropland. In addition, over-irrigation and seepage from unlined canals has resulted in elevated concentrations of nutrients and trace elements, such as selenium, from underlying marine shales into groundwater and the river that exceed environmental standards. Intensive data collection and modeling efforts by Colorado State University in the LARB over the past 20 years have resulted in development of the river basin management model GeoMODSIM, along …
Incorporating Compressive Strength Models Into Multi-Objective Optimization: An Approach For Mixture Proportioning Of Sustainable Concrete Mixtures, Mikaela Ann Derousseau, Joseph Kasprzyk, Wil V. Srubar Iii
Incorporating Compressive Strength Models Into Multi-Objective Optimization: An Approach For Mixture Proportioning Of Sustainable Concrete Mixtures, Mikaela Ann Derousseau, Joseph Kasprzyk, Wil V. Srubar Iii
International Congress on Environmental Modelling and Software
Concrete mixtures are complex material systems with a multitude of characteristics that decisionmakers may deem important. These characteristics can include economic, environmental, and physical properties of a concrete mixture. This paper utilizes a new hybrid approach for both modelling important physical properties of concrete and incorporating these prediction models in to a multi-objective optimization problem for optimizing concrete mixture performance. Specifically, a tree-based, random forest statistical model is employed to predict the compressive strength of concrete. Mixtures. Compressive strength is predicted as a function of the proportions of the mixture constituents because these relationships are known to be complex and …
Combining Socio-Economic And Ecological Modelling To Inform Natural Resource Management Strategies, Johannes P. M. Heinonen, Justin M. J. Travis, Steve M. Redpath, Michelle A. Pinard
Combining Socio-Economic And Ecological Modelling To Inform Natural Resource Management Strategies, Johannes P. M. Heinonen, Justin M. J. Travis, Steve M. Redpath, Michelle A. Pinard
International Congress on Environmental Modelling and Software
Effective management of natural resources requires understanding both the dynamics of the natural systems being subjected to management and the decision-making behaviour of stakeholders who are involved in the management process. We suggest that simulation modelling techniques can provide a powerful method platform for the transdisciplinary integration of ecological, economic and sociological aspects that is needed for exploring the likely outcomes of different management approaches and options. A concise review of existing literature on ecological and socio-economic modelling and approaches at the interface of these fields is presented followed by a framework coupling an individual-based ecological model with an agent-based …
A Method For Comparing Data Splitting Approaches For Developing Hydrological Ann Models, Wenyan Wu, Robert May, Graeme C. Dandy, Holger R. Maier
A Method For Comparing Data Splitting Approaches For Developing Hydrological Ann Models, Wenyan Wu, Robert May, Graeme C. Dandy, Holger R. Maier
International Congress on Environmental Modelling and Software
Data splitting is an important step in the artificial neural network (ANN)development process whereby data are divided into training, test and validationsubsets to ensure good generalization ability of the model. Considering that onlyone split of data is typically used when developing ANN models, data splitting hasa significant impact on the performance of the final model by potentially introducingbias and variance into the model development process. Therefore, it is important tofind a robust data splitting method which results in an ANN model that representsthe underlying data generation process of a given dataset. In practice, ANN modelsdeveloped using different data splitting methods …
An Integrated, Fast And Easily Useable Software Toolbox Allowing Comparative And Complementary Application Of Various Parameter Sensitivity Analysis Methods, Christian Fischer, Sven Kralisch, P. Krause, Wolfgang-Albert Flügel
An Integrated, Fast And Easily Useable Software Toolbox Allowing Comparative And Complementary Application Of Various Parameter Sensitivity Analysis Methods, Christian Fischer, Sven Kralisch, P. Krause, Wolfgang-Albert Flügel
International Congress on Environmental Modelling and Software
The analysis of parameter sensitivity in environmental models is an excellent technique to assess a model’s behavior, to determine its potential utility, to support its calibration, and to identify areas of improvement. Recent work on comparing sensitivity analysis methods shows that the methods available today are complementary, i.e. multiple methods should be used to assess a model. We present a software toolbox for global sensitivity analysis which supports the investigation of parameter sensitivity using different methods. The toolbox includes Regional Sensitivity Analysis, Morris Method, and a Sobols method. The majority of these methods require input data from a Monte-Carlo- Sampling …
A Neural Network Approach For Selecting The Most Relevant Variables For Foaming In Anaerobic Digestion, Jordi Dalmau, Joaquim Comas, Ignasi Rodríguez-Roda, Eric Latrille, Jean-Philippe Steyer
A Neural Network Approach For Selecting The Most Relevant Variables For Foaming In Anaerobic Digestion, Jordi Dalmau, Joaquim Comas, Ignasi Rodríguez-Roda, Eric Latrille, Jean-Philippe Steyer
International Congress on Environmental Modelling and Software
Activated sludge processes are complex biological systems in which organic matter and nutrients (nitrogen and phosphorous) are removed from wastewater. One of the most common alternatives for the treatment of waste from activated sludge systems is Anaerobic Digestion (AD). AD is even a more complex biological process. One of the most important problems which can appear in anaerobic digesters is foaming. In the literature there is not a big agreement on the foaming causes. Therefore, the aim of the paper is to apply a methodology based on artificial neural networks to determine the most relevant variables for foaming diagnosis in …
Using Artificial Neural Networks To Predict Local Disease Risk Indicators With Multi-Scale Weather, Land And Crop Data, A. Pechenick, Donna M. Rizzo
Using Artificial Neural Networks To Predict Local Disease Risk Indicators With Multi-Scale Weather, Land And Crop Data, A. Pechenick, Donna M. Rizzo
International Congress on Environmental Modelling and Software
The risk of fungal and bacterial crop disease can be predicted using risk models with specific environmental parameters such as temperature, relative humidity, solar radiation, wind speed, and leaf wetness duration (LWD). LWD has long been recognized as key in the management of crop disease. Air temperature and wetness influence the majority of fungal plant diseases. Wetness also impacts insect populations, as well as pollution deposits. Many parameters are well understood, readily defined, and easily measured. Unfortunately, LWD is a complex phenomenon, due to its spatial and temporal variability within a crop canopy. The inconvenience and uncertainty associated with monitoring …
Reducing Uncertainty In Ecological Niche Models With Ann Ensembles, Andrei Kirilenko, R. S. Hanley
Reducing Uncertainty In Ecological Niche Models With Ann Ensembles, Andrei Kirilenko, R. S. Hanley
International Congress on Environmental Modelling and Software
Transformation of spatial distributions of species is a key element for understanding global and regional impact of climate change on environment in the future. Yet little is known about current distributions of species, especially in relation with physical parameters of environment. This is primarily due to limited availability of data on species occurrences, which prevents reconstruction of their geographical distribution with standard methods of spatial statistics. To address this problem, specialized applications called distributional models, based on the hypothesis of fundamental niche, are being developed. We have built a new tool for predicting species’ geographical distributions based on presence-only data. …
New Sensor System For Environmental Monitoring: The Potentiometric Electronic Tongue, M. Gutiérrez, J. M. Gutiérrez, S. Alegret, L. Leija, P. R. Hernández, R. Muñoz, M. Del Valle
New Sensor System For Environmental Monitoring: The Potentiometric Electronic Tongue, M. Gutiérrez, J. M. Gutiérrez, S. Alegret, L. Leija, P. R. Hernández, R. Muñoz, M. Del Valle
International Congress on Environmental Modelling and Software
The use of electronic tongues is proposed for two different environmentalmonitoring applications. This approach in chemical analysis consists of an array ofnonspecific sensors coupled with a multivariate calibration tool. In this case, the proposedarrays were formed by potentiometric sensors based on polymeric membranes (PVC) andthe subsequent cross-response processing was based on a multilayer artificial neuralnetwork (ANN) model. Special attention was paid in compensating temperature effects andresponse drifts in the sensors. In addition, in order to demonstrate the viability of theproposed systems for automated remote applications, the use of data transmission byradiofrequency has been tested.
Radar-Based Surface Soil Moisture Retrieval Over Agricultural Used Sites – A Multi-Sensor Approach, M. Pause, Martin Volk, K. Schulz
Radar-Based Surface Soil Moisture Retrieval Over Agricultural Used Sites – A Multi-Sensor Approach, M. Pause, Martin Volk, K. Schulz
International Congress on Environmental Modelling and Software
Spatial distributed information of isochronal surface soil moisture is very important to compensate the inaccuracy of initial conditions and the uncertainty of parameters in hydrological models at the landscape scale. In this paper the conceptual procedure to derive spatial distributed surface soil moisture values from synthetic aperture radar data by using ancillary optical remote sensing data is presented. Different biophysical vegetation parameters like vegetation water content and leaf area index overlay the soil moisture information on the microwave signal and hamper the application of many existing models. Therefore the objective of the proposed study is to test the performance of …
Studying And Predicting Quality Of Life Atmospheric Parameters With The Aid Of Computational Intelligence Methods, Kostas Karatzas, D. Voukantsis
Studying And Predicting Quality Of Life Atmospheric Parameters With The Aid Of Computational Intelligence Methods, Kostas Karatzas, D. Voukantsis
International Congress on Environmental Modelling and Software
Air quality management is among the most challenging problems in terms of analysis and modelling. Air quality modelling and forecasting is directly affected by the highly nonlinear relationships between pollutants and weather, while in many cases there is insufficient domain knowledge due to the influences of local conditions. As atmospheric quality has an impact on the quality of life of millions of people, the ability to reveal interrelationships between parameters that influence environmental decision making is very important. In addition, forecasting of such parameters for the purpose of early warning and health risk prevention is of paramount importance for sensitive …
Application Of An Artificial Neural Network For Analysis Of Subsurface Contamination At The Schuyler Falls Landfill, Ny, Lance E. Besaw, Donna M. Rizzo, P. J. Mouser
Application Of An Artificial Neural Network For Analysis Of Subsurface Contamination At The Schuyler Falls Landfill, Ny, Lance E. Besaw, Donna M. Rizzo, P. J. Mouser
International Congress on Environmental Modelling and Software
Current subsurface site characterization, plume delineation, remediation designs and monitoring network designs that rely on a limited, albeit large, number of sparsely collected data, tend to be expensive, cumbersome and frequently inadequate for solving multi-objective, long-term environmental management problems. We present a subsurface characterization methodology that integrates multiple types of data using a modified counterpropagation artificial neural network (ANN) to provide parameter estimates and delineate groundwater contamination at a leaking landfill. Apparent conductivity survey data and hydrochemistry data (i.e. heavy metals, BOD5,20, chloride concentration, etc.) are used to estimate the extent of subsurface contamination at the Schuyler Falls Landfill, located …
On The Prediction Of The Ecological Status Of Human-Altered Streams And Its Rule-Based Interpretation, Terence A. Etchells, Alfredo Vellido, E. Martí, Paulo J. G. Lisboa, Joaquim Comas
On The Prediction Of The Ecological Status Of Human-Altered Streams And Its Rule-Based Interpretation, Terence A. Etchells, Alfredo Vellido, E. Martí, Paulo J. G. Lisboa, Joaquim Comas
International Congress on Environmental Modelling and Software
The recent Water Framework Directive of the European Union set year 2015 as their target for freshwater and coastal ecosystems all across Europe to achieve good ecological status. This study concerns the analysis of the empirical data from the knowledge base of an environmental decision support system developed within the European project STREAMES. These data, which come from several low-order streams located mostly on the Mediterranean region, consist of measurements of several physical, chemical and biological variables. We aim to classify these data according to the ecological status of the streams they correspond to, where stream nutrient retention efficiency (a …
Utilizing Artificial Neural Networks To Backtrack Source Location, Zhiqiang Li, Donna M. Rizzo, Nancy Hayden
Utilizing Artificial Neural Networks To Backtrack Source Location, Zhiqiang Li, Donna M. Rizzo, Nancy Hayden
International Congress on Environmental Modelling and Software
Determining the location of the contaminant source is important for improving remediation and site management decisions at many contaminated groundwater sites. At large sites, numerical flow and transport models have been developed that use historical measurement data for calibration. A well-calibrated model is useful for predicting plume migration and other management purposes; however, it is difficult to back out the source with these forward flow and transport models. We present a novel technique utilizing Artificial Neural Networks (ANNs) to backtrack source location and earlier plume concentrations from recent plume information. For proof-of-concept, two tracer tests (a non-point-source and a point-source) …
Application Of An Artificial Neural Network And Stochastic Simulation At The Schuyler Falls Landfill, Ny, Lance E. Besaw, Donna M. Rizzo
Application Of An Artificial Neural Network And Stochastic Simulation At The Schuyler Falls Landfill, Ny, Lance E. Besaw, Donna M. Rizzo
International Congress on Environmental Modelling and Software
Stochastic conditional simulation techniques have been developed to address issues of risk and uncertainty associated with spatially distributed phenomena in earth sciences (e.g. hydraulic conductivity, contaminant concentration, etc.). These techniques (e.g. Gaussian simulation, p-field simulation, turning bands algorithm, etc) are used to assess risk and uncertainty resulting from sparsely sampled field measurements, high measurement variability and incomplete site knowledge. Scientists and engineers are able to model the spatial continuity and quantify uncertainty associated with spatially distributed phenomena through the generation and analysis of many equiprobable stochastic simulations. Uncertainty in contaminated site characterization is of serious concern in environmental engineering. Stochastic …
Application Of An Artificial Neural Network For Analysis Of Subsurface Contamination At The Schuyler Falls Landfill, Ny, Lance E. Besaw, Donna M. Rizzo, P. J. Mouser
Application Of An Artificial Neural Network For Analysis Of Subsurface Contamination At The Schuyler Falls Landfill, Ny, Lance E. Besaw, Donna M. Rizzo, P. J. Mouser
International Congress on Environmental Modelling and Software
Current subsurface site characterization, plume delineation, remediation designs and monitoring network designs that rely on a limited, albeit large, number of sparsely collected data, tend to be expensive, cumbersome and frequently inadequate for solving multi-objective, long-term environmental management problems. We present a subsurface characterization methodology that integrates multiple types of data using a modified counterpropagation artificial neural network (ANN) to provide parameter estimates and delineate groundwater contamination at a leaking landfill. Apparent conductivity survey data and hydrochemistry data (i.e. heavy metals, BOD5,20, chloride concentration, etc.) are used to estimate the extent of subsurface contamination at the Schuyler Falls Landfill, located …
On The Prediction Of The Ecological Status Of Human-Altered Streams And Its Rule-Based Interpretation, Terence A. Etchells, Alfredo Vellido, E. Martí, Paulo J. G. Lisboa, Joaquim Comas
On The Prediction Of The Ecological Status Of Human-Altered Streams And Its Rule-Based Interpretation, Terence A. Etchells, Alfredo Vellido, E. Martí, Paulo J. G. Lisboa, Joaquim Comas
International Congress on Environmental Modelling and Software
The recent Water Framework Directive of the European Union set year 2015 as their target for freshwater and coastal ecosystems all across Europe to achieve good ecological status. This study concerns the analysis of the empirical data from the knowledge base of an environmental decision support system developed within the European project STREAMES. These data, which come from several low-order streams located mostly on the Mediterranean region, consist of measurements of several physical, chemical and biological variables. We aim to classify these data according to the ecological status of the streams they correspond to, where stream nutrient retention efficiency (a …
Utilizing Artificial Neural Networks To Backtrack Source Location, Zhiqiang Li, Donna M. Rizzo, Nancy Hayden
Utilizing Artificial Neural Networks To Backtrack Source Location, Zhiqiang Li, Donna M. Rizzo, Nancy Hayden
International Congress on Environmental Modelling and Software
Determining the location of the contaminant source is important for improving remediation and site management decisions at many contaminated groundwater sites. At large sites, numerical flow and transport models have been developed that use historical measurement data for calibration. A well-calibrated model is useful for predicting plume migration and other management purposes; however, it is difficult to back out the source with these forward flow and transport models. We present a novel technique utilizing Artificial Neural Networks (ANNs) to backtrack source location and earlier plume concentrations from recent plume information. For proof-of-concept, two tracer tests (a non-point-source and a point-source) …
Application Of An Artificial Neural Network And Stochastic Simulation At The Schuyler Falls Landfill, Ny, Lance E. Besaw, Donna M. Rizzo
Application Of An Artificial Neural Network And Stochastic Simulation At The Schuyler Falls Landfill, Ny, Lance E. Besaw, Donna M. Rizzo
International Congress on Environmental Modelling and Software
Stochastic conditional simulation techniques have been developed to address issues of risk and uncertainty associated with spatially distributed phenomena in earth sciences (e.g. hydraulic conductivity, contaminant concentration, etc.). These techniques (e.g. Gaussian simulation, p-field simulation, turning bands algorithm, etc) are used to assess risk and uncertainty resulting from sparsely sampled field measurements, high measurement variability and incomplete site knowledge. Scientists and engineers are able to model the spatial continuity and quantify uncertainty associated with spatially distributed phenomena through the generation and analysis of many equiprobable stochastic simulations. Uncertainty in contaminated site characterization is of serious concern in environmental engineering. Stochastic …
A Statistical Input Pruning Method For Artificial Neural Networks Used In Environmental Modelling, G. B. Kingston, Holger R. Maier, M. F. Lambert
A Statistical Input Pruning Method For Artificial Neural Networks Used In Environmental Modelling, G. B. Kingston, Holger R. Maier, M. F. Lambert
International Congress on Environmental Modelling and Software
Artificial neural networks (ANNs) provide a useful and effective tool for modelling poorly understood and complex processes, such as those that occur in nature. However, developing an ANN to properly model the desired relationship is not a trivial task. Selection of the correct causal inputs is one of the most important tasks faced by neural network practitioners, but as knowledge regarding the relationships modelled by ANNs is generally limited, selecting the appropriate inputs is also one of the most difficult tasks in the development of an ANN. Many of the methods available for assessing the significance of potential input variables …
A Fast Evolutionary-Based Meta-Modelling Approach For The Calibration Of A Rainfall-Runoff Model, Soon-Thiam Khu, Dragan Savic, Yang Liu, Henrik Madsen
A Fast Evolutionary-Based Meta-Modelling Approach For The Calibration Of A Rainfall-Runoff Model, Soon-Thiam Khu, Dragan Savic, Yang Liu, Henrik Madsen
International Congress on Environmental Modelling and Software
Population-based search methods such as evolutionary algorithm, shuffled complex algorithm, simulated annealing and ant colony search are increasing used as automatic calibration methods for a wide range of water and environmental simulation models. However, despite the advances in computer power, it may still be impractical to rely exclusively on computationally expensive (time consuming) simulation for many real world complex problems. This paper proposed the use of meta-models to replace numerical simulation models for the purpose of calibration. Meta-models are essentially “model of the model”. The meta-model used in this study is the artificial neural network and, when coupled with genetic …
An Evolutionary-Based Real-Time Updating Technique For An Operational Rainfall-Runoff Forecasting Model, Soon-Thiam Khu, Edward Keedwell, Oliver Pollard
An Evolutionary-Based Real-Time Updating Technique For An Operational Rainfall-Runoff Forecasting Model, Soon-Thiam Khu, Edward Keedwell, Oliver Pollard
International Congress on Environmental Modelling and Software
Error-correction is widely known to be one of the effective methods of real-time updating and tends to be the easiest method to implement and couple with existing simulation models. Methods such as autoregressive (AR) or autoregressive integrated moving average (ARIMA) have been widely used but the main disadvantage of such approaches is the prior assumption of the form of error correlation. Genetic programming (GP), a relatively new evolutionary-based technique, can be used to generate a suitable expression linking the observations, simulation model results and the error in the simulation for the purpose of error correction. In this study, GP functions …
Benthic Macroinvertebrates Modelling Using Artificial Neural Networks (Ann): Case Study Of A Subtropical Brazilian River, D. Pereiraa
International Congress on Environmental Modelling and Software
Back-propagation Artificial Neural Networks (ANN) were tested with the aim of modelling the occurrence of benthic macroinvertebrate families in a south Brazilian river. The dataset, consisting of 67 sets of observations of macroinvertebrate abundance (families Hydrobiidae, Tubificidae, Chironomidae, Baetidae and Leptophlebiidae) and water quality variables (pH, temperature, dissolved oxygen, biochemical oxygen demand, nitrate, phosphate, total solids, turbidity and fecal coliforms), was collected at eleven sampling sites in the Sinos River Basin during 1991-1993. Five different ANN architectures, with one hidden layer and 2, 5, 10, 20 and 25 neurons were tested. The ANN models were trained using the gradient descendent …
Relative Performance Of Empirical Predictors Of Daily Precipitation, Tereza Cavazos, Bruce Hewitson
Relative Performance Of Empirical Predictors Of Daily Precipitation, Tereza Cavazos, Bruce Hewitson
International Congress on Environmental Modelling and Software
The urgent need for realistic regional climate change scenarios has led to a plethora of empirical downscaling techniques. In many cases widely differing predictors are used, making comparative evaluation difficult. Additionally, it is not clear that the chosen predictors are always the most important. These limitations and the lack of physics in empirical downscaling highlight the need for a systematic assessment of the performance of physically meaningful predictors and their relevance in surface climate parameters. Accordingly, the objectives of this study are twofold: To examine the skill and errors of 29 individual atmospheric predictors of area-averaged daily precipitation in 15 …
Solving The Inverse Problem In Stochastic Groundwater Modelling With Artificial Neural Networks, C. Rajanayaka, S. Samarasinghe, D. Kulasiri
Solving The Inverse Problem In Stochastic Groundwater Modelling With Artificial Neural Networks, C. Rajanayaka, S. Samarasinghe, D. Kulasiri
International Congress on Environmental Modelling and Software
In this paper, prediction capability of a hybrid Artificial Neural Networks (ANN) was investigatedto solve the groundwater inverse problem. Initially, a Multi Layer Perceptron (MLP) network was developedand it was found that network produced better results when the target range of the parameters is smaller.Therefore, a Self-Organising Network (SON) was used to identify the objective subrange of the parameterand then the MLP model was employed to obtain final estimates. The data for the ANN was obtained from anumerical model that was utilised to simulate the solute transport in saturated groundwater flow. The forwardproblem of the numerical model was solved to …
Flood Forecasting Using Artificial Neural Networks In Black-Box And Conceptual Rainfall-Runoff Modelling, Elena Toth, Armando Brath
Flood Forecasting Using Artificial Neural Networks In Black-Box And Conceptual Rainfall-Runoff Modelling, Elena Toth, Armando Brath
International Congress on Environmental Modelling and Software
The paper presents a comparison of lumped runoff modelling approaches, aimed at the realtime forecasting of flood events, based on or integrating Artificial Neural Networks (ANNs). ANNs are used in two ways: (a) as black-box type runoff simulation models or (b) for the real-time improvement of the discharge forecasts issued by a conceptual-type rainfall-runoff model. As far as the coupling of ANNs with a conceptual model is concerned, feed forward neural networks are used as univariate time-series analysis techniques both for forecasting the future rainfall values to be provided as input to the hydrological model and for updating the river …
The Jdevs Modelling And Simulation Environment, J. B. Filippi, M. Delhom, F. Bernardi
The Jdevs Modelling And Simulation Environment, J. B. Filippi, M. Delhom, F. Bernardi
International Congress on Environmental Modelling and Software
This paper describes the JDEVS modelling and simulation environment. JDEVS has been developedfor over a year to serve as an experimental framework for natural systems modelling techniques. It enablesdiscrete-event, general purpose, object-oriented, component based, GIS connected, collaborative, visual simulationmodel development and execution. The sample models implementations shows that this experimentalenvironment might be used to solve any complex problems solvable by discrete-event simulation and is especiallysuited for natural system modelling and simulation. Already, hierarchical block (static) and cellularmodels can be modelled and simulated within the environment. We are now extending those capabilities withthe development of a multi-layered modelling paradigm for spatially …