Download or read book Flood Forecasting Using Artificial Neural Network ANN in Maran Pahang written by Nur Atiyah Dinie Mat Arifin and published by . This book was released on 2014 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Flood Forecasting Using Artificial Neural Networks written by P. Varoonchotikul and published by CRC Press. This book was released on 2017-10-02 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation considers various questions with respect to the effects of salinity on nutrification: what are the main inhibiting factors causing the effects, do all salts have similar effects, what is the maximum acceptable salt level, are ammonia oxidisers or nitrite oxidizers most sensitive to salt stress, can nitrifiers adapt to long term salt stress and are some specific nitrifiers more resistant to salt stress than others? Research was carried out at laboratory scale and in full-scale plants and modelling was employed in both phases to provide a mathematical description for salt inhibition on nitrification and to facilitate the comparison. The result has led to an improved understanding of the effect of salinity on nitrification. The results can be used to improve the sustainability of the exisisting wastewater treatment plants operated under salt stress.
Download or read book Flood Forecasting Using Machine Learning Methods written by Fi-John Chang and published by MDPI. This book was released on 2019-02-28 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.
Download or read book Flood Forecasting Using Artificial Neural Networks written by P Varoonchotikul and published by CRC Press. This book was released on 2003-01-01 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: Flood disasters continue to occur in many countries in the world and cause tremendous casualties and property damage. To mitigate the effects of floods, a range of structural and non-structural measures have been employed including dykes, channelling, flood-proofing property, land-use regulation and flood warning schemes. Such schemes can include the use of Artificial Neural Networks (ANN) for modelling the rainfall run-off process as it is a quick and flexible approach which gives very promising results. However, the inability of ANN to extrapolate beyond the limits of the training range is a serious limitation of the method, and this book examines ways of side-stepping or solving this complex issue.
Download or read book Flood Forecasting at Kinabatangan River Sabah by Utilizing Artificial Neural Network Ann written by Wan Nurulhafizah Bt Abd Razak and published by . This book was released on 2015 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: Flood event is among the most influential disaster in Malaysia .Therefore, the developing of flood forecasting model is to minimize the effects of flood and to achieve a model with high accuracy by utilizing Artificial Neural Network (ANN). Artificial Neural Network is a highly non-linear and can capture the complex interactions among input variables in a system without any prior knowledge about the nature of these interactions. Nowadays, ANN is widely used in prediction and forecasting in water resources. The area of study the flood forecasting is carried out at the Balat Station, Kinabatangan River, Sabah where the hourly water level data is collected from Department of Irrigation and Drainage (DID) from year 2000 until 2014. The results indicated that the model develop a highest accuracy is 6 hour time interval for 4000 iteration where the NSC result is 0.996 with lower RMSE 155.341 compared to others iteration and time interval. This modal achieved 100% at allowable error less than 500 mm which is show the prediction of water level. As a conclusion, this model shows high accuracy and water level can be used alone. This can be applied in the real world to give out warning on imminent flood.
Download or read book Artificial Neural Networks ANNs and GIS for Flood Forecasting written by Sulafa Hag Elsafi and published by . This book was released on 2015-03-19 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Study on Flood Forecasting at Sungai Lembing Using Artificial Neural Network written by Siti Noor Alina Mohd Zulkaply and published by . This book was released on 2014 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Improving Flood Management Prediction and Monitoring written by Zulkifli Yusop and published by Emerald Group Publishing. This book was released on 2018-11-21 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents chapters highlighting the methodologies and tools developed to improve flood management and flood risk reduction.
Download or read book Flood Area Size Prediction Using Artificial Neural Network written by and published by . This book was released on 2013 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Flood Prediction Modelling Using Artificial Neural Networks and GIS written by Purnama Budi Santosa and published by . This book was released on 2004 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Development of Forecasting in Sungai Muda Kuala Muda Kedah by Utilizing Artificial Neural Network ANN written by Nurul Murshida Mohd Sabri and published by . This book was released on 2015 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report deals with flood problem which is usually happened in Malaysia when it coincides with monsoon and gave harm and damages to human life, as it had took many lives each time it happens. A case study of flood is going to be conduct to analyze the pattern of water level and to determine other causes that contributes to the flood. The main aim of the study is to minimize the effect of flood problems. It is also used to develop high accuracy model utilizing Artificial Neural Network (ANN) in predicting flood. Furthermore, it used to forecast flood occasion in the study area of station number of 5606410 of Sungai Muda (Jambatan Syed Omar) which is the main river that supplies water to Kedah and Penang. Besides, it used to investigate whether water level data alone can be used to produce modelling and to determine whether ANN is functioning in the forecasting. In this case study, a computational model will be used to stimulate the input data and generate the result, which is called Artificial Neural Network. ANN, which are modelled on the operating behaviour of the brain, are tolerant of some imprecision and are especially useful for classification and function approximation or mapping problems, to which hard and fast rules cannot be applied easily. The terminology of artificial neural networks has created form an organic biological model of neural system, which it comprises an asset of joined cells, the neurons. The neurons receive impulses or response from either input cells or any other neurons. It will perform some kind of transformation of the input and then, it will transfer the outcome to other neurons or also known as output cells. The neural networks are developed from many layers of connected neurons. The result showed that input 7+1 had the highest NSC value of 0.979 with RMSE value of 288.332 for 6 hour interval time, while input 6+1 had the highest NSC value of 0.977 with RMSE value of 134.801 for 3 hour interval time. In conclusion, this research contributes toward the development of forecasting using Artificial Neural Network for flood problems.
Download or read book Artificial Neural Network Based Flood Forecasting written by Everett Joshua Snieder and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Floods are the most frequent and costly natural disaster in Canada. Flow forecasting models can be used to provide an advance warning of flood risk and mitigate flood damage. Data-driven models have proven to be suitable for flow forecasting applications, yet there are several outstanding challenges associated with model development. Firstly, this research compares four methods for input variable selection for data-driven models, which are used to minimize model complexity and improve performance. Next, methods for reducing the temporal error for data-driven flood forecasting models are investigated. Two procedures are proposed to minimize timing error: error weighting and least-squares boosting. A class of performance measures called visual measures is used to discriminate between timing and amplitude errors, and hence quantifying the impacts of each correction procedure. These studies showcase methods for improving the performance of flow forecasting models, more reliable flood risk predictions, and better preparedness for flood events.
Download or read book Online Flood Forecasting in Fast Responding Catchments on the Basis of a Synthesis of Artificial Neural Networks and Process Models written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed and comprehensive description of the state of the art in the field of flood forecasting opens this work. Advantages and shortcomings of currently available methods are identified and discussed. Amongst others, one important aspect considers the most exigent weak point of today's forecasting systems: The representation of all the fundamentally different event specific patterns of flood formation with one single set of model parameters. The study exemplarily proposes an alternative for overcoming this restriction by taking into account the different process characteristics of flood events via a dynamic parameterisation strategy. Other fundamental shortcomings in current approaches especially restrict the potential for real time flash flood forecasting, namely the considerable computational requirements together with the rather cumbersome operation of reliable physically based hydrologic models. The new PAI-OFF methodology (Process Modelling and Artificial Intelligence for Online Flood Forecasting) considers these problems and offers a way out of the general dilemma. It combines the reliability and predictive power of physically based, hydrologic models with the operational advantages of artificial intelligence. These operational advantages feature extremely low computation times, absolute robustness and straightforward operation. Such qualities easily allow for predicting flash floods in small catchments taking into account precipitation forecasts, whilst extremely basic computational requirements open the way for online Monte Carlo analysis of the forecast uncertainty. The study encompasses a detailed analysis of hydrological modeling and a problem specific artificial intelligence approach in the form of artificial neural networks, which build the PAI-OFF methodology. Herein, the synthesis of process modelling and artificial neural networks is achieved by a special training procedure. It optimizes the network according to the patterns of possible catchment.
Download or read book Bangladesh Flood Forecasting with Neural Networks and Fuzzy neural Model written by Wee Han Lim and published by . This book was released on 2001 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Prediction of Flood Event by Using Artificial Neural Network at Padas River written by Amir Aliff Amri and published by . This book was released on 2015 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: For the past few years, the flood that hits Beaufort District became worst with no early warning given out leaving citizens unprepared. Such warning is needed and can be achieved through modelling. In this study water level of Padas River is being used to produce a model by using Artificial Neural Network to predict the occurrence of flood from the rising of water level in Sungai Padas. Artificial Neural Network with feed forward back propagation architecture are used to produce a model to where future water level are produced by using past water level. The model that has been produced in this thesis proved to be practical in doing real time water level prediction as model 3-4000-6 has given satisfactory accuracy in forecasting. Therefore early warnings can be given to unaware citizens on the rising of water level that will surpass the danger level that will lead to flood.
Download or read book Implementation and Evaluation of Artificial Neural Networks for River Flood Prediction written by Mohammad Taghi Dastorani and published by . This book was released on 2002 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Artifical Neural Network and Support Vector Machine in Flood Forecasting written by Azizah Suliman and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
