Impatti, adattamento e vulnerabilita Le cause e le responsabilita dei cambiamenti climatici sono state trattate sul numero di ottobre della rivista Cda. Approfondiamo l’argomento presentando il documento: “Cambiamenti climatici 2007: impatti, adattamento e vulnerabilita” votato ad aprile 2007 dal secondo gruppo di lavoro del Comitato Intergovernativo sui Cambiamenti Climatici (Intergovernmental Panel on Climate Change). Si tratta del secondo di tre documenti che compongono il quarto rapporto sui cambiamenti climatici.

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Climate Change 2007: Impacts, Adaptation and Vulnerability.

Climate change 2014 - Synthesis Report

Over the past 15 years, artificial neural networks (ANNs) have been used increasingly for prediction and forecasting in water resources and environmental engineering. However, despite this high level of research activity, methods for developing ANN models are not yet well established. In this paper, the steps in the development of ANN models are outlined and taxonomies of approaches are introduced for each of these steps. In order to obtain a snapshot of current practice, ANN development methods are assessed based on these taxonomies for 210 journal papers that were published from 1999 to 2007 and focus on the prediction of water resource variables in river systems. The results obtained indicate that the vast majority of studies focus on flow prediction, with very few applications to water quality. Methods used for determining model inputs, appropriate data subsets and the best model structure are generally obtained in an ad-hoc fashion and require further attention. Although multilayer perceptrons are still the most popular model architecture, other model architectures are also used extensively. In relation to model calibration, gradient based methods are used almost exclusively. In conclusion, despite a significant amount of research activity on the use of ANNs for prediction and forecasting of water resources variables in river systems, little of this is focused on methodological issues. Consequently, there is still a need for the development of robust ANN model development approaches.

Review: Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions

https://ira.lib.polyu.edu.hk/bitstream/10397/1314/3/JH8.pdf

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

17 18 Lin Qiu 19 Professor, Institute of environmental & municipal engineering, North China Institute of Water 20 Conservancy and Hydroelectric power, ZhengZhou, 450011, P.R.Ch 21 22 Abstract. Developing a hydrological forecasting model based on past records is crucial to 23

A comparison of performance of several artificial intelligence

Multi-time scale stream flow predictions: The support vector machines approach

Herein, a recently developed methodology, Support Vector Machines (SVMs), is presented and applied to the challenge of soil moisture prediction. Support Vector Machines are derived from statistical learning theory and can be used to predict a quantity forward in time based on training that uses past data, hence providing a statistically sound approach to solving inverse problems. The principal strength of SVMs lies in the fact that they employ Structural Risk Minimization (SRM) instead of Empirical Risk Minimization (ERM). The SVMs formulate a quadratic optimization problem that ensures a global optimum, which makes them superior to traditional learning algorithms such as Artificial Neural Networks (ANNs). The resulting model is sparse and not characterized by the “curse of dimensionality.” Soil moisture distribution and variation is helpful in predicting and understanding various hydrologic processes, including weather changes, energy and moisture fluxes, drought, irrigation scheduling, and rainfall/runoff generation. Soil moisture and meteorological data are used to generate SVM predictions for four and seven days ahead. Predictions show good agreement with actual soil moisture measurements. Results from the SVM modeling are compared with predictions obtained from ANN models and show that SVM models performed better for soil moisture forecasting than ANN models.

Soil Moisture Prediction Using Support Vector Machines

Performance evaluation of a water resources system under varying climatic conditions: Reliability, Resilience, Vulnerability and beyond

[1] This study presents a methodology for designing long-term groundwater head monitoring networks in order to reduce spatial redundancy. A spatially redundant well does not change the potentiometric surface estimation error appreciably, if not sampled. This methodology, based on Support Vector Machines, makes use of a uniquely solvable quadratic optimization problem that minimizes the bound on generalized risk, rather than just the mean square error of differences between measured and “predicted” groundwater head values. The nature of the optimization problem results in sparse approximation of the function defining the potentiometric surface that was utilized to select the number and locations of long-term monitoring wells and guide future data collection efforts, which is a prerequisite in building and calibrating regional flow and transport models. The methodology is applied to the design of regional groundwater monitoring networks in the Water Resources Inventory Area (WRIA) 1, Whatcom County, northern Washington State, USA.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2004WR003304

Tirusew Asefa

Papers

Multi-time scale stream flow predictions: The support vector machines approach

Soil Moisture Prediction Using Support Vector Machines

Performance evaluation of a water resources system under varying climatic conditions: Reliability, Resilience, Vulnerability and beyond

Support vectors–based groundwater head observation networks design

Multiobjective analysis of chaotic dynamic systems with sparse learning machines