Climate Change: Implication on Aquatic Resources, Food Security and Livelihoods

Climate changes are growing environmental concerns which are much in the scientific government and public eye at present. The potential impact on aquatic resources and livelihood are immense. From local to global levels, fisheries and aquaculture play important roles for food supply, food security and income generation. Some 43.5 million people work directly in the sector, with the great majority in developing countries. Adding those who work in associated processing, marketing, distribution and supply industries, and the sector supports nearly 200 million livelihoods. Aquatic foods have high nutritional quality, contributing 20 percent or more of average per capita animal protein intake for more than 1.5 billion people, mostly from developing countries. They are also the most widely traded foodstuffs and are essential components of export earnings for many poorer countries. Extreme events will also impact on infrastructure, ranging from landing and farming sites to post-harvest facilities and transport routes. They will also affect safety at sea and settlements, with communities living in low-lying areas at particular risk. Livelihood diversification is an established means of risk transfer and reduction in the face of shocks, but reduced options for diversification will negatively affect livelihood outcomes

Evaluating avms performance. beyond the accuracy

Automated Valuation Models (AVMs) are regularly used in mass appraisal techniques. Thanks to developments in artificial intelligence, machine learning algorithms are increasingly being used alongside traditional econometric models. The final phase in the definition of the models consists in the verification phase of the results elaborated by Avm. The predictive effectiveness tests evaluate the models trained on part of the dataset (the training set) and then measure their ability to predict the remaining values of the dataset (testing set). This verification methodology provides as final output the accuracy parameter, i.e. the difference between predicted prices and actual prices. According to many authors this parameter, if considered alone, is insufficient. The research consists in an accuracy test of 5 Avm in the ability to predict the values of 1038 properties in the city of Padua. To the accuracy results of the test are added the results of cross-validation and the use of different statistical indicators for the measurement of predictive effectiveness. The results provide useful information that broadens the framework of model knowledge. They can be used in the analysis and description of automated evaluation models

The Cross Validation in Automated Valuation Models: A Proposal for Use

The appraisal of large amounts of properties is often entrusted to Automated Valuation Models (AVM). At one time, only econometric models were used for this purpose. More recently, also machine learning models are used in mass appraisal techniques. The literature has devoted much attention to assessing the performance capabilities of these models. Verification tests first train a model on a training set, then measure the prediction error of the model on a set of data not met before: the testing set. The prediction error is measured with an accuracy indicator. However, verification on the testing set alone may be insufficient to describe the model\u2019s performance. In addition, it may not detect the existence of model bias such as overfitting. This research proposes the use of cross validation to provide a more complete and effective evaluation of models. Ten-fold cross validation is used within 5 models (linear regression, regression tree, random forest, nearest neighbors, multilayer perception) in the assessment of 1,400 properties in the city of Turin. The results obtained during validation provide additional information for the evaluation of the models. This information cannot be provided by the accuracy measurement when considered alone