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Fitis a book about architecture and society that seeks to fundamentally change how architects and the public think about the task of design. Distinguished architect and urbanist Robert Geddes argues that buildings, landscapes, and cities should be designed to fit: fit the purpose, fit the place, fit future possibilities. Fit replaces old paradigms, such as form follows function, and less is more, by recognizing that the relationship between architecture and society is a true dialogue--dynamic, complex, and, if carried out with knowledge and skill, richly rewarding. With a tip of the hat to John Dewey,Fitexplores architecture as we experience it. Geddes starts with questions: Why do we design where we live and work? Why do we not just live in nature, or in chaos? Why does society care about architecture? Why does it really matter?Fitanswers these questions through a fresh examination of the basic purposes and elements of architecture--beginning in nature, combining function and expression, and leaving a legacy of form. Lively, charming, and gently persuasive, the book shows brilliant examples of fit: from Thomas Jefferson's University of Virginia and Louis Kahn's Exeter Library to contemporary triumphs such as the Apple Store on New York's Fifth Avenue, Chicago's Millennium Park, and Seattle's Pike Place. Fitis a book for everyone, because we all live in constructions--buildings, landscapes, and, increasingly, cities. It provokes architects and planners, humanists and scientists, civic leaders and citizens to reconsider what is at stake in architecture--and why it delights us.

Palmer Drought Severity Index (PDSI) is known as a robust agricultural drought index since it considers the water balance conditions in the soil. It has been widely used as a reference index for monitoring agricultural drought. In this study, the PDSI time series were calculated for nine synoptic stations to monitor agricultural drought in semi-arid region located at Zagros mountains of Iran. Autoregressive Moving Average (ARMA) was used as the stochastic model while Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM) were applied as Machine Learning (ML)-based techniques. According to the time series analysis of PDSI, for the driest months the most PDSI drought events are normal drought and mild drought conditions. As an innovation, Dragonfly Algorithm (DA) was used in this study to optimize the SVM’s parameters, called as the hybrid SVM-DA model. It is worthy to mention that the hybrid SVM-DA is developed as a meta-innovative model for the first time in hydrological studies. The novel hybrid SVM-DA paradigm could improve the SVM’s accuracy up to 29% in predicting PDSI and therefore was found as the superior model. The best statistics for this model were obtained as Root Mean Squared Error (RMSE) = 0.817, Normalized RMSE (NRMSE) = 0.097, Wilmott Index (WI) = 0.940, and R = 0.889. The Mean Absolute Error values of the PDSI predictions via the novel SVM-DA model were under 0.6 for incipient drought, under 0.7 for mild and moderate droughts. In general, the error values in severe and extreme droughts were more than the other classes; however, the hybrid SVM-DA was the best-performing model in most of the cases.