By Tanvir Islam, Prashant K. Srivastava, Manika Gupta, Xuan Zhu, Saumitra Mukherjee
Computational intelligence suggestions have loved growing to be curiosity in fresh many years one of the earth and environmental technology learn groups for his or her robust skill to resolve and comprehend numerous complicated difficulties and enhance novel ways towards a sustainable earth. This ebook compiles a set of contemporary advancements and rigorous functions of computational intelligence in those disciplines. thoughts coated contain man made neural networks, help vector machines, fuzzy common sense, decision-making algorithms, supervised and unsupervised class algorithms, probabilistic computing, hybrid tools and morphic computing. extra themes given therapy during this quantity contain distant sensing, meteorology, atmospheric and oceanic modeling, weather switch, environmental engineering and administration, catastrophic ordinary dangers, air and environmental pollutants and water caliber. by way of linking computational intelligence thoughts with earth and environmental technology orientated difficulties, this ebook promotes synergistic actions between scientists and technicians operating in parts resembling facts mining and desktop studying. We think assorted team of teachers, scientists, environmentalists, meteorologists and computing specialists with a typical curiosity in computational intelligence suggestions in the earth and environmental sciences will locate this booklet to be of serious value.
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Extra resources for Computational Intelligence Techniques in Earth and Environmental Sciences
The present study used the following three methods for testing stationarity of the variables. 1 Augmented Dickey–Fuller Test The augmented Dickey–Fuller (ADF) test (Dickey and Fuller 1979) examines the presence of unit root (non-stationarity) in the autoregressive model. The ADF test here consists of estimating the following regression (Eq. 1): Δyt ¼ β1 þ β2 t þ δytÀ1 þ p X γ j ΔytÀj þ εt , ð2:1Þ j¼1 where yt is any time series variable, ytÀ1 is the one period lag value of yt, Δyt ¼ yt À ytÀ1, and t is the trend variable.
Remote Sens Environ 115:600–614 Gu¨ler C, Kurt MA, Alpaslan M, Akbulut C (2012) Assessment of the impact of anthropogenic activities on the groundwater hydrology and chemistry in Tarsus coastal plain (Mersin, SE Turkey) using fuzzy clustering, multivariate statistics and GIS techniques. J Hydrol 414–415:435–451 Hagan MT, Demuth HB, Beale MH (1996) Neural network design. PWS Publishing, Boston Hasni A, Taibi R, Draoui B, Boulard T (2011) Optimization of greenhouse climate model parameters using particle swarm optimization and genetic algorithms.
Ferdous and Baten (2011) used least square method for analyzing trend of climatic data (temperature, rainfall, relative humidity, and sunshine) of Rajshahi and Rangpur Division to observe the climate variability. Shamsnia et al. (2011) used stochastic methods (autoregressive integrated moving average [ARIMA] model) for modeling of weather parameters, such as precipitation, temperature, and relative humidity. Kleiber et al. (2013) developed a bivariate stochastic model was applied to a daily temperature (minimum and maximum) data set covering the complex terrain of Colorado, USA, for studying climate impact and successfully quarters considerable temporally varying non-stationarity in both the direct-covariance and cross-covariance functions.