Mohsen Nasseri

... ﻩﮊﺍﻭ ﺎﻫ ﻱ ﻠﻛ ﻴ ﺪ ﻱ: ﺶﻴﭘ ،ﺵﺭﺎﺑ ﻩﺪﺷ ﺩﺭﺍﺪﻧﺎﺘﺳﺍ ﻪﻳﺎﻤﻧ ،ﻲﻟﺎﺴﮑﺸﺧ ﻲﻨﻴﺑ ﻲﺳﺎﻨﺷﺍﻮﻫ ﻲﻟﺎﺴﮑﺸﺧ ،ﻱﺯﺎﻓ ﺝﺎﺘﻨﺘﺳﺍ ﻢﺘﺴﻴﺳ Mid-term Prediction of Meteorological Drought Using Fuzzy Inference Systems Roohollah Ebrahimi 1 Banafsheh Zahraie 2 Mohsen Nasseri3 (Received May 4, 2009 Accepted Aug. 29, 2010) ...

In any geostatistical study, an important consideration is the choice of an appropriate, repeatable, and objective search strategy that controls the nearby samples to be included in the location-specific estimation procedure. Almost all geostatistical software available in the market puts the onus on the user to supply search strategy parameters in a heuristic manner. These parameters are solely controlled by geographical coordinates that are defined for the entire area under study, and the user has no guidance as to how to choose these parameters. The main thesis of the current study is that the selection of search strategy parameters has to be driven by data—both the spatial coordinates and the sample values—and cannot be chosen beforehand. For this purpose, a genetic-algorithm-based ordinary kriging with moving neighborhood technique is proposed. The search capability of a genetic algorithm is exploited to search the feature space for appropriate, either local or global, search strategy parameters. Radius of circle/sphere and/or radii of standard or rotated ellipse/ellipsoid are considered as the decision variables to be optimized by GA. The superiority of GA-based ordinary kriging is demonstrated through application to the Wolfcamp Aquifer piezometric head data. Assessment of numerical results showed that definition of search strategy parameters based on both geographical coordinates and sample values improves cross-validation statistics when compared with that based on geographical coordinates alone. In the case of a variable search neighborhood for each estimation point, optimization of local search strategy parameters for an elliptical support domain—the orientation of which is dictated by anisotropic axes—via GA was able to capture the dynamics of piezometric head in west Texas/New Mexico in an efficient way.► The paper addresses the selection of search strategy parameters in ground water hydrology. ► These parameters cannot be chosen beforehand and has to be driven by data. ► The dimensions of the search neighborhood are taken as design variables. ► A GA-based kriging constitutes the objective function to be minimized. ► Performance improved by taking geographical coordinates and sample values into account.

In this paper, a hybrid model which combines Extended Kalman Filter (EKF) and Genetic Programming (GP) for forecasting of water demand in Tehran is developed. The initial goal of the current work is forecasting monthly water demand using GP for achieving an explicit optimum formula. In the proposed model, the EKF is applied to infer latent variables in order to make a forecasting based on GP results of water demand. The available dataset includes monthly water consumption of Tehran, the capital of Iran, from 1992 to 2002. Five best formulas based on GP results on this dataset are presented. In these models, the first five to three lags of observed water demand are used as probable and independent inputs. For each model, sensitivity of the results for each input is measured mathematically. A model with the most compatibility of the computed versus the observed water demand is used for filtering based on EKF method. Results of GP and hybrid models of EKFGP demonstrate the visible effect of observation precision on water demand prediction. These results can help decision makers of water resources to reduce their risks of online water demand forecasting and optimal operation of urban water systems.► We implemented GP for developing suitable functional forms for water demand forecasting. ► These formula have been evaluated by mathematical sensitivity analysis and the best one has been chosen. ► Then, EKF as a nonlinear data assimilator has been used for increasing accuracy of the best model result.

Most applications of geostatistical estimation and simulation techniques are concerned with casting the kriging system for the entire data set. Although there have been many successful applications of geostatistical analysis to capture the nonlinear relationships inherent in hydrological processes, there are numerous instances where more data does not necessarily imply better performance. In this research, it is hypothesized that when the data are clustered in some way and/or are anisotropic in nature, casting the kriging system based on the entire data set does not necessarily result in better performance statistics. For this purpose, a set of numerical experiments were conducted whereby the accelerated exact k-mean method was used to cluster data into similar patterns using both spatial coordinates and associated attribute values. For the data set under consideration, it was shown that classifying data into six clusters minimizes the mean squared distance from each data point to its nearest cluster center. Then, the ordinary kriging system with moving neighborhood was developed by limiting data to each cluster when trying to conduct a cross-validation procedure. Assessment of numerical results showed that the cluster-based ordinary kriging technique was more effective compared to its counterparts (i.e., ordinary kriging with one cluster) in capturing the dynamics of piezometric head in West Texas/New Mexico. The current study highlights the importance of data clustering on the performance of ordinary kriging estimator and initiates the need for further research to identify patterns and clusters in hydrologic data in similar studies.

Rainfall forecasting plays many important role in water resources studies such as river training works and design of flood warning systems. Recent advancement in artificial intelligence and in particular techniques aimed at converting input to output for highly nonlinear, non-convex and dimensionalized processes such as rainfall field, provide an alternative approach for developing rainfall forecasting model. Artificial neural networks (ANNs), which perform a nonlinear mapping between inputs and outputs, are such a technique. Current literatures on artificial neural networks show that the selection of network architecture and its efficient training procedure are major obstacles for their daily usage. In this paper, feed-forward type networks will be developed to simulate the rainfall field and a so-called back propagation (BP) algorithm coupled with genetic algorithm (GA) will be used to train and optimize the networks. The technique will be implemented to forecast rainfall for a number of times using rainfall hyetograph of recording rain gauges in the Upper Parramatta catchment in the western suburbs of Sydney, Australia. Results of the study showed the structuring of ANN network with the input parameter selection, when coupled with GA, performed better compared to similar work of using ANN alone.