Transition index maps for urban growth simulation: application of artificial neural networks,weight of evidence and fuzzy multi-criteria evaluation

Transition index maps (TIMs) are key products in urban growth simulation models. However, their operationalization is still conflicting. Our aim was to compare the prediction accuracy of three TIM-based spatially explicit land cover change (LCC) models in the mega city of Mumbai, India. These LCC models include two data-driven approaches, namely artificial neural networks (ANNs) and weight of evidence (WOE), and one knowledge-based approach which integrates an analytical hierarchical process with fuzzy membership functions (FAHP). Using the relative operating characteristics (ROC), the performance of these three LCC models were evaluated. The results showed 85%, 75%, and 73% accuracy for the ANN, FAHP, and WOE. The ANN was clearly superior compared to the other LCC models when simulating urban growth for the year 2010; hence, ANN was used to predict urban growth for 2020 and 2030. Projected urban growth maps were assessed using statistical measures, including figure of merit, average spatial distance deviation, producer accuracy, and overall accuracy. Based on our findings, we recomend ANNs as an and accurate method for simulating future patterns of urban growth.     

Daily Precipitation Extremes in Iran: Decadal Anomalies and Possible Drivers

This study focuses on the empirical statistical analysis of the anomalies in daily precipitation extremes by applying the quantile perturbation method (QPM) to data from 31 Iranian weather stations during the period between 1961 and 2005. The possible causes behind the anomalies in precipitation extremes are identified by analyzing their relationship with the anomalies in eight atmospheric indices (i.e., NAO, SOI, PDO, AMO, NCP, DMI, WeMO, SSN). In terms of decadal oscillations, the country was generally wet in the 1960s and 1970s with most stations exhibiting periods of higher quantile perturbations, whereas lower quantile perturbations were dominant in the 1980s and 1990s. The highest perturbation in extreme precipitation quantiles prevails in Central Iran during the early 1980s, in which the quantiles are about 50% higher than the ones based on the full time series. The frequency of significant precipitation anomalies for winter season was greater than that for spring and autumn seasons. For the summer season, the humid region in North Iran demonstrates strong positive anomalies. The results highlight the noticeable role of large‐scale climatic factors in the anomalous behavior of precipitation extremes in Iran. The atmospheric drivers of the quantile anomalies in extreme precipitation were found to differ from one season to another.     

Mathematical Model for Pollution Source Identification in Rivers

Release of pollution into rivers is required to be handled with special consideration to environmental standards. For this purpose, it is essential to specify the contribution of each pollution source in contamination of water resources. In this study, a mathematical model is proposed for determining locations and concentration release histories of polluting point sources using measured downstream river concentrations via an inverse problem framework. The inverse solution is based on the integral equation obtained from applying the Green's function method on the one-dimensional advection-dispersion contaminant transport equation. Discretization of this integral equation results in a linear, over-determined and ill-posed system of algebraic equations that are solved by using the Tikhonov regularization method. Several examples and some real field data are investigated to illustrate the abilities of the proposed model. Results imply that the proposed method is effective and can identify the pollution sources in rivers with acceptable accuracy.     

Adaptation of Iranian farmers to climate variability and change

Climate change poses serious challenges for populations whose livelihoods depend principally on natural resources. Given the increases in extreme weather events projected to adversely affect the arid and semi-arid regions of Iran, adaptation of the agricultural sector is imperative. Few studies have addressed the farmers’ adaptation in Iran, and little is known about ongoing adaptation strategies in use. Adopting principal component analysis/fuzzy logic-based method, this paper considers the agricultural adaptation to climate variability. A survey of 255 farmers of Fars Province, selected through a multistage stratified random sampling method, revealed different levels of adaptation, specifically the low, moderate and high, which are principally distinguished by various degrees of sensitivity and adaptive capacity. The study also identified the main adaptation strategies used by farmers in response to climate-related shocks. Results indicated that although a large percentage of farmers make some adjustments to their farming practices, there are significant differences in choice of adaptation strategies by the adaptation categories. Some conclusions and recommendations are offered to increase the adaptive capacity of farmers and reduce negative impacts of climate variability and change.     

The effect of neglecting spatial variations of the parameters in pollutant transport modeling in rivers

Environmental Fluid Mechanics - For many environmental projects and plans, it is necessary to model pollutant transport in rivers. Pollutant transport modeling is a complex phenomenon with multiple...     

Green synthesis of 1,3-dihydrobenzimidazol-2-ones from aromatic diamines by microwave in a tetrabutylammonium bromide–ethanol molten salt paste

Environmental Chemistry Letters - The replacement of hazardous solvents by greener alternatives is a major goal in green chemistry. Benzimidazol-2-ones are heterocycles exhibiting a wide range of...     

Cellulose Schiff Base as a Bio-based Polymer Ligand: Extraction,Modification and Metal Adsorption Study

Journal of Polymers and the Environment - Cellulose was initially extracted from Safflower (Carthamus tinctorius L.) stem wastes and used for the preparation of a fully bio-based adsorbent polymer...     

Fish and shrimp waste management at household and market in Bushehr,Iran

Journal of Material Cycles and Waste Management - The aim of this study was to investigate the estimation and management of fish and shrimp wastes in Bushehr province. Two-part questionnaire...     

Sustainable rangeland management using a multi-fuzzy model: how to deal with heterogeneous experts' knowledge

While fuzzy specialists commonly use homogeneous experts' knowledge to construct fuzzy models, it is much more difficult to deal with knowledge elicited from a heterogeneous group of experts. This issue is exemplified in the area of sustainable rangeland management (SRM). One way to deal with the diversity of opinions is to develop a fuzzy system for all experts and to combine all these, the so-called primary systems, into one multi-fuzzy model. To derive each of the primary fuzzy systems, several semi-structured interviews were held in three different areas of the Fars province in Southwest Iran using the knowledge of a group of administrative experts. To obtain the final output of the multi-fuzzy model, we applied different 'voting' methods. The first method simply uses the arithmetic average of the primary outputs as the final output of the multi-fuzzy model. This final output represents an estimation of the right rate of stocking (RRS). We also propose other (un)supervised voting methods. Most importantly, by harmonising the primary outputs such that outliers get less emphasis, we introduce an unsupervised voting method for calculating a weighted estimate of the RRS. This harmonising method is expected to provide a new useful tool for policymakers dealing with heterogenity in experts' opinions: it is especially useful where limited field data are available and one is forced to rely on experts' knowledge only. By constructing the three fuzzy models based on the elicitation of heterogeneous experts' knowledge, our study shows the multidimensional vaguenesses that exist in SRM. Finally, by comparing the final RRS with its common values, this study strongly points to the existence of overgrazing in pastures in the three regions of the Fars province in Southwest Iran.