Modelling groundwater level variations by learning from multiple models using fuzzy logic

Modelling time series of groundwater levels is investigated by three fuzzy logic (FL) models, Sugeno (SFL), Mamdani (MFL) and Larsen (LFL), using data from observation wells. One novelty in the study is the re-use of these three models as multiple models through the following strategies: (a) simple averaging, (b) weighted averaging and (c) committee machine techniques; these are implemented using artificial neural networks (ANN). These strategies provide some evidence that (i) multiple models improve on the performance of individual models and those using committee machines perform better than the other two options; and (ii) committee machine models produce defensible modelling results to develop management scenarios. The study investigates water table declines through management scenarios and shows that in this aquifer water use has higher impacts on water table variations than climatic variations. This provides evidence of the need for planned management in the study area.     

Climatology of dust days in the Central Plateau of Iran

Natural Hazards - Dust storms are a major natural hazard to human health. Severe erosive storms in parts of the Central Plateau of Iran have made the situation very difficult for the inhabitants,...     

Anomaly Detection from Hyperspectral Images Using Clustering Based Feature Reduction

An anomaly detection method with a clustering based feature reduction is proposed in this paper to improve the performance of the Local RX detector. Because of high dimensionality of hyperspectral image and the low number of available samples in each local region around each testing pixel, the estimate of local covariance matrix is not possible. So, because of singularity problem, Local RX cannot use the local covariance matrix and misses the local structures of data to model the background clutter. To deal with this problem, a supervised clustering based feature reduction is introduced for extraction of background features with minimum overlap and redundant information. In the projected feature space with reduced dimensionality, the local structures of background pixels are estimated to efficiently model the background data. The experiments done on both synthetic and real hyperspectral images show the superior detection performance of the proposed method with a relatively high speed.     

The genesis of Mo-Cu deposits and mafic igneous rocks in the Senj area,Alborz magmatic belt,Iran

Mineralogy and Petrology - The geochemical and isotopic investigations were provided on the Upper Eocene Senj mafic intrusion and Mo-Cu mineralization to better understand the tectono-magmatic...     

Development of a new wavelet-based hybrid model to forecast multi-scalar SPEI drought index (case study: Zanjan city,Iran)

Theoretical and Applied Climatology - Drought forecasting plays a vital role in managing drought and reducing its effects on agricultural systems and water resources. In the present study, three...     

Arsenic in the Muteh gold mining district, Isfahan, Iran

Following the appearance of symptoms of arsenic toxicity in the inhabitants of villages in the Muteh gold mining region, central Iran, the concentration of this element in various parts of biogeochemical cycle is investigated. For this purpose, rock, groundwater, soil, plant, livestock hair and wool, and human hair samples are collected and analysed. Total arsenic content ranges from 23 to 2,500?mg/kg in rock samples, 7?C1,061???g/l in water, 12?C232?mg/kg in soil, 0.5?C16?mg/kg in plant samples, 4.10?C5.69?mg/kg in livestock hair and wool, and 0.64?C5.82?mg/kg in human hair. Arsenic concentration in various parts of biogeochemical cycle near the gold deposit in a metamorphic complex, and also close to the gold-processing plant, is very high and decreases exponentially with increasing distance from them. Arsenic concentration in water from a well close to the Muteh gold mine is above 1?mg/L. Arsenic in hair samples taken from local inhabitants is above the recommended levels, and the control samples in Shahre-Kord city. Arsenic concentration is higher in male population and correlates positively with age. It is suggested that arsenic resulting from the decomposition of ore mineral such as orpiment (As₂S₃), realgar (As₂S₂) and arsenopyrite (FeAsS) is responsible for polluting natural resources and the human intake via drinking water and the food chain. Gold mining and processing has undoubtedly enhanced the release of arsenic and intensified the observed adverse effects in Muteh area.     

Nematode community structure in relation to metals in the southern of Caspian Sea

Spatial distribution and structure of nematode assemblages in coastal sediments of the southern part of the Caspian Sea were studied in relation to environmental factors. By considering metals, organic matter, Shannon diversity index(H), maturity index(MI) and trophic diversity(ITD), ecological quality status of sediment was also determined. Fifteen nematode species belonging to eleven genera were identified at the sampling sites. Average density of nematode inhabiting in sediment of the studied area was 139.78±98.91(ind. per 15.20 cm~2). According to redundancy analysis(RDA), there was high correlation between metals and some species. Based on biological indicators, the studied area had different environmental quality. Generally, chemical and biological indices showed different results while biological indices displayed similar results in more sites.     

Modeling flood event characteristics using D-vine structures

The authors investigate the use of drawable (D-)vine structures to model the dependences existing among the main characteristics of a flood event, i.e., flood volume, flood peak, duration, and peak time. Firstly, different three- and four-dimensional probability distributions were built considering all the permutations of the conditioning variables. The Frank copula was used to model the dependence of each pair of variables. Then, the appropriate D-vine structures were selected using information criteria and a goodness-of-fit test. The influence of varying the data length on the selected D-vine structure was also investigated. Finally, flood event characteristics were simulated using the four-dimensional D-vine structure.     

Probabilistic Convective Initiation Nowcasting with Reduced Satellite - NWP Predictors over Iran

Geostationary satellites are able to nowcast Convective Initiation (CI) for the next 0–6 h. Compared to using satellite predictors only, the incorporation of satellite and Numerical Weather Prediction (NWP) predictors can provide the possibility to reduce false alarm rates in 0–1:30 Convective Initiation Nowcasting (COIN). However, the correlation among these predictors not only can cause error in COIN, but also increases the runtime. In this study for the first time, all effective predictors in Satellite Convection Analysis and Tracking version 2 (SATCASTv2) and NWP were applied over Iran from 22nd March 2015 to 9th January 2016. In applying SATCASTv2 over Iran, it was necessary to make some modifications to the algorithm, such as removing case specific thresholds of satellite predictors and rearranging COIN predictors. Then, SATCASTv2 was tested and evaluated with both the full and reduced set of predictors. The results suggested that using fixed thresholds for temporal difference predictors could miss COIN in some cases. To investigate the possibility of improving computational efficiency, a dimension reduction was conducted by Factor Analysis (FA) and the number of predictors was reduced from 22 to 11. The NWP-satellite, reduced NWP-satellite, and satellite predictors were used as input in Random Forest (RF), as a parametric machine learning method, for COIN evaluation. The Combination of NWP model and satellite predictors had lower false alarm rates in contrast with satellite predictors. This is in agreement with previous studies. The results from statistical metrics showed that the reduced NWP-satellite predictors had comparable performance to the NWP-satellite predictors over study area, but decreased the run time by almost 50%. The results indicated that Convective Inhibition (CIN) was the most significant predictor when the reduced set of predictors was used.