River flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques

In this research an attempt is made to develop highly accurate river flow forecasting models. Wavelet multi-resolution analysis is applied in conjunction with artificial neural networks and adaptive neuro-fuzzy inference system. Various types and structure of computational intelligence models are developed and applied on four different rivers in Australia. Research outcomes indicate that forecasting reliability is significantly improved by applying proposed hybrid models, especially for longer lead time and peak values

Post-collisional polycyclic plutonism from the Zagros hinterland: the Shaivar Dagh plutonic complex, Alborz belt, Iran

The petrological and geochronological study of the Cenozoic Shaivar Dagh composite intrusion in the Alborz Mountain belt (NW Iran) reveals important clues to decipher complex relations between magmatic and tectonic processes in the central sectors of the Tethyan (Alpine-Himalayan) orogenic belt. This pluton is formed by intrusion at different times of two main magmatic cycles. The older (Cycle 1) is formed by calc-alkaline silicic rocks, which range in composition from diorites to granodiorites and biotite granites, with abundant mafic microgranular enclaves. The younger cycle (Cycle 2) is formed by K-rich monzodiorite and monzonite of marked shoshonitic affinity. The latter form the larger volumes of the exposed plutonic rocks in the studied complex. Zircon geochronology (laser ablation ICP-MS analyses) gives a concordia age of 30.8 ± 2.1 Ma for the calc-alkaline rocks (Cycle 1) and a range from 23.3 ± 0.5 to 25.1 ± 0.9 Ma for the shoshonitic association (Cycle 2). Major and trace element relations strongly support distinct origins for each magmatic cycle. Rocks of Cycle 1 have all the characteristic features of active continental margins. Shoshonitic rocks (Cycle 2) define two continuous fractionation trends: one departing from a K-rich basaltic composition and the other from an intermediate, K-rich composition. A metasomatized-mantle origin for the two shoshonitic series of Cycle 2 is proposed on the basis of comparisons with experimental data. The origin of the calc-alkaline series is more controversial but it can be attributed to processes in the suprasubduction mantle wedge related to the incorporation of subducted mélanges in the form of silicic cold plumes. A time sequence can be established for the processes responsible of the generation of the two magmatic cycles: first a calc-alkaline cycle typical of active continental margins, and second a K-rich cycle formed by monzonites and monzodiorites. This sequence precludes the younger potassic magmas as precursors of the older calc-alkaline series. By contrast, the older calc-alkaline magmas may represent the metasomatic agents that modified the mantle wedge during the last stages of subduction and cooked a fertile mantle region for late potassic magmatism after continental collisio

An integrated simulation–optimization framework for assessing environmental flows in rivers

The present study proposes an integrated simulation–optimization framework to assess environmental flow by mitigating environmental impacts on the surface and ground water resources. The model satisfies water demand using surface water resources (rivers) and ground water resources (wells). The outputs of the ecological simulation blocks of river ecosystem and the ground water level simulation were utilized in a multiobjective optimization model in which six objectives were considered in the optimization model including (1) minimizing losses of water supply (2) minimizing physical fish habitat losses simulated by fuzzy approach (3) minimizing spawning habitat losses (4) minimizing ground water level deterioration simulated by adaptive neuro fuzzy inference system(ANFIS) (5) maximizing macroinvertebrates population simulated by ANFIS (6) minimizing physical macrophytes habitat losses. Based on the results in the case study, ANFIS-based model is robust for simulating key factors such as water quality and macroinvertebrate’s population. The results demonstrate the reliability and robustness of the proposed method to balance environmental requirements and water supply. The optimization model increased the percentage of environmental flow in the drought years considerably. It supplies 69% of water demand in normal years, while the environmental impacts on the river ecosystem are minimized. The proposed model balances the portion of using surface water and ground water in water supply considering environmental impacts on both sources. Using the proposed method is recommendable for optimal environmental management of surface water and ground water in river basin scale

Geochemistry, Sr-Nd-Pb isotopes and geochronology of amphibole- and mica-bearing lamprophyres in northwestern Iran: Implications for mantle wedge heterogeneity in a paleo-subduction zone

Highlights: • Northwestern Iranian lamprophyres have alkaline and calc-alkaline nature. • Studied lamprophyres are emplaced during Late Cretaceous to Late Miocene time. • Lamprophyres originated from different metasomatised lithospheric mantle. Abstract: Lamprophyres of different age showing distinctive mineralogy, geochemistry and isotopic ratios are exposed in northwestern Iran. They can be divided into Late Cretaceous sannaite, Late Oligocene-Early Miocene camptonite (amphibole-bearing) and Late Miocene minette (mica-bearing) and spessartite (amphibole-bearing) lamprophyres. Sannaites have high-Ti amphibole along with high-Ti and Al clinopyroxene, and they are characterised by homogeneous enrichment in incompatible trace elements with troughs at Pb. Spessartites have hornblende and low-Al and Ti clinopyroxene, and they are characterised by enriched incompatible trace element pattern with depletions of Nb, Ta, Pb, and Ti with respect to large ion lithophile elements. Minettes have high-Ti and Al brown mica and low-Al and Ti clinopyroxene, and similarly to spessartite, are characterised by fractionation of high field strength elements with respect to large ion lithophile elements, with troughs at Nb, Ta, and Ti and a peak at Pb. Minettes show high initial 87Sr/86Sr values up to 0.70760 and low initial 143Nd/144Nd down to 0.512463 with a negative correlation, consistent with the trace element distribution related with an enriched mantle source modified after sediment recycling during subduction and continental collision. Cretaceous sannaites and Early Miocene spessartites show low initial 87Sr/86Sr approaching 0.70447 and high 143Nd/144Nd values up to 0.512667, which are consistent with a depleted within-plate mantle source. Minette and spessartite lamprophyres show high initial 206Pb/204Pb, 207Pb/204Pb, and 208Pb/204Pb values, whereas sannaites have lower, but variable, initial 206Pb/204Pb, 207Pb/204Pb, and 208Pb/204Pb values with respect to those of calc-alkaline lamprophyres. Minettes originated by partial melting of a metasomatised lithospheric mantle following siliciclastic sediment recycling by subduction. In contrast, sannaites were generated from the partial melting of a similar lithospheric mantle that was metasomatised by within-plate agents

A mobile and intelligent device for customized logopedic therapy

An expert system for customized logopedic therapy must allow the training of children depending on its speech disabilities and former progress. The children’s training is accomplished using exercises chosen by the expert system and can be performed either in the doctor’s office or at home for each child using an intelligent mobile device. The expert system generates a set of exercises for each child depending on the doctor’s recommendation. These exercises are transferred from PC to mobile devices using a Universal Serial Bus connection. The mobile device saves the result of each therapy session and when it is connected to PC it transfers the results to the expert system for analysis. Using the results of these analyses the expert system will decided whether a new session is needed and if that is the case, compute a new set of exercises

Improving fuzzy-based model for seasonal river flow forecasting

Accurate river flow forecasts play a key role in sustainable water resources and environmental management. Recently, computational intelligence approaches have become increasingly popular due to minimum information requirements and their ability to simulate nonlinear and non-stationary characteristics of hydrological process. In this paper, the performance of seasonal river flow forecasting model is improved when different input combinations and data-preprocessing techniques are applied on adaptive neuro-fuzzy inference system (ANFIS). Wavelet multi-resolution analysis is coupled with ANFIS model to develop hybrid wavelet neuro-fuzzy model (WNF). Different models with different input selection and structure are developed for daily river flow forecasting in Railway Parade station on Ellen Brook River, Western Australia. The River flow time series is decomposed into multi-frequency time series by discrete wavelet transform (DWT) using the Haar, Coiflet number 1 and Daubechies number 5 mother wavelets, then the wavelet coefficients are imposed as input data to the neuro-fuzzy model. Models are developed based on Takagi-Sugeno-Kang fuzzy model with the grid partitioning approach for initializing the fuzzy rule-based structure. Mean square error and coefficients of determination are chosen as the performance criteria. Results show that the right selection of the inputs with high autocorrelation function (ACF) improves the accuracy of forecasting. However, comparing the performance of the hybrid WNF model with those of the original ANFIS models, indicates that the hybrid wavelet neuro-fuzzy models produce significantly better results

River flow forecasting using an integrated approach of wavelet analysis and artificial neural networks

The need for accurate river flow forecasting model has grown rapidly in the past decades for achieving better risk-based water resources planning due to issues like water demand increase or climate change. In this paper a hybrid Wavelet-Neural Networks (WNN) is developed to predict daily river flow. WNN is one of the most reliable recent methods for hydrological time series predictions. 30 years of daily stream flow and rainfall data from Dingo road station on Harvey River, Western Australia are used in this study. Both rainfall and runoff time series are decomposed into multi-frequency time series by using the Harr and Daubechies wavelet No5 (db5), then the wavelet coefficients are imposed as input data to feed-forward back propagation ANN. The best structure of ANN is chosen by trial and error to reach best daily stream flow forecasting. Comparing the results with those of the single ANN model indicates that the performances of WNN are more effective than ANN in terms of selected performance criteria

Combined Wavelet-Neural Network Model for Intermittent Stream Flow Prediction

Achieving accurate intermittent river flow forecasting, plays a key role in water resources and environmental management. Water demands are increasing while surface water availability is likely to decrease in Western Australia. Understandably, reliable information on current and future water availability is essential for properly manage and share the limited water resources. Forecasting intermittent stream flow is quite limited due to the complexity of fitting models to their time series as they do not have flow for some intervals. In this paper Wavelet-Neural Networks (WNN) technique is studied to reach accurate and reliable daily river flow prediction. WNN is based on combination of wavelet analysis and Artificial Neural Network (ANN), which is one of the most reliable recent hybrid methods for non-stationary hydrological time series predictions. Daily stream flow and precipitation historical data from Northam weir station on Avon River, Western Australia are used in this study. The observed stream flow and rainfall time series are both decomposed by Daubechies4 and Coiflet1 Wavelet transforms. Then the sub-series are added up to develop new time series for imposing as input data to the multilayer perceptron neural networks (MLP). Comparing the results of different wavelet neural networks with those of the single ANNs model indicates that preprocessing data with discrete wavelet transform have significantly improved artificial neural in terms of selected performance criteria