Estimating monthly total nitrogen concentration in streams by using artificial neural network

Artificial Neural Network (ANN) is a flexible and popular tool for predicting the non-linear behavior in the environmental system. Here, the feed-forward ANN model was used to investigate the relationship among the land use, fertilizer, and hydrometerological conditions in 59 river basins over Japan and then applied to estimate the monthly river total nitrogen concentration (TNC). It was shown by the sensitivity analysis, that precipitation, temperature, river discharge, forest area and urban area have high relationships with TNC. The ANN structure having eight inputs and one hidden layer with seven nodes gives the best estimate of TNC. The 1:1 scatter plots of predicted versus measured TNC were closely aligned and provided coefficients of errors of 0.98 and 0.93 for ANNs calibration and validation, respectively. From the results obtained, the ANN model gave satisfactory predictions of stream TNC and appears to be a useful tool for prediction of TNC in Japanese streams. It indicates that the ANN model was able to provide accurate estimates of nitrogen concentration in streams. Its application to such environmental data will encourage further studies on prediction of stream TNC in ungauged rivers and provide a useful tool for water resource and environment managers to obtain a quick preliminary assessment of TNC variations.     

Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique

Artificial neural networks (ANNs) are being used increasingly to predict and forecast water resources' variables. The feed-forward neural network modeling technique is the most widely used ANN type in water resources applications. The main purpose of the study is to investigate the abilities of an artificial neural networks' (ANNs) model to improve the accuracy of the biological oxygen demand (BOD) estimation. Many of the water quality variables (chemical oxygen demand, temperature, dissolved oxygen, water flow, chlorophyll a and nutrients, ammonia, nitrite, nitrate) that affect biological oxygen demand concentrations were collected at 11 sampling sites in the Melen River Basin during 2001-2002. To develop an ANN model for estimating BOD, the available data set was partitioned into a training set and a test set according to station. In order to reach an optimum amount of hidden layer nodes, nodes 2, 3, 5, 10 were tested. Within this range, the ANN architecture having 8 inputs and 1 hidden layer with 3 nodes gives the best choice. Comparison of results reveals that the ANN model gives reasonable estimates for the BOD prediction.     

人工神经网络法预测城市用水量

城市用水量的预测结果,对于城市规划、供水系统的管理及改扩建有着重要的意义,寻求科学合理的预测模型是保障预测结果准确可靠的关键。针对这一问题,利用人工神经网络理论建立了BP（Back—Propagation,反向传播算法）网络预测模型,该模型考虑了反映社会、经济的两个影响因素人口和工业产值对用水量需求的影响,具备系统决策功能。通过实例证明该模型是一种行之有效的用水量预测模型。     

Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance

A reliable model for any wastewater treatment plant is essential in order to provide a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. This process is complex and attains a high degree of nonlinearity due to the presence of bio-organic constituents that are difficult to model using mechanistic approaches. Predicting the plant operational parameters using conventional experimental techniques is also a time consuming step and is an obstacle in the way of efficient control of such processes. In this work, an artificial neural network (ANN) black-box modeling approach was used to acquire the knowledge base of a real wastewater plant and then used as a process model. The study signifies that the ANNs are capable of capturing the plant operation characteristics with a good degree of accuracy. A computer model is developed that incorporates the trained ANN plant model. The developed program is implemented and validated using plant-scale data obtained from a local wastewater treatment plant, namely the Doha West wastewater treatment plant (WWTP). It is used as a valuable performance assessment tool for plant operators and decision makers. The ANN model provided accurate predictions of the effluent stream, in terms of biological oxygen demand (BOD), chemical oxygen demand (COD) and total suspended solids (TSS) when using COD as an input in the crude supply stream. It can be said that the ANN predictions based on three crude supply inputs together, namely BOD, COD and TSS, resulted in better ANN predictions when using only one crude supply input. Graphical user interface representation of the ANN for the Doha West WWTP data is performed and presented.     

A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China

Wind energy, one of the most promising renewable and clean energy sources, is becoming increasingly significant for sustainable energy development and environmental protection. Given the relationship between wind power and wind speed, precise prediction of wind speed for wind energy estimation and wind power generation is important. For proper and efficient evaluation of wind speed, a smooth transition periodic autoregressive (STPAR) model is developed to predict the six-hourly wind speeds. In addition, the Elman artificial neural network (EANN)-based error correction technique has also been integrated into the new STPAR model to improve model performance. To verify the developed approach, the six-hourly wind speed series during the period of 2000–2009 in the Hebei region of China is used for model construction and model testing. The proposed EANN-STPAR hybrid model has demonstrated its powerful forecasting capacity for wind speed series with complicated characteristics of linearity, seasonality and nonlinearity, which indicates that the proposed hybrid model is notably efficient and practical for wind speed forecasting, especially for the Hebei wind farms of China.     

Assessing the potential of native tree species for carbon sequestration forestry in Northeast China

Although the native forests of China are exceptionally diverse, only a small number of tree species have been widely utilized in forest plantations and reforestation efforts. We used dendrochronological sampling methods to assess the potential growth and carbon sequestration of native tree species in Jilin Province, Northeast China. Trees were sampled in and near the Changbaishan Biosphere Reserve, with samples encompassing old-growth, disturbed forest, and plantations. To approximate conditions for planted trees, sampling focused on trees with exposed crowns (dominant and co-dominant individuals). A log-linear relationship was found between diameter increment and tree diameter, with a linear decrease in increment with increasing local basal area; no significant differences in these patterns between plantations and natural stands were detected for two commonly planted species (Pinus koraiensis and Larix olgensis). A growth model that incorporates observed feedbacks with individual tree size and local basal area (in conjunction with allometric models for tree biomass), was used to project stand-level biomass increment. Predicted growth trajectories were then linked to the carbon process model InTEC to provide estimates of carbon sequestration potential. Results indicate substantial differences among species, and suggest that certain native hardwoods (in particular Fraxinus mandshurica and Phellodendron amurense), have high potential for use in carbon forestry applications. Increased use of native hardwoods in carbon forestry in China is likely to have additional benefits in terms of economic diversification and enhanced provision of "ecosystem services", including biodiversity protection.     

Multi-purpose reverse logistics network design for medical waste management in a megacity: Istanbul,Turkey

Environment Systems and Decisions - In the study, a multi-purpose reverse logistics network has been designed to create effectual management of medical waste (MW) generated in 39 districts of...     

A new method for predicting vegetation distributions using decision tree analysis in a geographic information system

Decision tree analysis was used to predict the distribution of forest communities in an area on the south coast of New South Wales, Australia. The analysis was carried out using a geographical information system environmental data base of those topographic and geological variables thought to influence the distribution of vegetation and derived from cartographic sources. The resulting maps of forest communities are of a resolution sufficient to delimit individual forest stands and contain much ecological information.     

Anticipated Performance Index of some tree species considered for green belt development in and around an urban area: a case study of Varanasi city, India

It is well established that trees help to reduce air pollution, and there is a growing impetus for green belt expansion in urban areas. Identification of suitable plant species for green belts is very important. In the present study, the Air Pollution Tolerance Index (APTI) of many plant species has been evaluated by analyzing important biochemical parameters. The Anticipated Performance Index (API) of these plant species was also calculated by considering their APTI values together with other socio-economic and biological parameters. Based on these two indices, the most suitable plant species for green belt development in urban areas were identified and recommended for long-term air pollution management.