1- Introduction

2- Literature review

3- Methodology

4- Results and discussion

5- Conclusion

References

Abstract

Biomass is becoming an increasingly widespread source of energy. Yet land, as one of the most important resources in biomass production, is surprisingly understudied in the literature of biomass supply chain planning. This study proposes a novel framework that combines the literature of bioethanol supply chain design with agricultural land planning to simultaneously address optimal supply chain planning and sustainable land use in a bioethanol supply chain. A bi-objective mixed-integer linear programming (MILP) model is proposed to formulate the optimal design and planning of a bioethanol supply chain network considering competition of food and biomass feedstock over the available croplands. The proposed model is capable of making strategic decisions (i.e. locations and capacities of facilities, sourcing and allocation of biomass feedstocks to biorefineries), along with some tactical decisions (i.e. land planning, inventory and production of both biomass feedstock and bioethanol). The model incorporates the two objectives of minimum cost and maximum suitability of crops with their assigned croplands. A novel integration of the FAO framework, the best-worst multi-criteria decision-making method, PROMETHEE II and GIS is used to determine the suitability of available croplands according to the croplands’ soil and topographical characteristics. The performance of the proposed model is demonstrated through a multi-feedstock bioethanol supply chain in Fars province, Iran. It is concluded that the proposed integrated land planning-network design framework outperforms hierarchical approaches in which network design and land planning problems are solved separately in a sequential manner. Also, the case study shows that conditional on implementing second generation bioethanol production, Fars province has the potential to satisfy three percent of the fuel demand for transportation in the country.

Introduction

With the rapidly increasing energy demand and the destructive environmental implications of fossil fuel consumption, renewable energies (REs) play an important role in protecting the environment for the future. Among all the renewable energy resources, biofuel is very attractive due to its worldwide availability, storage potential, and the efficiency of its conversion technologies [1]. In the recent years, many studies have been conducted on its future contribution to the global energy demand [2,3]. Iran holds the largest natural gas and the fourth largest oil reserves in the world and is within the ten countries with the highest emissions of CO2 [4]. The country’s main primary energy resources include oil (fuel and crude), natural gas, electric power (generated mainly by natural gas, followed by fuel oil, gas/diesel, wind and Hydro), gas/diesel and motor gasoline [4,5]. As reported by Aslani et al. [6], transportation sector is the second most energy-intensive sector, following residential sector, in the country and is currently utilizing gasoline, gas/diesel, natural gas and fuel oil as its main energy resources [4]. Also, as reported by Mousavi et al. [4], transportation has produced the second largest amounts of carbon dioxide, again following residential, on average between 2003 and 2014. Biofuel is one of the few renewable energy resources which can contribute to satisfying the energy demand of the transportation sector [7]. Therefore, biofuel production could potentially play an important role in sustainability of energy consumption and meeting the increasing gasoline demand in the near future in Iran [8,9]. In addition, biofuel production contributes to waste management, specially the extensive agricultural waste produced annually in the country, produces jobs and promotes development specially in rural areas [10]. According to Ghobadian [11], only utilizing the 17.86 million tons of wasted crops in Iran, would be able to produce 4.91 billion liters of bioethanol per year.

1- Introduction

2- Related work

3- Urban big data fusion based on deep learning

4- Difficulties and ideas of urban big data fusion

5- Conclusion

References

Abstract

Urban big data fusion creates huge values for urban computing in solving urban problems. In recent years, various models and algorithms based on deep learning have been proposed to unlock the power of knowledge from urban big data. To clarify the methodologies of urban big data fusion based on deep learning (DL), this paper classifies them into three categories: DL-output-based fusion, DL-input-based fusion and DL-double-stage-based fusion. These methods use deep learning to learn feature representation from multi-source big data. Then each category of fusion methods is introduced and some examples are shown. The difficulties and ideas of dealing with urban big data will also be discussed.

Introduction

Our life and the city we live in affect each other. In the era of big data, it is urgent to effectively use urban big data to solve problems in the city, such as traffic congestion [1,2], noise pollution [3,4], air pollution [5,6], etc., to improve our life experience. Nowadays, many urban computing methods based on deep learning have been put forward to solve urban problems, such as urban traffic flow prediction [7,8], urban crowd flows prediction [9,10], urban air prediction [11,12], urban water quality prediction [13,14], etc. In these urban computing methods, the big data used by the researchers are all from different sources, such as meteorological stations, taxi detectors, online weather web sites, etc. Moreover, urban big data shows different representations, such as text, numbers and symbols. Bello et al. [15] and Zhang et al.[16] summarized five characteristics of big data, that is, large volume, large velocity, large variety, veracity and value, which are called 5V’s features. The 5V’s features of the data indirectly indicate a big explosion in data amount. On the one hand, how to sense, obtain and manage these big data is a challenge; On the other hand, how to analyze and excavate the value of these big data is another significant challenge. Apparently, the urban big data with 5V’s characteristics brings great challenges to urban computing. Fig. 1 depicts the urban big data. Firstly, urban big data comes from many sources. When studying the real-time city-wide traffic volume, the data usually come from taxi sensor, exploratory data, monitoring data and Internet web data. For example, Meng et al.

1- Introduction

2- The audiences of earthquake emergency maps

3- Earthquake emergency maps for different audiences

4- Mapping earthquake emergency maps

5- Case study and discussion

6- Conclusions

References

Abstract

Emergency response and rescue is an important way to mitigate the loss caused by earthquakes. Various maps containing information about earthquakes provide effective guidance and references for individuals undertaking such action. However, the production of earthquake emergency maps aimed at the “right” user in an effective manner is challenging. In this study, we focus on two questions: (1) Who are the users of earthquake emergency maps and what information do these individuals need? (2) How can the maps be produced quickly and efficiently according to the requirements of different users? To answer the first question, we classify the users into four categories under the guidance of audience theory: earthquake emergency decision-makers, auxiliary decision-making technicians, emergency rescue workers, and the public. The map contents are also described according to the requirements of different audiences. We depict the methods used for representation in such earthquake emergency maps for different audiences, which places a foundation for answering the second question. Following that, a template-matching mapping method is proposed for the rapid production of emergency maps, including two phases: before the earthquake templates of emergency maps for different audiences are prepared; after an earthquake, the template is updated with information from seismic models, so that the emergency maps are automatically plotted. Finally, a case study is provided to verify the mapping method. We conclude that the application of audience theory and the related template-matching method for map production not only answers the two questions but also will benefit the emergency mapping of other disasters.

Introduction

Earthquakes are natural disasters that usually lead to numerous deaths and injuries, and extensive economic loss [1]. China lies between the circum-Pacific seismic belt and the Eurasian seismic zone and has suffered from some of the most severe earthquake disasters in the world [2]. For example, the Wenchuan earthquake in 2008 caused 69,227 deaths, 374,643 injuries, 17,923 missing people, and more than 10,000 square kilometers of severe damage [3]. A method for the accurate short- and medium-period prediction of earthquakes has not yet been discovered. Earthquake emergency response and rescue is an important means by which mitigation of such disasters can take place after an earthquake, and the effectiveness of this system has often been verified [4]. After the occurrence of an earthquake, maps that include information pertaining to an earthquake and the associated possible disaster can provide a foundation for effective emergency search and rescue, and therefore play an important role in the emergency response and rescue process. These maps are therefore essential thematic maps that display the various types of information needed for earthquake emergency command and rescue. Earthquake emergency maps are important to the leaders of rescue operations and the staff members of governments. These individuals can intuitively understand the information regarding an earthquake disaster, and carry out rescue operations in a timely manner [5]. The three days following an earthquake are known as the “golden period of emergency rescue”; after this time the number of dead sharply increases [6,7]. In China, the government needs approximately 24 h to collect and send emergency rescue materials to a disaster area, which is usually located in the mountain regions of Southwest China. This means that the governmental lead needs to make rescue decisions within half a day following an earthquake. In such a situation, emergency maps that include useful information are crucial in aiding such decision-makers. However, the means by which useful maps in earthquake emergencies can be produced in an effective manner is challenging. Two questions concerning earthquake emergency mapping are discussed in this paper. Many people are involved in emergency response and rescue.

Despite the popularity of the term urban livability, it is often used by different groups under different circumstances. A broader understanding of urban livability is that it concerns the quality of life in any human living environment. The World Health Organization, among many others, suggests a four-dimension assessment system based on the concepts of convenience, amenity, health and safety that can be used to evaluate any cities’ potential livability. Following this proposal, the current study taps into the power of GIS and Remote Sensing technologies to generate a set of urban livability evaluating indicators via extracted land use information. Using the city proper of Changchun, Jilin Province of China as an example, the study extracts fifteen individual land use indicators from topographic maps and a remote sensing imagery. A principal component analysis-based approach was used to build an urban livability index with the fifteen indicators. Furthermore, with detailed examination of relevant studies, national documents and local fieldwork, this research also establishes potential benchmark values for all fifteen livability evaluating indicators for comparison purposes. Results suggest that slightly more than half of Changchun’s city proper is above the livability benchmark in the framework of the current study. Residents’ access to parks and open spaces is a major lagging factor for the city proper’s livability. The study provides an alternative of quantifiable and verifiable approach for sustainable urban planning, especially from a land use policy perspective.

Introduction

Urbanization in China has experienced rapid development during the past decades. While rapid urbanization brought tremendous changes on urban landscape, scholars also observe the increase of urban pollution, traffic congestion, shrinking public services and aging infrastructure in Chinese cities (Fang et al., 2016; Liu, 2018; Liu et al., 2014; Yu et al., 2014; Zhan et al., 2018). These so-called “urban diseases” (Fang and Yu, 2016) have adversely impacted on city residents’ daily lives across the entire city. Under such circumstances, both the Chinese government and scholars attempt to address urban livability issues in recent years (Zhan et al., 2018). Although used liberally, the term urban livability often lacks a consensus of what exactly it refers to. Kashef (2016) summarizes broadly three different aspects of urban livability research, namely, from the design literature, the planning literature, and the popular media and global ranking literature, and advocates for an interdisciplinary understanding that potentially considers all aspects of urban livability. Such suggestions, though conceptually appealing, often lack practical operability. Urban planners and other urban science practitioners, on the other hand, carefully consider urban livability as a balanced and harmonious mode of economic, social, cultural, land use and environmental development in cities (Asgarzadeh et al., 2012; Flores et al., 1998; Kazemi et al., 2018; Liu et al., 2014).

The aim of this study is to assess the water quality of Lower Jhelum Canal (LJC) and its suitability for irrigation purposes. An effort has been made to develop a method by integrating water quality index with geographic information system (GIS) for an effective interpretation of LJC water quality. The pollution status of LJC was estimated by different physicochemical and biological parameters. Based on results of analysis, a spatial distribution map of selected water quality parameters was prepared using GIS. An inverse distance weighting (IDW), which is an interpolation technique, was applied to prepare a thematic layer of parameters at each station of Lower Jhelum canal. The results of individual parameters showed that the concentrations of contamination were within permissible limits of WHO and NEQS guidelines except for E. coli. Overall, most of the water falls in excellent quality category indicating the suitability of water for irrigation purpose. The results suggest that most of the water can be used for irrigation and various intended purposes except direct use of water for potable or drinking purposes without treatment.

Introduction

Water is the most significant ingredient for supporting and evolution of life (Kuutondokwa, 2008). Humans get different sorts of benefits from freshwater, which includes water for drinking, industrialization, domestic uses, irrigation, for the production of waterfowl and fisheries, use for leisure, shipping, and waste discarding (Jackson et al., 2001). Water is a source of economic gain. Almost 70% of water is used in agricultural production, which is indirectly the cause of economic growth (Brown and Matlock, 2011). Water resources (Freshwater) are becoming limited for individuals due to overpopulation, so the accessibility of freshwater for human beings decreases (Iqbal et al., 2018). Quality of freshwater deteriorates by developmental activities that contaminate the water bodies, their effects on human health, ecosystem disturbance and issues related to its management and monitoring (Iqbal et al., 2019). Water quality deterioration is the primary threat to public health at the global level (Rahman et al., 2020). Anthropogenic actions, like improper disposal of municipal, industrialized effluents, and unsystematic use of chemicals in agriculture, are vital aspects causative in the worsening of water quality (Azizullah et al., 2011; Iqbal et al., 2020; Subedi et al., 2019; Shirani et al., 2018; Li et al., 2020). The result of these activities is eutrophication, loss of water quality, loss of biodiversity, effects on human health and social security, deposition of nutrients and other inorganic pollutants, acidification and significant economic losses (Kraemer et al., 2001). Contamination of water leads to water scarcity or it may be polluted at that level where it is expensive to treat (Gupta et al., 2012; Maged et al., 2020; Imran et al., 2020). Consequences of both water treatment and water scarcity with high cost would be the reason for the increase in water prices (Kuutondokwa, 2008). According to (Rogers et al., 2002), water is an efficient good. However, the nations which have water resources could grow their economies, while on the other hand, it will be difficult for poor or developing countries, even to avail of their essential needs.

1- Introduction

2- Methodology

3- Results

4- Discussion

5- Conclusion

References

Abstract

Weather radar can provide spatially explicit precipitation grids. However interference, ground clutter and various causes of attenuation introduce uncertainty into the result. Typically, rain gauge observations, recognized as a precise measure of precipitation at point locations, are used to adjust weather radar grids to obtain more accurate precipitation maps. This adjustment involves one or more of various geostatistic techniques. Yet, since gauges are sparsely located, a geostatistic approach is sometimes limited or even not applicable.
This work adopts an alternative to radar adjustment by merging location-based variables with rain grids from weather radar. Recognizing that location-based variables: elevation, slope, aspect and distance from the coast all affect precipitation, these are applied to the original weather radar grid to produce an altered precipitation distribution.
The merging procedure presented here uses fuzzy logic, whereby all variables, as well as the original radar are assigned probabilities known as membership functions (MF), then a joint membership function (JMF) combines all MFs in the fuzzy set, each multiplied by its weight, to create a precipitation probability grid. This JMF probability grid is validated with gauge observation data. We show up to 30% higher correlation coefficients between gauges and the JMF grid than between gauges and the original radar. The improved correlation results from the flexibility of fuzzy logic in transforming location-based variables to probabilities.

Background

Estimating spatially distributed precipitation grids is a prerequisite to flood management and flood forecasting (Merz et al. (2014)). Hydrological models need basin-scale, spatially explicit precipitation data, among other inputs, to construct accurate flood forecasts (Todini et al. (2005)) for surface runoff management. Rain radar can produce such spatial precipitation distributions, however the challenges in calibrating and correcting for the various sources of error (detailed in Villarini et al. (2008)) create spatial and temporal uncertainty in the precipitation distribution (Cecinati et al. (2017), Krajewski and Smith, 2002). Nevertheless, the underlying motivation for research in improving precipitation maps rests in the needs of hydrological modeling and flood forecasting. Since weather radar became an accepted source of spatially distributed rainfall (Krajewski and Smith (2002), Morin et al. (2003)), extensive research has examined adjustment procedures to merge rain gauge observations with weather radar. Gauge data are accepted as reference observations (see for example Colli et al. (2013)), but represent point locations. Such point data can adjust weather radar grids through several geostatistic methods, reviewed and evaluated by Goovaerts (2000), Berndt et al. (2014) and McKee and Binns (2016). Kriging based methods have been examined by Kebaili Bargaoui and Chebbi (2009), Adhikary et al. (2017), and Ly et al. (2013). A comparison of various kriging methods where elevation was the secondary variable was done by Carrera-Hernández and Gaskin (2007). Another unique algorithm known as Conditional Merging, developed and evaluated by Sinclair and Pegram (2005), applies multiple kriging steps to achieve successful adjustment (Kim et al. (2007)) of weather radar grids.

1- Introduction

2- Exploration information systems (EIS)

3- Discussion

4- Concluding remarks

References

Abstract

The advent of modern data collection and storage technologies has brought about a huge increase in data volumes with both traditional and machine learning tools struggling to effectively handle, manage and analyse the very large data quantities that are now available. The mineral exploration industry is by no means immune to this big data issue. Exploration decision-making has become much more complex in the wake of big data, in particular with respect to questions about how to best manage and use the data to obtain information, generate knowledge and gain insight. One of the ways in which the mineral exploration industry works with big data is by using a geographic information system (GIS). For example, GIS platforms are often used for integration, interrogation and interpretation of diverse geoscience and mineral exploration data with the goal of refining and prioritising known and identifying new targets. Here we (i) briefly discuss the importance of carefully translating conceptual ore deposit models into effective exploration targeting maps, (ii) propose and describe what we term exploration information systems (EIS): a new idea for an information system designed to better integrate the conceptual mineral deposit model (i.e., the critical and constituent processes of the targeted mineral system) with data available to support exploration targeting, and (iii) discuss how best to categorise mineral systems in an EIS as scale-dependent subsystems to form mineral deposits. Our vision for the future use of EIS in exploration targeting is one whereby the mappable ingredients of a targeted mineral system are translated and combined into a set of weighted evidence (or proxy) maps automatically, resulting in an auto-generated mineral prospectivity map and a series of ranked exploration targets. We do not envisage the EIS replacing human input and ingenuity; rather we envisage the EIS as an additional tool in the exploration toolbox and as an intelligence amplifying system in which humans are making use of machines to achieve the best possible results.

Introduction

Mineral exploration targeting requires the compilation, integration and interrogation of diverse, multi-disciplinary data (e.g., Hronsky and Groves, 2008). In today’s world, this is commonly done using a geographic information system (GIS), the light table environment of our modern computer-age. A GIS is a powerful computer-based system designed to capture, store, manipulate, analyse, manage, and present spatial data (e.g., Groves et al., 2000) and their non-spatial attributes that can be stored in linked, interrogatable tables. In mineral exploration targeting, as in many other fields of applied geoscience, GIS has surpassed the human ability to integrate and quantitatively analyse the ever-growing amount of geospatial data available, thereby progressively replacing the traditional working methods. Over the past two decades and driven by significant improvements in soft- and hardware capabilities, powerful GIS-based, algorithm-driven methods have been developed in support of exploration targeting. These methods fall under the umbrella term of mineral prospectivity mapping (MPM), also known as mineral prospectivity analysis, spatial predictive modelling or mineral potential modelling (e.g., Bonham-Carter, 1994; Pan and Harris, 2000; Carranza, 2008; Porwal and Kreuzer, 2010; Yousefi and Nykänen, 2017; Hronsky and Kreuzer, 2019).

1- Introduction

2- Methodology

3- Results

4- Discussion

5- Conclusions

References

Abstract

Uncertainty in capturing the effects of riparian tree shade for assessment of algal growth rates and water temperature hinders the predictive capability of models applied for river basin management. Using photogrammetry-derived tree canopy data, we quantified hourly shade along the River Thames (UK) and used it to estimate the reduction in the amount of direct radiation reaching the water surface. In addition we tested the suitability of freely-available LIDAR data to map ground elevation. Following removal of buildings and objects other than trees from the LIDAR dataset, results revealed considerable differences between photogrammetry- and LIDAR-derived methods in variables including mean canopy height (10.5 m and 4.0 m respectively), percentage occupancy of riparian zones by trees (45% and 16% respectively) and mid-summer fractional penetration of direct radiation (65% and 76% respectively). The generated data on daily direct radiation for 2010 were used as input to a river network water quality model (QUESTOR). Impacts of tree shading were assessed in terms of upper quartile levels, revealing substantial differences in indicators such as biochemical oxygen demand (BOD) (1.58–2.19 mg L−1 respectively) and water temperature (20.1 and 21.2 °C respectively) between ‘shaded’ and ‘non-shaded’ radiation inputs. Whilst the differences in canopy height and extent derived by the two methods are appreciable they only make small differences to water quality in the Thames. However such differences may prove more critical in smaller rivers. We highlight the importance of accurate estimation of shading in water quality modelling and recommend use of high resolution remotely sensed spatial data to characterise riparian canopies. Our paper illustrates how it is now possible to make better reach scale estimates of shade and make aggregations of these for use at river basin scale. This will allow provision of more effective guidance for riparian management programmes than currently possible. This is important to support adaptation to future warming and maintenance of water quality standards.

Introduction

The influence that riparian vegetation exerts on river water temperatures and light availability by intercepting incoming solar radiation has long been studied (Davies-Colley and Rutherford, 2005; Greenberg et al., 2012; Moore et al., 2005; Webb et al., 2008). Shading is a key parameter due to the control it exerts over the amount of direct radiation reaching the river surface making it an important consideration in water quality modelling and management. Solar radiation has direct effects on rates of primary production of both macrophytes and algae (Bowes et al., 2016, 2012b; Wood et al., 2012) which is important for river metabolic regime and is known to be influenced by riparian shade (Bernhardt et al., 2017). Water temperature also directly influences river fauna and dissolved oxygen concentrations. Therefore considerations of shading are of growing importance given the increasing stress on the water environment likely to arise under future climate. Effective and realistic riparian planting schemes to mitigate against these unwanted effects will become increasingly valuable and enhance water ecosystem services (Martin-Ortega et al., 2015). They will provide alternatives to traditional end-of-pipe solutions arising primarily from the EU Urban Wastewater Treatment Directive, which have been assessed through modelling (e.g. at large basin scale across Europe: Grizzetti et al., 2011).

Loss of the vegetative cover becomes the paying way of accelerated carbon emissions. It leads to increase in Land Surface Temperature (LST) and resultant resulting in global warming. This study has deployed remote sensing and GIS to observe LST variations in Islamabad to study vegetative cover loss for the time span of 25 years (1992e2017). The study’s findings confirmed 22% reduction in the vegetative cover from 1992 to 2000 with LST range 13e27
C/year. It was followed by continuous reduction of the said cover up-to 27% between 2000 and 08, confirming 16e34
C/year increase in LST. Likewise, from 2008 to 2017, alarmingly 51% vegetative cover loss contributing to 23e43
C/year rise in LST in the study area. The results confirmed correlation between loss of vegetative cover and LST that is contributing into global warming. So, attempts to halt massive urbanization so as to reduce the polluting gateways and increasing carbon sinks efforts through afforestation and reforestation remains essential to safeguard the humanity from climatic hazards.

Introduction

The 21st century’s dramatic changes in the climatic patterns became the biggest malice and severe threats to growth and development, yet alone human survival. Forests are the diverse environmental subjects, which have the capacity to adapt to the changes in the ecosystem and provide positive sustainable returns to the environment (Aljerf, 2017). But there is great threat to forest cover und the broader domain of vegetative cover on the land surface and the main source is humans. The reported adverse impacts erosion, drought and loss of biodiversity (Xie et al., 2017; Gao et al., 2017). Similarly, human-induced activities remain the main causes behind the loss of forest cover, e.g., removal of forests and/or reduction in the capacity of the forests to serve as carbon sinks (Zhang et al., 2015). The other and the most severe reason behind the loss of forest cover and there-upon massive carbon release is the rapid urbanization (Bhatt, 1990). Here, urbanization is amongst the critical factors affecting forest cover that is posing severe impacts on both regional and global environment (Lin et al., 2019). Thus, urban settlements, amongst other major threats, are posing increasing challenge as land cover change is occurring from forested land, high coverage rate grassland etc. to bare land and massive physical infrastructure development. It therefore decreases the net decrease in the rate of grass and forest land as of growing human activities (Li et al., 2017). Thus, land use change as of conversion of forest land effects the terrestrial ecosystems and thereupon the carbon storage, thus leading to anthropogenic carbon emissions. The transfer of land to build high land infrastructures in urban settings is accounted for the large scale conversion of land and associated increased carbon emissions (Chuai et al., 2015).

In the task of using deep learning semantic segmentation model to extract water from high-resolution remote sensing images, multiscale feature sensing and extraction have become critical factors that affect the accuracy of image classification tasks. A single-scale training mode will cause one-sided extraction results, which can lead to ‘‘reverse’’ errors and imprecise detail expression. Therefore, fusing multiscale features for pixel-level classification is the key to achieving accurate image segmentation. Based on this concept, this paper proposes a deep learning scheme to achieve fine extraction of image water bodies. The process includes multiscale feature perception splitting of images, a restructured deep learning network model, multiscale joint prediction, and postprocessing optimization performed by a fully connected conditional random field (CRF). According to the scale space concept of remote sensing, we apply hierarchical multiscale splitting processing to images. Then, we improve the structure of the image semantic segmentation model DeepLabV3+, an advanced image semantic segmentation model, and adjust the feature output layer of the model to multiscale features after weighted fusion. At the back end of the deep learning model, the water boundary details are optimized with the fully connected CRF. The proposed multiscale training method is well adapted to feature extraction for the different scale images in the model. In the multiscale output fusion, assigning different weights to the output features of each scale controls the influence of the various scale features on the water extraction results. We carried out a large number of water extraction experiments on GF1 remote sensing images. The results show that the method significantly improves the accuracy of water extraction and demonstrates the effectiveness of the method.

Introduction

Accurate extraction of water information from remotesensing images has always been an important research topic in the field of remote sensing image analysis because it plays a vital role in national land/water resource monitoring and environmental protection. The traditional remote sensing image water body extraction methods are divided into two main types. One is based on the spectral characteristics of water bodies and involves setting different thresholds and using a water body index to classify and extract water information [1]–[3]. The other is based on mathematical morphology [4] and segments water body edges [5], [6] to extract their features. Among the two types of methods, the index method (NDWI), which is a spectrum-photometric method, is widely used in the task of water extraction because of its simplicity and universality. In actual appliances, especially in the large-scale daily monitoring of water body nationwide, it is inevitable to process massive remote sensing images. The traditional methods with more manual intervention are not able to guarantee the quality of data products. In particular, there is substantial random interference in images, such as clouds, shadows, fog, etc. Even if some models have good generality, there will also be an extraction loss on the details of water.

Photovoltaics (PV) and wind constitute approximately half of the world’s new generation capacity installed in 2014–16. At the end of 2016, the global installations of PV and wind were beyond 300 gigawatts (GW) and 480 GW respectively [1,2]. Rapid growth of PV and wind energy in the electricity sector is expected to continue, driven by a broad range of issues associated with climate change, energy security and economics. High shares of intermittent PV and wind energy in electricity grids bring significant challenges to the economics and security of the system as is the case in South Australia (SA), where nearly half of the state’s electricity production come from rooftop PV and wind farms [3]. SA has a low level of interconnection with the rest of the Australian National Electricity Market (NEM) and there is no existing hydroelectric or pumped hydro facility established within the region. This brings significant challenges to power system operation and the state’s energy security due to supply intermittency and lack of sufficient inertial energy to support PV and wind electricity, especially in light of continuing rapid growth of PV and wind energy investment. In July 2016, when upgrades to the Heywood interconnector coincided with low wind generation at peak times, the average wholesale electricity prices in SA surged to $229/MWh (Australian dollars per megawatt-hour) with 3 extreme price events on 7, 13 and 14 July beyond $5000/MWh [4]. By contrast, the long-term average price in SA when the interconnector is available to import brown coal electricity from Victoria is $50/MWh. Additionally, a range of system events such as load shedding and islanding occasionally occurred in 2016–17 [5,6]. This included a state-wide blackout on 28 September 2016, when three 275 kilovolts (kV) backbone transmission lines were damaged by a major storm event [7]. Pumped hydro energy storage (PHES) is capable of large-scale energy time shifting and a range of ancillary services such as frequency regulation, which can facilitate high levels of photovoltaics and wind integration in electricity systems. Developments of PHES began in the 1890s and surged through the 1960s, 70s and 80s in Europe, the United States and Japan where the rapid growth of nuclear energy and coalfired units continued. These large thermal steam plants lack sufficient operational flexibility to accommodate changing demand and required the capability of load levelling. PHES was also regarded as a more economical alternative to oil and natural gas-fired plants for peak shaving, especially during the post-periods of energy crisis in the 1970s [8,9]. In recent years, the prosperity of PV and wind developments has led to a resurgence of interest in PHES. Open-loop PHES, which is continuously connected to a naturally flowing water feature [10], dominates the deployment of existing PHES. However, developments of conventional river-based hydroelectric including PHES are usually constrained by the availability of water resources and a variety of environmental concerns such as the interactions with ecology and natural systems [11]. Consequently, expansions of pumped hydro were generally not included in many high renewables future studies such as [12–14]. By contrast, short-term off-river PHES, which incorporates closed-loop pumped hydro systems, consumes modest amounts of water and has little impacts on the environment and natural landscape.

Architectural and urban planning works influence both formation and transformation of historic character of a city, which can be accepted as an evidence of the past society that gives the guidelines for the future development and prosperity. Revitalization and conservation of historic urban areas include aspects of maintenance and renewal of their physical traditional texture together with economic and social dynamism on their development. Hence, the multi-layered structure of a historic city center is necessary to be understood in unity despite of its complex character, which should be defined in a systematic way of geographical information management. Historic urban areas are vulnerable to number of stressors such as rapid urbanization, population growth and natural disasters. Management strategies related to historic urban areas aim to provide the use of cultural heritage for sustainable development of cultural landscapes and natural environment. As Agapiou et al. (2015) mention, conservation of cultural heritage areas require innovative and cost effective tools for systematic monitoring which require costly and time consuming data and information collection procedures for large areas. The importance of digital photogrammetry and laser scanning during process of a digital documentation for an immovable is also reminded by Yastıklı (2007), while geographical information systems (GIS)-based surface analysis is capable to be done systematically on investigating former status of various sites, from archaeological and natural sites to historic mining areas, as studied by Kovács et al. (2011) and Al-kheder et al. (2008). In addition to analyzing the morphological development of a historic city, heritage conservation process is also essential to be assessed and elaborated by digitizing collected input data in different methods. There are several tools, one of which is known as GIS, which is used to manage large data sets for developing sustainable management plans of historic urban areas. The process of urban conservation can be facilitated by making use of spatial database in this system, as already been defined by the scholars (Bilgin Altınöz, 2002; Günay, 2009; Lerotic, 2011). Moreover, He et al. (2015) state that the use of GIS systems brings their own challenges for supporting decision-making strategies because of the large scales of their database system despite of the remarkable advances in its field to support management of cultural heritage sites. As one of the historic cities in Turkey, Bursa, a Nomination File of Bursa as UNESCO World Heritage Site (WHS) since 2014, has followed legal and organizational aspects in conservation issue, which also being witnessed to practices occurred in conservation of historic urban areas since the end of the nineteenth century. It is significant as being the first capital of Ottoman Empire and a developing industrial city in Turkey. Various types of cultural properties have been subjected to different conservation activities, from the public participated symposiums to the restoration applications. These activities have continuously effected to the sustainability of both tangible and intangible cultural heritage in Bursa, which should be studied by searching on historical documents describing faults and successes on the applied projects.

There have been many successes in national development in the health sector, but the Indonesian people have not yet achieved the “Healthy Indonesia” as desired in the National Long Term Development Plan [1]. The unfulfilled maternal mortality rate is still high, infant mortality rate is still high, while there are still many short stunting children, and various nutritional problems. In the field of disease control, they are exposed to a double burden, infectious diseases such as AIDS, Tuberculosis and Malaria are still high prevalence, while non-communicable diseases such as Hypertension, Diabetes, Cancer, and Mental Disorders continue to grow. Although the source of funds for the health sector and also human resources of health sector in terms of quantity, type, quality, and even distribution continues to increase, the increase in resources has not been able to fully offset the increased needs. Realizing such problems, to achieve Healthy Indonesia within the period of 2015 – 2019, the health sector efforts, now are directed to focus to:  Reduce Maternal Mortality Rate and Infant Mortality Rate.  Lower prevalence of short toddlers (stunting).  Tackling infectious diseases, HIV-AIDS, Tuberculosis, and Malaria.  Tackling non-communicable diseases Hypertension, Diabetes, Obesity, Cancer, and Mental Disorders. In order for existing health sector resources can be utilized effectively and efficiently, those efforts has been conducted in an integrated manner from planning to implementation, monitoring and evaluation. The goal was focused on the “Family Approach”. To realize the goal of Family Approach in Healthy Indonesia program above, The Minister of Health, Republic of Indonesia issued a ministerial regulation. Regulation of the Minister of Health, Republic of Indonesia No. 39 Year of 2016 about Guidelines for the Program of Healthy Indonesia Programs with Family Approach, and in Article 5 of the Ministerial Regulation states “the Implementation of Healthy Indonesia Program with Family Approach implemented by Puskesmas (Community Health Center)” [1]. On the other hand, Jembrana Regency has been 10 years implementing Information Systems on its community service. So, to support the achievement of a Healthy Indonesia goal which is mandated to the puskesmas, it is necessary to integrate the health sector initiative into the Blueprint of e-Government Jembrana Regency. Through eGovernment Jembrana Regency implementation, Jembrana’s government services will take place in a transparent and traceable process, so it can be considered as accountable public services. Moreover, elements of irregularities can be avoided and services can be delivered effectively and efficiently. The E-Government Integration in this study called E-Prokesmas, a GIS Web-Based software application and expected to become a family-based health monitoring tool for Community Health Program in Jembrana Regency.