Urban Inclusion and Environmentally Responsive Architecture: Creating a Women-Friendly Space in Izbat al-Burg, Damietta

Introduction As the urban population continues to grow, understanding ecosystems in the city has become increasingly important and is now a rallying cry for action to respond to and mitigate the repercussions of growing urban sprawl. Urban areas echo a rather harsh image of concrete jungles with impoverished ecosystems and a disconnection between the built and natural environments. Although the current trend towards urbanization repeatedly manifests this visual image – putting the viability o..

Middle Eastern Cities in a Time of Climate Crisis

The climate crisis is hitting around the world, including in the Middle East and its cities. Urban regions are exposed to increasingly frequent heat waves and floods that leave decision makers without immediate answers. In the context of this global crisis, this book addresses the need for a better understanding of the current model of urban expansion. Cities are major sources of greenhouse gas (GHG) emissions but they are also celebrated for their contribution to economic growth. The current moment is one of a large paradigm shift as climate change is now recognized as a legitimate public problem. This is especially true for city dwellers, who are increasingly exposed to climate change, the loss of biodiversity and heavy pollution while natural breathing spaces continue to shrink around them. The sixteen chapters of this book do not offer any off-the-rack or technical solutions, but they analyze the urban conundrum and the contribution of cities to the climate crisis. Some chapters focus on individual car ownership, land privatization, waste management and land use changes under the guise of development. Others explore local and contextual answers to urban governance issues. With the support of CEDEJ and the Friedrich-Ebert-Stiftung, researchers, experts and civil society actors explore the ongoing transformations of Middle Eastern urban environments and mobilities and question them in relation to the climate crisis. The contributions are based on empirical knowledge gathered in the Nile Delta, the Greater Cairo Region, Riyadh and Beirut. Without concessions to mainstream thinking, this book contributes to a better understanding of urban challenges, climate threats and policy responses in contexts marked by growing environmental inequalities

Assessing the Qatari Food Security Situation During The 2017 Crisis: Potentials, Achievements, and Challenges

تهدف الورقة إلى رصد أبرز تداعيات أزمة الحصار (2017) التي مرت بها دولة قطر في مجال الأمن الغذائي، الذي كانت تعتمد فيه الدولة بشكل أساسي على الاستيراد من الخارج، لاسيما بعد إغلاق حدودها البرية الوحيدة مع السعودية، إلى جانب إغلاق المجال الجوي الخاص بالدول المشارِكة في الأزمة بشكل مباشر (السعودية، الإمارات العربية المتحدة، البحرين، مصر). مما ضاعَف التحديات في وجه قطر لضمان استقرار الجبهة الداخلية، واستقرار الأسواق، وتأمين حاجات الناس داخل الدولة. لكن ما حصل أنّ قطر استطاعت الحفاظ على استقرار الأسواق، وتأمين إمدادات الغذاء لحوالي 2.5 مليون مواطن ومقيم، ولم يكن هذا مجرد استراتيجية مؤقتة لمواجهة الأزمة فحسب، وإنما استمرت لما بعدها وأصبحت قطر تصدّر بعض المنتجات الغذائية بعد تحقيق الاكتفاء الذاتي منها. في ظل المعطيات السابقة، تعمل الورقة على مقاربة مجموعة من التساؤلات البحثية من قبيل: كيف حصل هذا الإنجاز القطري في مجال الأمن الغذائي؟ وما مستقبل الأمن الغذائي في دولة قطر؟ وتتوسل الورقة منهجية علمية وصفية تحليلية تستند إلى استقراء وتحليل للإجراءات التي اتخذتها الدولة لتجاوز الأزمة (الإمكانات)، وصولًا إلى الحالة المتقدمة التي وصلت إليها قطر في مجال الأمن الغذائي (الإنجازات) وهي تمثل النتائج التي توصلت إليها الورقة، ويمكن اختصارها في أنّ قطر استطاعت الوصول إلى مرحلة متقدمة من مراحل الأمن الغذائي، مستفيدةً من إمكاناتها الذاتية. إلا أن هذه الورقة تكتسب أصالتها وقيمتها من خلال تحليل الاستراتيجية التي اتبعتها الدولة في التركيز على استثمار الإمكانات الذاتية الداخلية لتجاوز تحديات الحصار في مجال الأمن الغذائي وجعْل تلك الإمكانات جسرًا للتعاون مع الأطراف الدولية الخارجية.This paper aims to examine the significant repercussions of the 2017 blockade crisis on Qatar's food security. The crisis led to Qatar's isolation due to the closure of its only land border with Saudi Arabia and the airspace by the countries directly involved in the crisis (Saudi Arabia, the United Arab Emirates, Bahrain, and Egypt). These events exacerbated the challenges that Qatar faced in maintaining internal and market stability, as well as securing the essential needs of its population. However, Qatar managed to overcome this bottleneck phase, successfully maintaining market stability and securing food supplies for around 2.5 million citizens and residents. This achievement was not merely a temporary strategy to address the crisis; rather, it persisted beyond the crisis, with Qatar even exporting some food products after achieving self-sufficiency. Given this context, this paper addresses several research questions, such as: How did Qatar achieve this food security accomplishment? What is the future of food security in Qatar? The paper adopts a scientific descriptive-analytical approach based on the extrapolation and analysis of the measures taken by the state to overcome the crisis (potentials), leading to the advanced stage of food security in Qatar (achievements), which represent the findings of the study. These findings can be summarized as Qatar's successful attainment of an advanced stage of food security, benefiting from its internal potentials. The research gains authenticity and value by analyzing the strategy Qatar adopted, focusing on investing in its internal self-sufficiency to overcome the challenges of the blockade in the realm of food security and leveraging these potentials as a bridge for cooperation with external international parties

Stream flow analysis and modelling using artificial intelligence techniques / Mohammed A. B. Seyam

The reliable prediction of stream flow (SF) is an important aspect in the planning, design and management of surface water and rivers systems. This prediction can be performed using either process-based or data driven-based models (DDMs). Several modelling approaches fall under DDMs, such as statistical and artificial intelligence (AI) techniques. AI includes artificial neural networks (ANNs), support vector machines (SVM) and other techniques. The main goal of this research is to develop and employ a group of efficient AI-based models for predicting the real-time hourly stream flow (Q) in the downstream area of the Selangor River basin, taken here as the paradigm of humid tropical rivers in Southeast Asia. The Q of this river is yet to be subjected to prediction using AI. Despite intensive applications of monthly and daily SF prediction using AI over the last two decades, the prediction of Q is rare, particularly in small rivers in humid tropical regions, such as the Selangor River. The significance of this research lies in the uniqueness of the considered process and the novelty of the applied methodology in the modelling process. The performance of AI-based models can be improved through the integration of the hydrological description of SF in the modelling process through estimation of lag time (Lt) and analysis of the long-term changes of SF regimes which verified considerable changes may potentially result in increasing the probability of floods occurring in future. The integration process is essential to the selection of input and output variables of AIbased models and the lag intervals between them. The modelling process are performed in two phases to explore the possibility of improving the performance of AI-based models through the accurate timing of the model variables based on Lt estimation by two approaches, namely, the correlation coefficient and hydrological graphical approaches. Through the two modelling phases, four AI techniques, which include three types of ANNs, namely, the multi-layer perceptron network, radial basis function network, and generalized regression neural networks, along with SVM, are employed to develop six AI-based models to predict the Q. Three scenarios were employed to achieve six combinations of input variables, the first adopts RF and the second adopts WL while the third adopts both WL and RF as input variables. A total of 8753 patterns of Q, water level, and rainfall hourly records representing a one-year period (2011) were utilized in the modelling process. The performance evaluation of the developed AI-based models shows that high correlation coefficient (R) between the observed and predicted Q is achieved by most of the developed models. For example, R in SVM-M6 model is 0.992 and 0.953 for the training and testing data sets, respectively. The developed AI-based models were efficiently employed in some hydrological applications, such as Q prediction, analysis of the influence of both water level and rainfall on Q and estimation of the missing records of Q. They also were employed in flood early warning throughout the advanced detection of hydrological conditions that could lead to formations of floods

Groundwater Salinity Modeling Using Artificial Neural Networks

The main source of water in Gaza Strip is the shallow aquifer which is part of the coastal aquifer. The quality of the groundwater is extremely deteriorated in terms of salinity. Salinization of groundwater may be caused and influenced by many variables. Studying the relation of between these variables and salinity is often a complex and nonlinear process, making it suitable for Artificial Neural Networks (ANN) application. In order to model groundwater salinity in Gaza Strip using ANN it is necessary to gather data for training purposes. Initially, it is assumed that the groundwater salinity (represented by chloride concentration, mg/l) may be affected by some variables as: recharge rate (R), abstraction (Q), abstraction average rate (Qr), life time (Lt), groundwater level Wl, aquifer thickness (Th), depth from surface to well screen (Dw), and distance from sea shore line (Ds). Data were extracted from 56 wells, most of them are municipal wells and they almost cover the total area of Gaza Strip. The initial modeling trials were made using all input variables and many trials were applied to get best performance model. From the created ANN models, the importance and effect of each variables was studied and represented, also depending on the results of ANN models some input variables were neglected and new modeling trials are made without using neglected input variables. After a number of trials, the best neural network was determined to be Multilayer Perceptron network (MLP) with four layers: an input layer of 6 neurons, first hidden layer with 10 neurons, second hidden layer with 7 neurons and the output layer with 1 neuron. The six input neurons are: initial chloride concentration (Clo), recharge rate (R), abstraction (Q), abstraction average rate of area (Qr), life time (Lt), aquifer thickness (Th). The output neuron gives the final chloride concentration (Clf). The ANN model generated very good results depending on the high correlation between the observed and simulated values of chloride concentration. The correlation coefficient (r) was 0.9848. The high value of (r) showed that the simulated chloride concentration values using the ANN model were in very good agreement with the observed chloride concentration which mean that ANN model is useful and applicable for groundwater salinity modeling. ANN model was successfully utilized as analytical tool to study influence of the input variables on chloride concentration. It proved that chloride concentration in groundwater is directly affected by abstraction (Q), abstraction average rate (Qr) and life time (Lt). Furthermore, it was adversely affected by recharge rate (R) and aquifer thickness (Th). Furthermore, it is utilized as simulation and prediction tool of chloride concentration in domestic wells in Gaza Strip, the prediction of chloride concentration will be based on some scenarios of abstraction from groundwater. Also it will be used as a decision making support tool that suggests the appropriate abstraction from groundwater wells comparing with the status of salinity

طريقة جديدة لإدارة نوعية المياه الجوفية

The main source of water in Gaza Strip is the shallow aquifer, the quality of the aquifer's groundwater is extremely deteriorated in terms of salinity. Salinization of groundwater may be caused and influenced by many variables. Studying the relation of between these variables and salinity is often a complex and nonlinear process, making it suitable to model by Artificial Neural Networks (ANN). In order to model groundwater salinity in Gaza Strip using ANN it is necessary to gather data for training purposes. Initially, it is assumed that the groundwater salinity (represented by chloride concentration, mg/l) may be affected by some variables as: recharge rate (R), abstraction (Q), abstraction average rate (Qr), life time (Lt), groundwater level Wl, aquifer thickness (Th), depth from surface to well screen (Dw), and distance from sea shore line (Ds). Data were extracted from 56 wells, most of them are municipal wells and they almost cover the total area of Gaza Strip. After a number of trials, the best neural network was determined to be Multilayer Perceptron network (MLP) with four layers: an input layer of 6 neurons, first hidden layer with 10 neurons, second hidden layer with 7 neurons and the output layer with 1 neuron. The ANN model generated very good results depending on the high correlation between the observed and simulated values of chloride concentration. The correlation coefficient (r) was 0.9848. The high value of (r) showed that the simulated chloride concentration values using the ANN model were in very good agreement with the observed chloride concentration which mean that ANN model is useful and applicable for groundwater salinity modeling. The ANN model proved that chloride concentration in groundwater is directly affected by abstraction (Q), abstraction average rate (Qr) and life time (Lt) and it was inversely affected by recharge rate (R) and aquifer thickness (Th). The approach is reasonable for the new planning and management of water resources through the attended reconstruction process in Gaza.تعتبر المياه الجوفية المصدر الرئيسي للمياه في قطاع غزة و هي معرضة للتلوث وخصوصاً فيما يتعلق بازدياد معدلات الملوحة التي تتواجد وتتأثر بالعديد من العوامل. دراسة هذه العوامل عادة ما تكون عملية معقدة مما يجلها مناسبة لتدرس من خلال نظام الشبكات العصبية الصناعية. إن نمذجة ملوحة المياه الجوفية من خلال الشبكات العصبية الصناعية تتطلب جمع البيانات اللازمة لعملية التدريب التي تقوم بها الشبكة العصبية. في البداية أُفترض أن ملوحة المياه الجوفية المتمثلة بكمية الكلوريد في المياه الجوفية تتأثر بتسعة عوامل هي معدل تسرب مياه الأمطار للخزان الجوفي و كمية السحب الخاصة بكل بئر ومعدل السحب من الخزان الجوفي و المدة الزمنية التي تعرض فيها الخزان الجوفي للسحب و منسوب المياه الجوفية و سمك الخزان الجوفي و عمق الخزان الجوفي و المسافة بين منطقة السحب و البحر ولقد استخرجت هذه البيانات من 56 بئر مياه تغطي معظم مساحة قطاع غزة. تم تنفيذ عدة محاولات للحصول على نموذج يعطى نتائج جيدة. في البداية تمت عملية النمذجة باستخدام جميع العوامل المفترضة و من النماذج التي تم تطويرها تم دراسة تأثير العوامل على تركيز الكلوريد في المياه الجوفية و بناء على الدراسة تبين أنه يمكن تجاهل بعض العوامل و تم عمل محاولات أخرى تبين من خلالها أن أفضل شبكة عصبية تم التوصل إليها هيMultilayer Perceptron network (MLP) و تتكون من أربع طبقات هي طبقة المدخلات و يوجد بها 6 نيورن و الطبقة المخفية الأولى و يوجد بها 10 نيورن و الطبقة المخفية الثانية و يوجد بها 7 نيورن وطبقة المخرجات و يوجد بها نيرون واحد. طبقة المدخلات تمثل العوامل التالية تركيز الكلوريد الابتدائي و معدل تسرب مياه الأمطار للخزان الجوفي و كمية السحب الخاصة بكل بئر ومعدل السحب من الخزان الجوفي و المدة الزمنية التي تعرض فيها الخزان الجوفي للسحب و سمك الخزان الجوفي أما طبقة المخرجات فتمثل تركيز الكلوريد النهائي. لقد أعطت الشبكة العصبية نتائج ممتازة اعتماداً على التقارب الكبير بين القيم الحقيقة و القيم المستخرجة من النموذج حيث بلغت قيمة معامل الارتباط 0.9848 و هذا يعني أن هناك توافقا كبيراً بين القيم الحقيقة و القيم المستخرجة من النموذج مما يجعل النموذج صالحاً للاستخدام و التطبيق. تم استخدام النموذج بنجاح كأداة لدراسة تأثير العوامل على تركيز الكلوريد حيث تبين أن تركيز الكلوريد يتناسب طردياً مع كمية السحب الخاصة بكل بئر ومعدل السحب من الخزان الجوفي والمدة الزمنية التي تعرض فيها الخزان الجوفي للسحب و أنها تتناسب عكسياً مع معدل تسرب مياه الأمطار للخزان الجوفي و سمك الخزان الجوفي واستخدم النموذج كوسيلة للتنبؤ بتركيز الكلوريد من الخزان الجوفي في المستقبل وذلك في حالة إعادة إعمار غزة أيضاً

Assessment of long-term water demand for the Mgeni system using Water Evaluation and Planning (WEAP) model considering demographics and extended dry climate periods

The Mgeni System is recognised as the main source of water supply for the Durban and Pietermaritzburg region in South Africa. This area is regarded as the primary economic hub of KwaZulu-Natal Province, and this brings about a high level of demographic pressure, with potential water supply problems in the future. This study investigates the water resource situation in the Mgeni System and evaluates future supply and demand accounting based on the (Water Evaluation and Planning) WEAP software. WEAP was used to analyse the study area for the period 2009–2050 to assess the impacts of various scenarios on future water supply shortfalls. Four scenarios were used, which take into account changing population growth rates and extended dry climates. The study found that the catchment is relatively sensitive to changes in population growth and extended dry climates, and this will alter the water availability significantly, causing a water supply deficit. In response to the projected future water demands, one technique to overcome the unmet demand is by introducing water conservation and demand management (WC/DM) strategies to reduce the water losses and shortfall encountered. By implementing adequate measures, water losses can be reduced, preventing water scarcity and giving decision makers time to provide further solutions to water supply problems

Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines

Stream flow (SF) prediction is considered as a very complex due to the hydrological systems of surface water are complex and dynamic. The reliable prediction of stream flow (SF) can be performed by either conceptual or data-driven based models. In the modelling of hydrological processes, the support vector machine (SVM) is a novel, data-driven approach. Hence, six SVM-based models were generated in this study to predict real time hourly SF in the Selangor River Basin from the water level and rainfall of upstream stations. These models composed of six different combinations of input variables and were trained and tested under hourly records of SF, rainfall, and water level over one year (2011). Among the SVM-based models, SVM-M6, which has nine input variables, was the most effective. Under the training and testing data sets, its correlation coefficient and mean absolute error values were 0.992, 0.953, 0.061 and 0.253 respectively