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"River engineering"
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Fault lines
Earth's fractured geology is visible in its fault lines. It is along these lines that earthquakes occur, sometimes with disastrous effects. These disturbances can significantly influence urban development, as seen in the aftermath of two earthquakes in Messina, Italy, in 1908 and in the Belice Valley, Sicily, in 1968. Following the history of these places before and after their destruction, this book explores plans and developments that preceded the disasters and the urbanism that emerged from the ruins. These stories explore fault lines between \"rural\" and \"urban,\" \"backwardness\" and \"development,\" and \"before\" and \"after,\" shedding light on the role of environmental forces in the history of human habitats.
eBook
Breaching the peace : the Site C Dam and a valley's stand against big hydro
\"Breaching the Peace tells the story of the ordinary citizens who stood up to the most expensive megaproject in BC history and the government-sanctioned bullying that propelled it forward. Starting in 2013, journalist Sarah Cox travelled to the Peace River Valley to talk to locals about the Site C dam and BC Hydro's claim that the clean energy project was urgently needed. She discovered farmers, First Nations, and scientists caught up in a modern-day David and Goliath battle to save the valley, their farms, and traditional lands from wholesale destruction. Told in frank and moving prose, their stories stand as a much-needed cautionary tale at a time when concerns about global warming have helped justify a renaissance of environmentally irresponsible hydro megaprojects around the world.\"--Provided by publisher.
Book
The Feasibility of Integrative Radial Basis M5Tree Predictive Model for River Suspended Sediment Load Simulation
Accurate suspended sediment transport prediction is highly significant for multiple river engineering sustainability. Conceptually evidenced, sediment load transport is highly stochastic, spatial distributed and redundant pattern due to the incorporation of various hydrological and morphological variables such as river flow discharge and sediment physical properties. The motivation of this study is to explore the feasibility of newly intelligent model called Radial basis M5 model tree (RM5Tree) for suspended sediment load (St) prediction for daily scale information at Trenton hydrological station, Delaware River. Numerous input combination attributes are formulated based on the preceding information of sediment and river flow discharge. The prediction accuracy “based statistical and graphical visualizations” of the proposed model validated against numerous well-established predictive models including response surface method (RSM), artificial neural network (ANN) and classical M5Tree based models. The investigated input combinations behaved differently from one case to another. The optimum input combination attributes are included two months lead times of sediment and discharge information to predict one step ahead St. The attained results of the proposed RM5Tree model exhibited a remarkable prediction accuracy with minimal values of root mean square error (RMSE≈2091 ton/day) and coefficient of determination (R2≈0.86). This presenting a percentage of enhancement in the prediction accuracies by (51.6, 53.1 and 26.3) over (RSM, ANN and M5Tree) optimal models over the testing phase.
Journal Article
River control in India : spatial, governmental and subjective dimensions
Using India as a case study, this book identifies the spatial aspects of norms through which concepts of the ideal river and the deficient river in need of control are created. Examines different subjective stances arising from the same body of expertise.
eBook
Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data
Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome of population growth and industrialization. Bearing in mind the importance of GW potential assessment in reaching sustainability, this study seeks to use remote sensing (RS)-derived driving factors as an input of the advanced machine learning algorithms (MLAs), comprising deep boosting and logistic model trees to evaluate their efficiency. To do so, their results are compared with three benchmark MLAs such as boosted regression trees, k-nearest neighbors, and random forest. For this purpose, we firstly assembled different topographical, hydrological, RS-based, and lithological driving factors such as altitude, slope degree, aspect, slope length, plan curvature, profile curvature, relative slope position, distance from rivers, river density, topographic wetness index, land use/land cover (LULC), normalized difference vegetation index (NDVI), distance from lineament, lineament density, and lithology. The GW spring indicator was divided into two classes for training (434 springs) and validation (186 springs) with a proportion of 70:30. The training dataset of the springs accompanied by the driving factors were incorporated into the MLAs and the outputs were validated by different indices such as accuracy, kappa, receiver operating characteristics (ROC) curve, specificity, and sensitivity. Based upon the area under the ROC curve, the logistic model tree (87.813%) generated similar performance to deep boosting (87.807%), followed by boosted regression trees (87.397%), random forest (86.466%), and k-nearest neighbors (76.708%) MLAs. The findings confirm the great performance of the logistic model tree and deep boosting algorithms in modelling GW potential. Thus, their application can be suggested for other areas to obtain an insight about GW-related barriers toward sustainability. Further, the outcome based on the logistic model tree algorithm depicts the high impact of the RS-based factor, such as NDVI with 100 relative influence, as well as high influence of the distance from river, altitude, and RSP variables with 46.07, 43.47, and 37.20 relative influence, respectively, on GW potential.
Journal Article
