'We didn't expect it to be this bad': Couple survives Hurricane Ida at home
02:34 - source:CNN
Editor's note:Jesse M. Keenan is an associate professor of real estate in the School of Architecture at Tulane University in New Orleans. Keenan is an internationally recognized expert on climate adaptation and the built environment. He served as Chair of the Committee on Resilience of Building and Infrastructure Systems in American Communities as part of the Obama White House Climate Change Action Initiative. He and his family evacuated their home in New Orleans and are now in Florida. The opinions expressed here are his own. See moreVieww CNN-u.
In the 16 years since Hurricane Katrina, Louisiana and the federaldisaster resiliencethe idea that physical and social infrastructure are integrated and built to rebuild and sustain operations in the event of extreme events. Yes, thank God, one goal, profitableGrantIt was built by the US Army Corps of Engineers after Hurricane Katrina. But after Hurricane Ida, almost every other infrastructure system that disaster recovery advocates promised to integrate failed or performed extremely poorly—frombroken cablecome911 System crash.
Since Hurricane IdaproveThe role of crisis management is limited. weekend in New Orleansnot even enough timeCoordinated evacuation. Implementing off-the-shelf crisis management protocols takes time, as evidenced by Hurricane Idaclimate-induced intensification of hurricanes,There is less and less time to respond.
In practice, disaster resilience is a misconception that leads us to mistrust our ability to withstand extreme weather and climate change.
The concept gained popularity after 9/11, when federal reconstruction efforts aimed to protect America's infrastructure from future attacks. In planning for the next large-scale attack, the government recognized that fault-tolerant systems could indeed fail and focused on the philosophy of designing systems so that, in the event of failure,ReturnAs quickly and economically as possible.lack of energyThis is expected to take several weeks or even months.
When Hurricane Katrina hit,bush administrationenough to believedisaster resilienceHoworganizational philosophyEngage stakeholders in developing a plan to rebuild New Orleans and the Gulf Coast. The flag of resilience flew when the multibillion-dollar rebuilding effort was launched in 2005pick upSponsored by consultants, engineering groups, foundations, and even state and federal agencies who want a piece of the multi-billion dollar pie. SResilience Conference and AwardscomeImmunity prototypes and even regionsthis became the decisive rallying callRecovery after Hurricane Katrina.
During the Obama administration,The principles of flexibility multiply,but only a fractionTechnical expertsLearn how to use many of them. Some policies are effective in helping coordinationFederal, tribal, state and local disaster and climate plannerswhile other related policiesstricter building regulationsThis is wishful thinking.mixed resultsPractices, programs, and statements that never quite deliver on their promises.
During my leadership of national resilience efforts, I have had tremendous success with public, private and civic stakeholders. However, the main challenge is that disaster resilience is a highly technical concept whose policy principles are recovery oriented. Stakeholders in interconnected infrastructure systems, such as mobile telecommunications and electricity distribution, often define resilience in different and sometimes contradictory ways.
Defining and measuring any strategic concept is critical to managing public investments. However, policy makers have had difficulty distinguishing between competing concepts of resilience and as a resultDozens of different rule definitionsFor immunity.
Over time, communities and vulnerable groups began to suffer more and moreresponsibilityFor disaster resilience and preparedness. The good news is that investing in the resilience of communities and local governments has indeed significantly improved their situationCrisis management skillsagainst a range of risks. The bad news is that the scale of the challenges facing communities on the brink of the climate crisis is beyond the capacity of many to deal with alone.
Similarly, these investments in resilience are focused on responding to disasters – not necessarily preventing them or adapting to climate change.
The main problem with disaster resilience is that politicians and bureaucrats use the concept as a way to avoid hard questions about the structural problems that make people vulnerable to natural disasters like hurricanes in the first place: it pointed to a lack of social resilience. No one wants to stand up and ask why poor and historically marginalized communities bear disproportionate environmental risks, or why state and local governments allow people to build and even live in high-risk areas.
Disaster resilience gives politicians an easy way out instead of trying to change the way you do things. Because of this, they have always agreed to further investment in infrastructure, but have never been forced to reject construction in high-risk areas such as the Louisiana coast.
President Biden's slogan reads:restore better' is a direct borrowing of the popular sloganMovement for disasteralthough his governmentResilience is a funding priority,The entire resilience paradigm needs to be rethought, and its administration needs to commit to ensuring that climate change adaptation and disaster risk reduction go hand in hand. This means program managementRelocation of risk areas, giving the federal government higher priority over risky local land use decisions and reformsNational Flood Insurance Programand other policies to better support smart and fair investments in housing and infrastructure.
Hurricane Ida will be remembered as a storm from which many people were unable to recover - let alone rebuild. Some will be relocated, while others will choose to move to other parts of the country that are not threatened with extinction like the Louisiana coast.
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There is a resistance threshold above which you either adapt or fall. as one ofthe poorest city in the country, New Orleans cannot afford to fail. He's barely surviving. We need to transform our cities and infrastructure to adapt to climate change. In some cases this means better rebuilding, but for many it also means building elsewhere. We have to ask ourselves hard questions about where we will rebuild and who has the resources to rebuild.
New Orleans will be reborn as it has many times over the centuries. But we can no longer live in denial with the belief that we will always be immune. If Hurricane Ida marked the end of an era of resilience, it could also mark the beginning of a new era of sustainable management of our built and natural environment.
Loss is the penalty for a bad prediction. That is, loss is a number indicating how bad the model's prediction was on a single example. If the model's prediction is perfect, the loss is zero; otherwise, the loss is greater.What is the adaptive learning rate method? ›
Adaptive learning rate methods are an optimization of gradient descent methods with the goal of minimizing the objective function of a network by using the gradient of the function and the parameters of the network.How can the learning rate be adjusted during training? ›
Learning rate schedules seek to adjust the learning rate during training by reducing the learning rate according to a pre-defined schedule. Common learning rate schedules include time-based decay, step decay and exponential decay.How can we reduce training loss in neural networks? ›
- Hyperparameters are the configuration settings used to tune how the model is trained.
- Derivative of (y - y')2 with respect to the weights and biases tells us how loss changes for a given example.
- So we repeatedly take small steps in the direction that minimizes loss.
What's a loss function? At its core, a loss function is incredibly simple: It's a method of evaluating how well your algorithm models your dataset. If your predictions are totally off, your loss function will output a higher number. If they're pretty good, it'll output a lower number.What is the loss function in training? ›
A loss function is a function that compares the target and predicted output values; measures how well the neural network models the training data. When training, we aim to minimize this loss between the predicted and target outputs.What are examples of adaptive learning? ›
It refers to a type of learning where students are given customized resources and activities to address their unique learning needs. For example, if a student struggles with adding fractions, a teacher might offer 1:1 tutoring or additional practice problems.What are the types of adaptive learning? ›
There are 2 main types of technologies where adaptive learning is being deployed: designed adaptivity and algorithmic adaptivity.What are examples of adaptive teaching strategies? ›
- Rephrasing questions or content.
- Adapting language to ensure all learners understand the content.
- Providing exemplars or WAGOLLs – 'what a good one looks like. ...
- Highlighting and emboldening key learning points.
- Prompting learners with key words, visuals, sound bites or other sensory stimuli.
A learning rate that is too large can cause the model to converge too quickly to a suboptimal solution, whereas a learning rate that is too small can cause the process to get stuck. The challenge of training deep learning neural networks involves carefully selecting the learning rate.
If the learning rate is very large we will skip the optimal solution. If it is too small we will need too many iterations to converge to the best values. So using a good learning rate is crucial.How should we change our learning rate if the training loss is fluctuating? ›
To summarize, if the training accuracy is fluctuating a lot while using deep learning models, you can try addressing the issue by increasing the amount of training data, using regularization techniques, using a more stable optimizer, or tuning the hyper-parameters.What to do if training loss is not decreasing? ›
If the loss value is not decreasing, but it just oscillates, the model might not be learning at all. However, if it's decreasing in the training set but not in the validation set (or it decreases but there's a notable difference), then the model might be overfitting.Why training loss is high? ›
A high loss value usually means the model is producing erroneous output, while a low loss value indicates that there are fewer errors in the model. In addition, the loss is usually calculated using a cost function, which measures the error in different ways.Does training loss always decrease? ›
The scale of the data can make an enormous difference on training. Sometimes, networks simply won't reduce the loss if the data isn't scaled. Other networks will decrease the loss, but only very slowly.What is the most common loss function? ›
The Mean Squared Error (MSE) is the simplest and most common loss function. To calculate the MSE, you take the difference between the actual value and model prediction, square it, and average it across the whole dataset.What does loss mean in neural network? ›
Loss is nothing but a prediction error of Neural Net. And the method to calculate the loss is called Loss Function. In simple words, the Loss is used to calculate the gradients. And gradients are used to update the weights of the Neural Net. This is how a Neural Net is trained.What is the expectation of the loss function? ›
The expected value of the loss is called risk.What is the loss function and minimize it? ›
At its core, a loss function is a measure of how good your prediction model does in terms of being able to predict the expected outcome(or value). We convert the learning problem into an optimization problem, define a loss function and then optimize the algorithm to minimize the loss function.What is the difference between loss function training and validation? ›
The training loss indicates how well the model is fitting the training data, while the validation loss indicates how well the model fits new data. Another common practice is to have multiple metrics in the same chart as well as those metrics for different models.
A custom loss function can be created by defining a function that takes the true values and predicted values as required parameters. The function should return an array of losses. The function can then be passed at the compile stage.Why do we need adaptive learning? ›
Adaptive learning technology can help identify when they are struggling and provide additional resources to help them work through it on their own. Adaptive learning can help students get a better idea on when they are ready to move on.What are 4 examples of adaptive behavior? ›
Adaptive behaviors include life skills such as grooming, dressing, safety, food handling, working, money management, cleaning, making friends, social skills, and the personal responsibility expected of their age, social group and wealth group.What are three adaptive behaviors? ›
Adaptive behaviors include real-life skills such as grooming, getting dressed, avoiding danger, safe food handling, following school rules, managing money, cleaning, and making friends. Adaptive behavior also includes the ability to work, practice social skills, and take personal responsibility.What are adaptive skills for learners with disabilities? ›
These skills include interpersonal social communication, empathy, ability to relate to peers as friends, social problem-solving, social responsibility, and self-esteem. Gullibility, the ability to follow rules, and avoiding victimization may also be included.Is adaptive learning effective? ›
There are many benefits of this style of learning to students, teachers, and administrators, including: More effective learning. Every student learns differently and at different paces. Adaptive learning helps to nurture each students' unique needs for a more effective learning environment.What are adaptive learning principles? ›
Adaptive learning technologies provide an environment that can intelligently adjust to individual learner needs by presenting appropriate information, instructional materials, scaffolds, feedback, and recom- mendations based on learner characteristics and particular situation.What is adaptive on IEP? ›
For school age children, the IEPs will usually address both intellectual and adaptive functioning. Adaptive functioning refers to a set of skills needed for daily living. Three broad sets of skills make up adaptive functioning. These are conceptual skills, social skills, and practical life skills.What is an adaptive challenge in education? ›
The essence of adaptive challenges can be captured in this sentence of Heifetz, Grashow, and Linsky's book: “Adaptive challenges are typically grounded in the complexity of values, beliefs, and loyalties rather than technical complexity and stir up intense emotions rather than dispassionate analysis,” (Loc. 1283).How does adaptive learning help students? ›
Adaptive learning may enable students to become more successful and self-directed by providing insight into their level of mastery and allowing them to work at their own pace. It potentially improves student engagement by providing lessons and activities that are tailored to their needs.
|Gives better results than simple SGD if we have both sparse and dense features||Less efficient than some other optimization algorithms like AdaDelta and Adam|
There's a Goldilocks learning rate for every regression problem. The Goldilocks value is related to how flat the loss function is. If you know the gradient of the loss function is small then you can safely try a larger learning rate, which compensates for the small gradient and results in a larger step size.What is the best learning rate? ›
The learning rate is the most important neural network hyperparameter. It can decide many things when training the network. In most optimizers in Keras, the default learning rate value is 0.001. It is the recommended value for getting started with training.How do I stop Overfitting? ›
- Early stopping. Early stopping pauses the training phase before the machine learning model learns the noise in the data. ...
- Pruning. You might identify several features or parameters that impact the final prediction when you build a model. ...
- Regularization. ...
- Ensembling. ...
- Data augmentation.
No, there is a catch here: the lesser is better but not the lowest since, the lower learning rate affects the time and accuracy of the model.What is negative learning rate? ›
If η is a negative value, you are moving away from the minimum instead. It is moving to reverse what the gradient descent does and makes even the non-learning of the neural network.Why is my training loss lower than my validation loss? ›
This is because as the network learns the data, it also shrinks the regularization loss (model weights), leading to a minor difference between validation and train loss. However, the model is still more accurate on the training set.How much validation loss is acceptable? ›
Typically validation loss should be similar to but slightly higher than training loss. As long as validation loss is lower than or even equal to training loss one should keep doing more training.How do you reduce validation loss? ›
Solutions to this are to decrease your network size, or to increase dropout. For example you could try dropout of 0.5 and so on. If your training/validation loss are about equal then your model is underfitting. Increase the size of your model (either number of layers or the raw number of neurons per layer)Why is training loss negative? ›
The loss is just a scalar that you are trying to minimize. It's not supposed to be positive. One of the reason you are getting negative values in loss is because the training_loss in RandomForestGraphs is implemented using cross entropy loss or negative log liklihood as per the reference code here.
A dropout of 0.1-0.3 is pretty typical but a reasonable amount should be ok. Shuffle and randomly split the train and validation data. If the model recognizes some pattern that's in the training data, but not in the validation, this would also cause some overfitting.What does it mean when training loss increases? ›
With higher learning rates you are moving too much in the direction opposite to the gradient and may move away from the local minima which can increase the loss.Can training loss be negative? ›
there's nothing wrong with having a negative-loss function as such. It's just a function that has to be minimized, and your weights are updated according to that.What is the difference between training loss and training error? ›
The training loss is calculated over the entire training dataset. Train Error involves the human interpretable metric of your model's performance. Normally it means what percentage of training examples the model got incorrect.Why is my loss decreasing but accuracy not increasing? ›
This means you are overfitting (training loss diminished but no improvement in validation loss/accuracy) so you should try using any technique that helps reduce overfitting: weight decay, more dropout, data augmentation (if applicable)…Why is my validation loss increasing? ›
So, if validation loss is moving up, it means that the model is indicating that it is becoming a function which better represents patterns in the training data and not patterns in the validation data. Hence the overfitting.What is accuracy and loss in deep learning? ›
By definition, Accuracy score is the number of correct predictions obtained. Loss values are the values indicating the difference from the desired target state(s).What is loss and cost function in deep learning? ›
The terms cost function & loss function are analogous. Loss function: Used when we refer to the error for a single training example. Cost function: Used to refer to an average of the loss functions over an entire training dataset.What does loss mean in classification? ›
In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price paid for inaccuracy of predictions in classification problems (problems of identifying which category a particular observation belongs to).What is the difference between loss and cost in deep learning? ›
There is no major difference. In other words, the loss function is to capture the difference between the actual and predicted values for a single record whereas cost functions aggregate the difference for the entire training dataset. The Most commonly used loss functions are Mean-squared error and Hinge loss.
On the contrary, validation loss is a metric used to assess the performance of a deep learning model on the validation set. The validation set is a portion of the dataset set aside to validate the performance of the model.What are the different types of loss functions? ›
- Mean Square Error / Quadratic Loss / L2 Loss. We define MSE loss function as the average of squared differences between the actual and the predicted value. ...
- Mean Absolute Error / L1 Loss. ...
- Huber Loss / Smooth Mean Absolute Error. ...
- Log-Cosh Loss. ...
- Quantile Loss.
Loss functions provide more than just a static representation of how your model is performing–they're how your algorithms fit data in the first place. Most machine learning algorithms use some sort of loss function in the process of optimization or finding the best parameters (weights) for your data.Which loss function is typically used in deep learning? ›
Cross-entropy and mean squared error are the two main types of loss functions to use when training neural network models.What loss function will you optimize and why? ›
In machine learning, a loss function and an optimizer are two essential components that help to improve the performance of a model. A loss function measures the difference between the predicted output of a model and the actual output, while an optimizer adjusts the model's parameters to minimize the loss function.What are the two types of loss? ›
The consignor has to bear the burden of the loss. There are two types of losses in consignment: Normal Loss And Abnormal Loss.What is loss examples? ›
Different Kinds of Loss
Loss of a close friend. Death of a partner. Death of a classmate or colleague. Serious illness of a loved one. Relationship breakup.
A loss is calculated by subtracting total revenue from total expenses.What is the difference between loss and validation loss? ›
One of the most widely used metrics combinations is training loss + validation loss over time. The training loss indicates how well the model is fitting the training data, while the validation loss indicates how well the model fits new data.Can loss function be negative? ›
Yes, it is perfectly fine to use a loss that can become negative.
An error function measures the deviation of an observable value from a prediction, whereas a loss function operates on the error to quantify the negative consequence of an error.