Predicting floods by machine learning (ML) is crucial for disaster management and preparations. Flood detection includes accessing different parameters for examining the chances of possible flooding activities. The following are the step-by-step guide which we implement for constructing a flood detection machine using ML.

  1. Objective Definition

      We define the main aim of our project is to design a ML framework that detects the chances of flooding depends on the previous data and ecological reactions.

  1. Data Collection
  • Historical Data: Aggregating data on previous flooding activities includes date, duration, severity and damaged areas and others which assist us.
  • Environmental Data: This involves rainfall data, river & stream water levels, soil moisture content, weather detections and other similar data that we make use of it.
  1. Pre-processing of Data
  • Data Cleaning: To maintain missing values, errors and noisy data we do this process.
  • Feature Engineering: For presentation of data we derive latest features and convert old ones to better version. We collecting routine rainfall data into weekly and monthly totals.
  • Normalization & Standardization: When we utilize methods like SVM and neural Networks which are susceptible feature measure, we evolve normalization and standardization features to bring them in relevant scale.
  1. Framework choosing and Development
  • We implement Regression models like Linear Regression, Decision Trees, and Random Forest are suitable when we detect constant results like water levels.
  • When we categorically forecast occurrence of flood, the classification models such as Logistic Regression, SVM, or Neural Networks, are applicable for our work.
  1. Training the Model
  • Divide the Data: Partitioning our dataset into training, evaluation and validate sets.
  • Training: For instructing our model we use the training dataset and evaluate its performance on test set. We also track metrics such as RMSE for reverse tasks and classification tasks done by accuracy, precision and recall.
  1. Model Evaluation
  • To validate our framework’s efficiency we employ the test dataset.
  • When the data is time-sequences we analyze time-based cross-validation.
  1. Optimization & Hyperparameter Tuning (optional)
  • We adapt model hyperparameters that depends on test outcomes.
  • For systematic hyperparameter tuning we utilize methods such as grid and random search.
  1. Deployment
  • By applying our predictive model on a server, cloud platforms and collaborate it with tracking mechanisms, we activate alerts based on the framework’s detection.
  1. User Interface (if suitable)
  • We build a dashboard and UI that shows flood forecasting, danger level and provides suggestions for preventions.
  • Implementing the geospatial visualizations displays us the locations which are at high threat of flooding.
  1. Conclusion & Future Improvements
  • Overviewing our project results, limitations occurred and the system’s effect.
  • Possible enhancements involves:
  • We combining the real-world date sources for up-to-the-minute detections.
  • To increase the accuracy we integrate with remote sensing data like satellite imagery.
  • Enlarging our scope to detect other metrics like duration of flood and damaged places.

Tips:

  • Forecasting floods becomes usable for us when we add Geographic Information System (GIS) data to analyse the factors such as local infrastructure and terrain.
  • When we get more data, then we update our model periodically.
  • We interact with domain professionals in hydrology and environmental science to get better interpretation and understand the data and outcomes.

     Our powerful flood detection mechanism is beneficial in early warning, allowing officials and groups to take necessary measures, possibility of saving our people lives and decreasing the property damage.

Flood Prediction Using Machine Learning Ideas

Flood Prediction Using Machine Learning Thesis Ideas

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  1. Flood Prediction Using Ensemble Machine Learning Model

Keywords:

Flood Prediction, Ensemble Machine Learning, Rainfall, Support Vector Classifier(SVC), K-Nearest Neighbor(KNN), Decision Tree Classifier(DTC), Binary Logistic Regression, Stacked Generalization

            We concentrate on the comparative analysis of different machine learning methods to predict the flood in India. We estimate the methods like KNN, Support Vector Classifier, Decision Tree classifier, Binary Logistic Regression and stacked generalization. We utilize the rainfall dataset to test and train the model. Our stacked generalization methods performs best accuracy rate.

  1. A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction

Keywords:

Flood susceptibility, Machine learning algorithm, Uncertainty analysis, GIS, Remote sensing            Our paper offer an outline to decrease spatial disagreement between four separate and hybridized ML based FMS’s namely RF, KNN, MLP and hybridized genetic algorithm-Gaussian radial basis function- support vector regression (GA-RBF-SVR). The optimized models were developed by merging the outcome of our four methods. Our optimized model exhibit increased accuracy and enhanced spatial agreement by decreasing the number of classification faults.  

  1. An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and Deep Learning Algorithms with Advanced Alert System

Keywords:

flood forecasting and prediction; multilayer perceptron (MLP); time series analysis; deep learning (DL); artificial neural network (ANN); decision tree (DT); recurrent neural network (RNN); exponential smoothing

            The goal of our paper is to construct a prediction method based on the exponential smoothing LSTM (ES-LSTM) and RNN to predict hourly rainfall seasons and categorize the rainfall using a methods ANN and DT. We can work with the Historical daily weather dataset to test the efficacy of our suggested method. Our result displays that ES-LSTM and ANN performs best when compare to other models. 

  1. Flood susceptible prediction through the use of geospatial variables and machine learning methods

Keywords:

Flooding, Flood susceptibility mapping, Natural hazards

            Our paper uses six ML methods like DT, RF, MLP-NN, AdaBoost, LR and SVM. We used several metrics to estimate our models performance. We can reclassify the flood conditioning factors to estimate them by utilizing Frequency Ratio method. The flood susceptibility method can be provided by utilizing the RF method specifies that the flood susceptibility is high, moderate or low. 

  1. Predicting and analyzing food susceptibility using boosting‑based ensemble machine learning algorithms with SHapley Additive exPlanations

Keywords:

NGBoost, LightGBM, CatBoost, SHapley Additive exPlanations

            Our paper improve FMSs Adana province on the Mediterranean coast of Türkiye by utilizing tree based ML classifiers. We used the methods like NGBoost for the first time in FSM study as well as comparing LightGBM and CatBoost versus other methods like RF, GB, XGBoost and AdaBoost. Our AdaBoost and LightGBM method gives the best accuracy. Hence we used ML-based FSMs with the aid of an XAI method i.e. SHAP.

  1. Prediction of flood routing results in the Central Anatolian region of Türkiye with various machine learning models

Keywords:

Flood routing, Flood management, Gradient-boosted machine, Central Anatolian

            We aim to compare the presentation of different ML methods like Bagged Tree, Extreme Gradient Boosting, Gradient Boosting Machine, RF, KNN and SVM to predict the flood routing models. In addition predicting the success of tree based method recognized by comparing optimized and default parameters. Our gradient boosted machine is the successful method to estimate flood routing.

  1. Flood susceptibility prediction using tree-based machine learning models in the GBA

Keywords:

Tree-based machine learning, Flood susceptibility, SHAP values

            We establish an outline for estimating flood susceptibility in GBA by utilizing tree-based ML and geographical information system method. We utilize the tree based methods like RF, Gradient Boost Tree, Extreme Gradient Boosting and Categorical boosting to train and test the ML methods and the trained method can be utilized to predict susceptibility. The model Shapely additive explanation values have the robust effect on flood susceptibility.

  1. Spatial prediction of flash flood susceptible areas using novel ensemble of bivariate statistics and machine learning techniques for ungauged region

Keywords:

Flash flood susceptibility modelling, Ungauged region, Bivariate statistical model, Multivariate statistical model

            The goal of our study is to include novel ensemble method to bivariate statistical methods like ANN, SVM and KNN model. We have established flash flood and geospatial dataset. A geospatial dataset can be generated with aspect, plan curvature, NDVI, Slope, SPI, TWI, LULC and STI in GIS. Every influence factor based on correlation utilizing WOE in E-open source software that can ensemble with ANN, SVM and KNN. Finally our WOE-AN performs best when compare to other methods.

  1. Real-Time Flood Prediction System Using Machine Learning Algorithms

Keywords:

Flood prediction system, K Neighbours classifier, Logistic regression, Random forest classifier

            Our paper utilizes six ML methods like ANN, K Neighbors classifier, LR, Support Vector Classifier, DT and Random Forest Classifier all are executed separately to estimate and contrast the performance when predicting the flood possibility. Our Random Forest method gives the best performance accuracy.

  1. MLFP: Machine Learning Approaches for Flood Prediction in Odisha State

Keywords:

Flood, Naïve bayes, Accuracy

            Our paper utilizes the rainfall data that can fed into various machine learning methods. Before we do this process first we clean and pre-process the data. Then the dataset for cleaning is divided into train set and test set. Then we have to compare the accuracy of the method to estimate and evaluate. Then at last we choose the best model by comparing the accuracy.

Important Research Topics