Evaluation of Machine Learning approach in flood prediction scenarios and its input parameters: A systematic review

Flood disaster is a major disaster that frequently happens globally, it brings serious impacts to lives, property, infrastructure and environment. To stop flooding seems to be difficult but to prevent from serious damages that caused by flood is possible. Thus, implementing flood prediction could help in flood preparation and possibly to reduce the impact of flooding. This study aims to evaluate the existing machine learning (ML) approaches for flood prediction as well as evaluate parameters used for predicting flood, the evaluation is based on the review of previous research articles. In order to achieve the aim, this study is in two-fold; the first part is to identify flood prediction approaches specifically using ML methods and the second part is to identify flood prediction parameters that have been used as input parameters for flood prediction model. The main contribution of this paper is to determine the most recent ML techniques in flood prediction and identify the notable parameters used as model input so that researchers and/or flood managers can refer to the prediction results as the guideline in considering ML method for early flood prediction

Developing machine learning tools for long-lead heavy precipitation prediction with multi-sensor data

A large number of extreme floods were closely related to heavy precipitation which lasted for several days or weeks. Long-lead prediction of extreme precipitation, i.e., prediction of 6-15 days ahead of time, is important for understanding the prognostic forecasting potential of many natural disasters, such as floods. Yet, long-lead flood forecasting is a challenging task due to the cascaded uncertainty with prediction errors from measurements to modeling, which makes the current physics-based numerical simulation models extremely complex and inaccurate. In this paper, we formulate the modeling work as a machine learning problem and introduce a complementary data mining framework for heavy precipitation prediction. Heavy precipitation that may lead to extreme floods is a rare event. Long-lead prediction requires the corresponding feature space to be sampled from extremely high spatio-Temporal dimensions. Such a complexity makes long-lead heavy precipitation prediction a high dimensional and imbalanced machine learning problem. In this work, we firstly define the extreme precipitation and non-extreme precipitation clusters and then design the Nearest-Sample Choosing method to handle the imbalanced data sets. We introduce streaming feature selection and subspace learning to extract the most relevant features from high dimensional data. We evaluate the machine learning tools using historical flood data collected in the State of Iowa, the United States and associated hydrometeorological variables from 1948 to 2010

Developing Machine Learning Tools For Long-Lead Heavy Precipitation Prediction With Multi-Sensor Data

A large number of extreme floods were closely related to heavy precipitation which lasted for several days or weeks. Long-lead prediction of extreme precipitation, i.e., prediction of 6-15 days ahead of time, is important for understanding the prognostic forecasting potential of many natural disasters, such as floods. Yet, long-lead flood forecasting is a challenging task due to the cascaded uncertainty with prediction errors from measurements to modeling, which makes the current physics-based numerical simulation models extremely complex and inaccurate. In this paper, we formulate the modeling work as a machine learning problem and introduce a complementary data mining framework for heavy precipitation prediction. Heavy precipitation that may lead to extreme floods is a rare event. Long-lead prediction requires the corresponding feature space to be sampled from extremely high spatio-Temporal dimensions. Such a complexity makes long-lead heavy precipitation prediction a high dimensional and imbalanced machine learning problem. In this work, we firstly define the extreme precipitation and non-extreme precipitation clusters and then design the Nearest-Sample Choosing method to handle the imbalanced data sets. We introduce streaming feature selection and subspace learning to extract the most relevant features from high dimensional data. We evaluate the machine learning tools using historical flood data collected in the State of Iowa, the United States and associated hydrometeorological variables from 1948 to 2010

Hybrid approach on multi- spatiotemporal data framework towards analysis of long-lead upstream flood: a case of Niger State, Nigeria

Floods have become a global concern because of the vast economic and ecological havoc that ensue. Thus, a flood risk mitigation strategy is used to reduce flood-related consequences by a long-lead identification of its occurrence. A wide range of causative factors, including the adoption of hybrid multi-spatiotemporal data framework is considered in implementing the strategy. Besides the structural or homogenous non-structural factors, the adoption of various Information Systems-based tools are also required to accurately analyse the multiple natural causative factors. Essentially, this was needed to address the inaccurate flood vulnerability classifications and short time of flood prediction. Thus, this study proposes a framework named: Hybrid Multi-spatiotemporal data Framework for Long-lead Upstream Flood Analysis (HyM-SLUFA) to provide a new dimension on flood vulnerability studies by uncovering the influence of multiple factors derived from topography, hydrology, vegetal and precipitation features towards regional flood vulnerability classification and long-lead analysis. In developing the proposed framework, the spatial images were geometrically and radiometrically corrected with the aid of Quantum Geographic Information System (QGIS). The temporal data were cleaned by means of winsorization methods using STATA statistical tool. The hybrid segment of the framework classifies flood vulnerability and performs long-lead analysis. The classification and analysis were conducted using the corrected spatial images to acquire better understanding on the interaction between the extracted features and rainfall in inducing flood as well as producing various regional flood vulnerabilities within the study area. Additionally, with the aid of regression technique, precipitation and water level data were used to perform long-lead flood analysis to provide a foresight of any potential flooding event in order to take proactive measures. As to confirm the reliability and validity of the proposed framework, an accuracy assessment was conducted on the outputs of the data. This study found the influence of various Flood Causative Factors (FCFs) used in the developed HyM-SLUFA framework, by revealing the spatial disparity indicating that the slope of a region shows a more accurate level of flood vulnerability compared to other FCFs, which generally causes severe upstream floods when there is low volume of precipitation within regions of low slope degree. Theoretically, the HyM-SLUFA will serve as a guide that can be adopted or adapted for similar studies. Especially, by considering the trend of precipitation and the pattern of flood vulnerability classifications depicted by various FCFs. These classifications will determine the kind(s) of policies that will be implemented in town planning, and the Flood Inducible Precipitation Volumes can provide a foresight of any potential flooding event in order to take practical proactive measures by the local authority