Wetland Water-Level Prediction in the Context of Machine-Learning Techniques: Where Do We Stand?

Wetlands are simply areas that are fully or partially saturated with water. Not much attention has been given to wetlands in the past, due to the unawareness of their value to the general public. However, wetlands have numerous hydrological, ecological, and social values. They play an important role in interactions among soil, water, plants, and animals. The rich biodiversity in the vicinity of wetlands makes them invaluable. Therefore, the conservation of wetlands is highly important in today’s world. Many anthropogenic activities damage wetlands. Climate change has adversely impacted wetlands and their biodiversity. The shrinking of wetland areas and reducing wetland water levels can therefore be frequently seen. However, the opposite can be seen during stormy seasons. Since wetlands have permissible water levels, the prediction of wetland water levels is important. Flooding and many other severe environmental damage can happen when these water levels are exceeded. Therefore, the prediction of wetland water level is an important task to identify potential environmental damage. However, the monitoring of water levels in wetlands all over the world has been limited due to many difficulties. A Scopus-based search and a bibliometric analysis showcased the limited research work that has been carried out in the prediction of wetland water level using machine-learning techniques. Therefore, there is a clear need to assess what is available in the literature and then present it in a comprehensive review. Therefore, this review paper focuses on the state of the art of water-level prediction techniques of wetlands using machine-learning techniques. Nonlinear climatic parameters such as precipitation, evaporation, and inflows are some of the main factors deciding water levels; therefore, identifying the relationships between these parameters is complex. Therefore, machine-learning techniques are widely used to present nonlinear relationships and to predict water levels. The state-of-the-art literature summarizes that artificial neural networks (ANNs) are some of the most effective tools in wetland water-level prediction. This review can be effectively used in any future research work on wetland water-level prediction.publishedVersio

Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

The community’s well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and aquatic ecosystems. Thus, these considerations have made the water level monitoring process essential to save the environment. Machine learning hybrid models are emerging robust tools that are successfully applied for water level monitoring. Various models have been developed, and selecting the optimal model would be a lengthy procedure. A timely, detailed, and instructive overview of the models’ concepts and historical uses would be beneficial in preventing researchers from overlooking models’ potential selection and saving significant time on the problem. Thus, recent research on water level prediction using hybrid machines is reviewed in this article to present the “state of the art” on the subject and provide some suggestions on research methodologies and models. This comprehensive study classifies hybrid models into four types algorithm parameter optimisation-based hybrid models (OBH), pre-processing-based hybrid models (PBH), the components combination-based hybrid models (CBH), and hybridisation of parameter optimisation-based with preprocessing-based hybrid models (HOPH); furthermore, it explains the pre-processing of data in detail. Finally, the most popular optimisation methods and future perspectives and conclusions have been discussed

Model identification and accuracy for estimation of suspended sediment load

In the present study, three widely used modeling approaches: (1) sediment rating curve (SRC) and optimized OSRC, (2) machine learning models (ML) (random forest (RF) and Dagging-RF (DA-RF)) and (3) the semi-physically based soil and water assessment tool (SWAT) are applied to predict suspended sediment load (Qs) at the Talar watershed in Iran. Various graphical and quantitative methods were used to evaluate the goodness of fit. Results indicated that the RF model had the best prediction power in the training phase, while the dagging-RF hybrid algorithm outperformed all other models in the validation phase. The OSRC, RF and DA-RF had ‘very good’ performances based on the NSE in the validation phase, SRC showed ‘good’ performance, while the predicted values using SWAT were ‘satisfactory’. Our results suggest that the OSRC and ML models are more suitable for prediction of Qs in study catchments with poor data availability.</p

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

Probabilistic and artificial intelligence modelling of drought and agricultural crop yield in Pakistan

Pakistan is a drought-prone, agricultural nation with hydro-meteorological imbalances that increase the scarcity of water resources, thus, constraining water availability and leading major risks to the agricultural productivity sector and food security. Rainfall and drought are imperative matters of consideration, both for hydrological and agricultural applications. The aim of this doctoral thesis is to advance new knowledge in designing hybridized probabilistic and artificial intelligence forecasts models for rainfall, drought and crop yield within the agricultural hubs in Pakistan. The choice of these study regions is a strategic decision, to focus on precision agriculture given the importance of rainfall and drought events on agricultural crops in socioeconomic activities of Pakistan. The outcomes of this PhD contribute to efficient modelling of seasonal rainfall, drought and crop yield to assist farmers and other stakeholders to promote more strategic decisions for better management of climate risk for agriculturalreliant nations

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

Global Ensemble Streamflow and Flood Modeling with Application of Large Data Analytics, Deep learning and GIS

ABSTRACTFlooding is one of the most dangerous natural disasters that repeatedly occur globally, and flooding frequently leads to major urban, financial, anthropogenic, and environmental impacts in the subjected area. Therefore, developing flood susceptibility maps to identify flood zones in the catchment is necessary for improved flood management and decision making. Streamflow and flood forecasting can provide important information for various applications including optimization of water resource allocations, water quality assessment, cost analysis, sustainable design of hydrological infrastructures, improvement in agriculture and irrigation practices. Compared to conventional or physically based hydrological modeling, which needs a large amount of historical data and parameters, the recent data-driven models which require limited amounts of data, have received growing attention among researchers due to their high predictive performance. This makes them more appropriate for hydrological forecasting in basin-scale and data-scarce regions. In this context, the main objective of this study was to evaluate the performance of various data-driven modeling approaches in flood and streamflow forecasting. One of the significant desires in daily streamflow prediction in today’s world is recognizing possible indicators and improving their applicability for effective water management strategies. In this context, the authors proposed an ensemble data mining algorithm coupled with various machine learning methods to perform data cleaning, dimensionality reduction, and feature subset selection. To perform the task of data mining, three data cleaning approaches: Principle Component Analysis (PCA), Tensor Flow (TF) and Tensor Flow K-means clustering(TF-k-means clustering) have been used. For the feature selection, four different machine learning approaches including K Nearest Neighbor (KNN), Bootstrap aggregating, Random Forest (RF) and Support Vector Machin (SVM) have been investigated. Out of twelve different combinations of data mining and machine learning, the best ensemble model was TF-k-means clustering coupled with RF, which outperformed the other methods with 96.52% classification accuracy. Thereafter, a modified Nonlinear Echo State Networks Multivariate Polynomial (NESN-MP) named in the current study as Robust Nonlinear Echo State Network (RNESN) was utilized for daily streamflow forecasting. The RNESN decreases the size of the reservoir (hidden layer which performs random weigh initialization), reduces the computational burden compared with NESN-MP, and increases the interactions between the internal states. The model is thus simple and user-friendly with better learning ability and more accurate forecasting performance. The method has been tested with data provided by the United States Geological Survey (USGS), Natural Resource Conservation Service (NRCS), National Weather Service Climate Prediction Center (NOAA) and Daymet Data Set from NASA through the Earth Science Data and Information System (ESDIS). Each data set includes the daily records of the local observed hydrological and large-scale weather/climate variability parameters. The efficiency of the proposed method has been evaluated in three regions namely Berkshire County (MA), Tuolumne County (CA), and Wasco County (OR). These basins were designated based upon the wide range of climatic conditions across the US that they represent. The simulation results were compared with NESN-MP and Adaptive Neuro-Fuzzy Inference System (ANFIS). The results validate the superiority of the proposed modeling approach compared to NESN-MP and ANFIS. The proposed RNESN approaches outperform the other methods with an RMSE = 0.98. For flood forecasting, an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, has been proposed to prepare the flood susceptibility map. In in this study, we proposed a new ensemble of models of Bootstrap aggregating as a Meta classifier based upon the K-Nearest Neighbor (KNN) functions including coarse, cosine, cubic and weighted as base classifiers to perform spatial prediction of the flood. We first selected 10 conditioning factors to spatial prediction of floods and then their prediction capability using the relief-F attribute evaluation (RFAE) method was assessed. Model validation was performed using two statistical error-indexes and the area under the curve (AUC). Results concluded that the Bootstrap aggregating -cubic KNN ensemble model outperformed the other ensemble models. Therefore, the Bootstrap aggregating -cubic KNN model can be used as a promising technique for the sustainable management of flood-prone areas. Furthermore, the AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%. The results showed that the EBF model had the highest accuracy in predicting the flood susceptibility map, in which 14% of the total areas were located in high and very high susceptibility classes and 62% were located in low and very low susceptibility classes

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

The Applications of Soft Computing Methods for Seepage Modeling: A Review

In recent times, significant research has been carried out into developing and applying soft computing techniques for modeling hydro-climatic processes such as seepage modeling. It is necessary to properly model seepage, which creates groundwater sources, to ensure adequate management of scarce water resources. On the other hand, excessive seepage can threaten the stability of earthfill dams and infrastructures. Furthermore, it could result in severe soil erosion and consequently cause environmental damage. Considering the complex and nonlinear nature of the seepage process, employing soft computing techniques, especially applying pre-post processing techniques as hybrid methods, such as wavelet analysis, could be appropriate to enhance modeling efficiency. This review paper summarizes standard soft computing techniques and reviews their seepage modeling and simulation applications in the last two decades. Accordingly, 48 research papers from 2002 to 2021 were reviewed. According to the reviewed papers, it could be understood that regardless of some limitations, soft computing techniques could simulate the seepage successfully either through groundwater or earthfill dam and hydraulic structures. Moreover, some suggestions for future research are presented. This review was conducted employing preferred reporting items for systematic reviews and meta-analyses (PRISMA) method