A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation

In regions with lack of hydrological and hydraulic data, a spatial flood modeling and mapping is an opportunity for the urban authorities to predict the spatial distribution and the intensity of the flooding. It helps decision-makers to develop effective flood prevention and management plans. In this study, flood inventory data were prepared based on the historical and field surveys data by Sari municipality and regional water company of Mazandaran, Iran. The collected flood data accompanied with different variables (digital elevation model and slope have been considered as topographic variables, land use/land cover, precipitation, curve number, distance to river, distance to channel and depth to groundwater as environmental variables) were applied to novel hybridized model based on neural network and swarm intelligence-grey wolf algorithm (NN-SGW) to map flood-inundation. Several confusion matrix criteria were used for accuracy evaluation by cutoff-dependent and independent metrics (e.g., efficiency (E), positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic curve (AUC)). The accuracy of the flood inundation map produced by the NN-SGW model was compared with that of maps produced by four state-of-the-art benchmark models: random forest (RF), logistic model tree (LMT), classification and regression trees (CART), and J48 decision tree (J48DT). The NN-SGW model outperformed all benchmark models in both training (E = 90.5%, PPV = 93.7%, NPV = 87.3%, AUC = 96.3%) and validation (E = 79.4%, PPV = 85.3%, NPV = 73.5%, AUC = 88.2%). As the NN-SGW model produced the most accurate flood-inundation map, it can be employed for robust flood contingency planning. Based on the obtained results from NN-SGW model, distance from channel, distance from river, and depth to groundwater were identified as the most important variables for spatial prediction of urban flood inundation. This work can serve as a basis for future studies seeking to predict flood susceptibility in urban areas using hybridized machine learning (ML) models and can also be applied in other urban areas where flood inundation presents a pressing challenge, and there are some problems regarding required model and availability of input data

Assessing the impacts of dam construction on river morphology by applying a new automated method on remote sensing images

Spatiotemporal morphological impacts of river regulation owing to the construction of hydraulic structures on Kor River, Fars Province, Iran has been quantified using Remote Sensing images during 1993–2017. The river morphology has been studied before and after the construction of the Mollasadra Dam in 2006. MATLAB programming was utilized to extract the waterline in order to reduce the errors derived from manual extraction of the river path. Several characteristics of river morphology, such as the temporal thalweg movements and spatiotemporal Sinuosity Index (SI) have been calculated. Through this work, the Absolute and Rate of Thalweg Movement (ATM and RTM), and spatial movement of meander centroids were proposed as new indices to show morphological changes in the river. The results indicate that thalweg has moved towards to the southwest by an average movement of 40 cm, to the northeast by 20 cm and to the southwest by an average of 40 cm per year during 1993–2003 (pre-impact), 2003–2011, and 2011–2017 (post-impact), respectively. In spatial scale, changes in the morphology of the river is increased from upstream to downstream and this was particularly evident in the last 10% of the river length. The results of SI values show that despite a 5% mutation in the straight class of sinuosity in the pre-impact period, there is a decrease of 18 % in the same class during the post-impact period and river tends to meander after the construction of Mollasadra Dam. Considering the spatial movement of meander centroid, temporal change in major meanders was assessed

Past, present, and future of river flow regime in Nordic region focusing on river ice break-up events

Abstract This thesis aims to enhance the current body of research on arctic flow regimes by conducting a thorough analysis of the long-term fluctuations in cold hydrologic extremes. The study will specifically concentrate on examining the inter-annual variability in low and high flows, ice processes, and overall river dynamics, including morphological parameters. It provides new perspectives on changes in river hydrology, specifically snow-melt patterns and their relationship to streamflow hydrology and aims to assess the long-term effects of extreme flow events, including both high and low flows, across different seasons. Furthermore, the research has yielded valuable insights pertaining to the determination of ice break-up dates by means of analyzing hydrographs. This contribution enhances our comprehension of river ice dynamics and its impact on climate change. One of the goals of the RiTiCE was to identify the break-up date (BUD) of ice by analyzing daily hydrographs. Moreover, the study has developed indices with the intention of analyzing the exceptional values, patterns, and changes in flow attributes over a period. The examination of BUD and Temperature Transition Point (TTP) at each station yields insights into the interplay between temperature dynamics and river ice behavior. The timing offset that has been observed serves to emphasize the connection between these variables and emphasizes the necessity for additional research into this captivating interaction. The investigation has revealed a notable degree of consistency in trends observed across various indices, indicating that modifications in river flow patterns can be used as reliable indicators of shifts in climate patterns. In contrast to the prevailing consensus, it is uncommon for floods and droughts to occur simultaneously in the majority of rivers, suggesting a lack of uniformity in the modification of extreme hydrological events. The results validate the substantial influence of climate change and global warming on Arctic hydrology, specifically in relation to the decreasing trends observed in annual maximum flow.Tiivistelmä Tämän väitöskirjan tavoitteena on täydentää nykyistä arktisia virtausjärjestelmiä koskevaa tutkimusta analysoimalla perusteellisesti kylmien hydrologisten ääriarvojen pitkän aikavälin vaihteluita. Tutkimuksessa keskitytään erityisesti tarkastelemaan pienten ja suurten virtaamien, jääprosessien ja yleisen jokidynamiikan, myös morfologisten parametrien, vuotuista vaihtelua. Tutkimus tarjoaa uusia näkökulmia jokihydrologian muutoksiin, erityisesti lumen sulamismalleihin ja niiden suhteeseen virtaamahydrologiaan, ja sen tavoitteena on arvioida äärimmäisten virtaamatapahtumien, mukaan lukien suurten ja pienten virtaamien, pitkän aikavälin vaikutuksia eri vuodenaikoina. Lisäksi tutkimus on tuottanut arvokasta tietoa jään hajoamisajankohtien määrittämisestä hydrografioiden analysoinnin avulla. Tämä tutkimus parantaa käsitystämme jokijään dynamiikasta ja sen vaikutuksesta ilmastonmuutokseen. Yksi RiTiCE-tutkimuksen tavoitteista oli määrittää jään hajoamisajankohta (BUD) analysoimalla päivittäisiä hydrografioita. Lisäksi tutkimuksessa on kehitetty indeksejä, joiden tarkoituksena on analysoida poikkeuksellisia arvoja, malleja ja muutoksia virtausominaisuuksissa tietyn ajanjakson aikana. BUD:n ja lämpötilan siirtymispisteen (TTP) tarkastelu kullakin asemalla antaa tietoa lämpötiladynamiikan ja jokijään käyttäytymisen välisestä vuorovaikutuksesta. Havaittu ajallinen siirtymä korostaa näiden muuttujien välistä yhteyttä ja korostaa tarvetta lisätutkimuksiin tästä kiehtovasta vuorovaikutuksesta. Tutkimus on osoittanut, että eri indekseissä havaitut suuntaukset ovat huomattavan johdonmukaisia, mikä osoittaa, että jokien virtauskuvioiden muutoksia voidaan käyttää luotettavina indikaattoreina ilmastomallien muutoksista. Vallitsevasta yksimielisyydestä poiketen on harvinaista, että tulvat ja kuivuus esiintyvät samanaikaisesti useimmissa joissa, mikä viittaa siihen, että hydrologiset ääri-ilmiöt eivät muutu yhdenmukaisesti. Tulokset vahvistavat ilmastonmuutoksen ja ilmaston lämpenemisen merkittävän vaikutuksen arktisen alueen hydrologiaan, erityisesti vuotuisen maksimivirtaaman havaitun laskusuuntauksen osalta

RiTiCE:River flow Timing Characteristics and Extremes in the Arctic region

Abstract (1) Background: river ice has a significant impact on nearly 66% of rivers in the Northern Hemisphere. Ice builds up during winter when the flow gradually reduces to its lowest level before the spring melt is initiated. Ice-induced floods can happen quickly, posing a risk to infrastructure, hydropower generation, and public safety, in addition to ecological repercussions from the scouring and erosion of the riverbeds. (2) Methods: we used the annual daily hydrograph to develop a RiTiCE tool that detects the break-up date and develops indices to analyze timing characteristics of extreme flow in the Tana and Tornio Rivers. (3) Results: the study showed that low-flow periods in two rivers had a significant trend with a confidence level of 95%. Additionally, it was observed that the occurrence date of seasonal 90-day low- and high-flow periods occurred earlier in recent years. Conversely, the Tana River showed a negative trend in its annual minimum flow over the century, which is the opposite of what happened with the Tornio River. (4) Conclusions: the method can be used to detect the date when the river ice breaks up in a given year, leading to a better understanding of the river ice phenomenon

A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation

Abstract In regions with lack of hydrological and hydraulic data, a spatial flood modeling and mapping is an opportunity for the urban authorities to predict the spatial distribution and the intensity of the flooding. It helps decision-makers to develop effective flood prevention and management plans. In this study, flood inventory data were prepared based on the historical and field surveys data by Sari municipality and regional water company of Mazandaran, Iran. The collected flood data accompanied with different variables (digital elevation model and slope have been considered as topographic variables, land use/land cover, precipitation, curve number, distance to river, distance to channel and depth to groundwater as environmental variables) were applied to novel hybridized model based on neural network and swarm intelligence-grey wolf algorithm (NN-SGW) to map flood-inundation. Several confusion matrix criteria were used for accuracy evaluation by cutoff-dependent and independent metrics (e.g., efficiency (E), positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic curve (AUC)). The accuracy of the flood inundation map produced by the NN-SGW model was compared with that of maps produced by four state-of-the-art benchmark models: random forest (RF), logistic model tree (LMT), classification and regression trees (CART), and J48 decision tree (J48DT). The NN-SGW model outperformed all benchmark models in both training (E = 90.5%, PPV = 93.7%, NPV = 87.3%, AUC = 96.3%) and validation (E = 79.4%, PPV = 85.3%, NPV = 73.5%, AUC = 88.2%). As the NN-SGW model produced the most accurate flood-inundation map, it can be employed for robust flood contingency planning. Based on the obtained results from NN-SGW model, distance from channel, distance from river, and depth to groundwater were identified as the most important variables for spatial prediction of urban flood inundation. This work can serve as a basis for future studies seeking to predict flood susceptibility in urban areas using hybridized machine learning (ML) models and can also be applied in other urban areas where flood inundation presents a pressing challenge, and there are some problems regarding required model and availability of input data

A century of variations in extreme flow across Finnish rivers

Abstract River flow in cold climates is known to be one of the hydrological systems most affected by climate change, playing a central role in the sustainability of downstream socio-ecological systems. Numerous studies on the temporal and spatial variations of streamflow characteristics have been done, and a comprehensive study on the variation of hydrologic extremes is becoming increasingly important. This study evaluated the long-running changes in the magnitude, time, and inter-annual variability of hydrologic extremes, including high and low flow in 16 major Finnish rivers. We applied four new hydrologic extreme indices for summer–winter low flow ratio, spring-absolute high flow ratio, time-to-peak index, and increasing rate index during the snowmelt period to analyze the spatiotemporal variations of extreme streamflow from 1911 to 2020. The most detected trends in flow regimes have started in the last six decades and become more severe from 1991 to 2020, which is likely to be dominated by anthropogenic global warming. The results also indicated that alteration of low pulses in most rivers was associated with an increase (decrease) in winter (summer) flows, suggesting the annual minimum flow in summer frequently contradicts natural hydrologic regimes in Arctic rivers. Southern Finland has experienced higher variations in extreme hydrology over the last century. A new low flow regime was detected for southern rivers, characterized by frequent annual minimum flow in summer instead of winter. Moreover, the annual maximum flow before/after spring dictated a new high-flow regime characterized by frequent double peak flows in this region

RiMARS:an automated river morphodynamics analysis method based on remote sensing multispectral datasets

Abstract Assessment and monitoring of river morphology own an important role in river engineering; since, changes in river morphology including erosion and sedimentation affect river cross-sections and flow processes. An approach for River Morphodynamics Analysis based on Remote Sensing (RiMARS) was developed and tested on the case of Mollasadra dam construction on the Kor River, Iran. Landsat multispectral images obtained from the open USGS dataset are used to extract river morphology dynamics by the Modified Normalized Difference Water Index (MNDWI). RiMARS comes with a river extraction module which is independent of threshold segmentation methods to produce binary-level images. In addition, RiMARS is equipped with developed indices for assessing the morphological alterations. Five characteristics of river morphology (spatiotemporal Sinuosity Index (SI), Absolute Centerline Migration (ACM), Rate of Centerline Migration (RCM), River Linear Pattern (RLP), and Meander Migration Index (MMI)), are applied to quantify river morphology changes. The results indicated that the Kor River centerline underwent average annual migration of 40 cm to the southwest during 1993–2003 (pre-construction impact), 20 cm to the northeast during 2003–2011, and 40 cm to the south-west during 2011–2017 (post-construction impact). Spatially, as the Kor River runs towards the Doroudzan dam, changes in river morphology have increased from upstream to downstream; particularly evident where the river flows in a plain instead of the valley. Based on SI values, there was a 5% change in the straight sinuosity class in the pre-construction period, but an 18% decrease in the straight class during the post-construction period. Here we demonstrate the application of RiMARS in assessing the impact of dam construction on morphometric processes in Kor River, but it can be used to assess other riverine changes, including tracking the unauthorized water consumption using diverted canals. RiMARS can be applied on multispectral images